2024/07/07 00:22:44 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.10.14 (main, May 6 2024, 19:42:50) [GCC 11.2.0] CUDA available: True numpy_random_seed: 123940593 GPU 0,1,2,3,4,5: NVIDIA GeForce RTX 3090 CUDA_HOME: /data1/tanghao/cuda/cuda-11.8:/data1/tanghao/cuda/cuda-11.8: GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 PyTorch: 2.1.0+cu118 PyTorch compiling details: PyTorch built with: - GCC 9.3 - C++ Version: 201703 - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v3.1.1 (Git Hash 64f6bcbcbab628e96f33a62c3e975f8535a7bde4) - OpenMP 201511 (a.k.a. OpenMP 4.5) - LAPACK is enabled (usually provided by MKL) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 11.8 - NVCC architecture flags: -gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_90,code=sm_90 - CuDNN 8.9.5 - Built with CuDNN 8.7 - Magma 2.6.1 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.8, CUDNN_VERSION=8.7.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-invalid-partial-specialization -Wno-unused-private-field -Wno-aligned-allocation-unavailable -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.1.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, TorchVision: 0.16.0+cu118 OpenCV: 4.10.0 MMEngine: 0.8.3 Runtime environment: cudnn_benchmark: True mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: 123940593 Distributed launcher: pytorch Distributed training: True GPU number: 6 ------------------------------------------------------------ 2024/07/07 00:22:46 - mmengine - INFO - Config: auto_scale_lr = dict(base_batch_size=24) backend_args = None base_img_size = 672 custom_hooks = [ dict( ema_type='ExpMomentumEMA', momentum=0.0002, priority=49, type='EMAHook', update_buffers=True), dict( switch_epoch=100000, switch_pipeline=[ dict( meta_keys=( 'img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'task_name', 'head_cfg', 'git_cfg', ), type='PackDetInputs'), ], type='PipelineSwitchHook'), ] default_hooks = dict( checkpoint=dict( by_epoch=False, interval=1000, max_keep_ckpts=1, type='CheckpointHook'), logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), param_scheduler=dict(type='ParamSchedulerHook'), sampler_seed=dict(type='DistSamplerSeedHook'), timer=dict(type='IterTimerHook'), visualization=dict(type='DetVisualizationHook')) default_scope = 'mmdet' det_cfgs = dict( global_only_image=True, grid_interpolate=True, grid_resolution_perwin=[ 5, 5, ], max_length=30, mode='detection', num_vocal=1426, samples_grids_eachwin=10) det_test_pipeline = [ dict(backend_args=None, type='LoadImageFromFile'), dict(keep_ratio=False, scale=( 672, 672, ), type='Resize'), dict(type='LoadAnnotations', with_bbox=True), dict( meta_dict=dict( git_cfg=dict( global_only_image=True, grid_interpolate=True, grid_resolution_perwin=[ 5, 5, ], max_length=30, mode='detection', num_vocal=1426, samples_grids_eachwin=10), head_cfg=dict( max_length=30, num_bins=1344, num_classes=80, num_vocal=1426), task_name='detection'), type='AddMetaInfo'), dict( meta_keys=( 'img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'task_name', 'head_cfg', 'git_cfg', ), type='PackDetInputs'), ] det_train_pipeline = [ dict(max_num_pasted=100, type='CopyPaste'), dict( meta_keys=( 'img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'task_name', 'head_cfg', 'git_cfg', ), type='PackDetInputs'), ] det_train_pipeline_stage2 = [ dict( meta_keys=( 'img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'task_name', 'head_cfg', 'git_cfg', ), type='PackDetInputs'), ] env_cfg = dict( cudnn_benchmark=True, dist_cfg=dict(backend='nccl'), mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) launcher = 'pytorch' load_from = None load_pipeline = [ dict(backend_args=None, type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True, with_mask=True), dict( meta_dict=dict( git_cfg=dict( global_only_image=True, grid_interpolate=True, grid_resolution_perwin=[ 5, 5, ], max_length=30, mode='detection', num_vocal=1426, samples_grids_eachwin=10), head_cfg=dict( max_length=30, num_bins=1344, num_classes=80, num_vocal=1426), task_name='detection'), type='AddMetaInfo'), dict(prob=0.5, type='RandomFlip'), dict( transforms=[ [ dict( keep_ratio=False, scales=[ ( 672, 672, ), ], type='RandomChoiceResize'), ], [ dict( keep_ratio=True, scales=[ ( 400, 4200, ), ( 500, 4200, ), ( 600, 4200, ), ], type='RandomChoiceResize'), dict( allow_negative_crop=True, crop_size=( 384, 600, ), crop_type='absolute_range', type='RandomCrop'), dict( keep_ratio=False, scales=[ ( 672, 672, ), ], type='RandomChoiceResize'), ], ], type='RandomChoice'), dict(min_gt_bbox_wh=( 1e-05, 1e-05, ), type='FilterAnnotations'), ] log_level = 'INFO' log_processor = dict(by_epoch=False, type='LogProcessor', window_size=4000) max_iters = 120000 model = dict( backbone=dict( arch='base', drop_path_rate=0.1, img_size=672, init_cfg=dict( checkpoint='./sam-base-repeat-10.pth', prefix='backbone.', type='Pretrained'), new_more_layers=[ 'win', 'win', 'win', 'win', 'win', 'win', ], out_channels=0, out_type='featmap', patch_size=16, text_cfg=dict( hidden_size=768, pretrain_path='./blip_embed.pt', type='bert-base'), type='ViTGiTPromptBeta', use_abs_pos=True, use_checkpoints=True, use_rel_pos=True, window_size=14), data_preprocessor=dict( bgr_to_rgb=True, mean=[ 123.675, 116.28, 103.53, ], pad_seg=True, pad_size_divisor=224, seg_pad_value=255, std=[ 58.395, 57.12, 57.375, ], type='GeneralDataPreprocessor'), head_list=dict( detection_head=dict( nms=dict(iou_threshold=0.5, type='soft_nms'), repeat_times=3, test_cfg=dict(max_per_img=100), train_cfg=dict( assigner=dict( match_costs=[ dict( box_format='xywh', type='PointsL1Cost', weight=5.0), ], type='HungarianAssigner')), type='GiTDetHeadPromptBeta')), mean_layes=[ 12, 13, 14, 15, 16, 17, ], mean_output=True, support_tasks=[ 'detection', 'semantic_segmentation', 'instance_segmentation', 'caption', 'grounding', ], tokenizer=dict(name_or_path='bert-base-uncased', type='BlipTokenizer'), type='GiTPromptBeta', use_checkpoints=True) optim_wrapper = dict( clip_grad=dict(max_norm=0.1, norm_type=2), dtype='bfloat16', optimizer=dict(lr=0.0002, type='AdamW', weight_decay=0.05), paramwise_cfg=dict( custom_keys=dict({ 'backbone': dict(lr_mult=0.1), 'backbone.embed': dict(lr_mult=1.0), 'backbone.layers.10': dict(lr_mult=0.7429), 'backbone.layers.11': dict(lr_mult=0.8714), 'backbone.layers.12': dict(lr_mult=1.0), 'backbone.layers.13': dict(lr_mult=1.0), 'backbone.layers.14': dict(lr_mult=1.0), 'backbone.layers.15': dict(lr_mult=1.0), 'backbone.layers.16': dict(lr_mult=1.0), 'backbone.layers.17': dict(lr_mult=1.0), 'backbone.layers.6': dict(lr_mult=0.2286), 'backbone.layers.7': dict(lr_mult=0.3571), 'backbone.layers.8': dict(lr_mult=0.4858), 'backbone.layers.9': dict(lr_mult=0.6143), 'reference_points': dict(lr_mult=0.1), 'sampling_offsets': dict(lr_mult=0.1) })), type='AmpOptimWrapper') param_scheduler = [ dict( T_max=120000, begin=0, by_epoch=False, end=120000, eta_min=2e-06, type='CosineAnnealingLR'), ] pretrained = './sam-base-repeat-10.pth' resume = True test_cfg = dict(type='TestLoop') test_dataloader = dict( batch_size=8, dataset=dict( ann_file='annotations/instances_val2017.json', backend_args=None, data_prefix=dict(img='val2017/'), data_root='data/coco/', pipeline=[ dict(backend_args=None, type='LoadImageFromFile'), dict(keep_ratio=False, scale=( 672, 672, ), type='Resize'), dict(type='LoadAnnotations', with_bbox=True), dict( meta_dict=dict( git_cfg=dict( global_only_image=True, grid_interpolate=True, grid_resolution_perwin=[ 5, 5, ], max_length=30, mode='detection', num_vocal=1426, samples_grids_eachwin=10), head_cfg=dict( max_length=30, num_bins=1344, num_classes=80, num_vocal=1426), task_name='detection'), type='AddMetaInfo'), dict( meta_keys=( 'img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'task_name', 'head_cfg', 'git_cfg', ), type='PackDetInputs'), ], return_classes=True, test_mode=True, type='CocoDataset'), drop_last=False, num_workers=3, persistent_workers=True, sampler=dict(shuffle=False, type='DefaultSampler')) test_evaluator = dict( ann_file='data/coco/annotations/instances_val2017.json', backend_args=None, format_only=False, metric='bbox', type='CocoMetric') test_pipeline = [ dict(backend_args=None, type='LoadImageFromFile'), dict(keep_ratio=False, scale=( 672, 672, ), type='Resize'), dict(type='LoadAnnotations', with_bbox=True), dict( meta_dict=dict( git_cfg=dict( global_only_image=True, grid_interpolate=True, grid_resolution_perwin=[ 5, 5, ], max_length=30, mode='detection', num_vocal=1426, samples_grids_eachwin=10), head_cfg=dict( max_length=30, num_bins=1344, num_classes=80, num_vocal=1426), task_name='detection'), type='AddMetaInfo'), dict( meta_keys=( 'img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'task_name', 'head_cfg', 'git_cfg', ), type='PackDetInputs'), ] train_cfg = dict( max_iters=120000, type='IterBasedTrainLoop', val_interval=5000) train_dataloader = dict( batch_sampler=None, batch_size=4, dataset=dict( datasets=[ dict( dataset=dict( ann_file='annotations/instances_train2017.json', backend_args=None, data_prefix=dict(img='train2017/'), data_root='data/coco/', filter_cfg=dict(filter_empty_gt=True, min_size=32), pipeline=[ dict(backend_args=None, type='LoadImageFromFile'), dict( type='LoadAnnotations', with_bbox=True, with_mask=True), dict( meta_dict=dict( git_cfg=dict( global_only_image=True, grid_interpolate=True, grid_resolution_perwin=[ 5, 5, ], max_length=30, mode='detection', num_vocal=1426, samples_grids_eachwin=10), head_cfg=dict( max_length=30, num_bins=1344, num_classes=80, num_vocal=1426), task_name='detection'), type='AddMetaInfo'), dict(prob=0.5, type='RandomFlip'), dict( transforms=[ [ dict( keep_ratio=False, scales=[ ( 672, 672, ), ], type='RandomChoiceResize'), ], [ dict( keep_ratio=True, scales=[ ( 400, 4200, ), ( 500, 4200, ), ( 600, 4200, ), ], type='RandomChoiceResize'), dict( allow_negative_crop=True, crop_size=( 384, 600, ), crop_type='absolute_range', type='RandomCrop'), dict( keep_ratio=False, scales=[ ( 672, 672, ), ], type='RandomChoiceResize'), ], ], type='RandomChoice'), dict( min_gt_bbox_wh=( 1e-05, 1e-05, ), type='FilterAnnotations'), ], return_classes=True, type='CocoDataset'), pipeline=[ dict(max_num_pasted=100, type='CopyPaste'), dict( meta_keys=( 'img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'task_name', 'head_cfg', 'git_cfg', ), type='PackDetInputs'), ], type='MultiImageMixDataset'), ], ignore_keys=[ 'reduce_zero_label', 'label_map', 'classes', 'palette', ], type='ConcatDataset'), num_workers=4, persistent_workers=True, sampler=dict( batch_size=4, if_group=[ True, ], shuffle=True, source_ratio=[ 1.0, ], type='GroupMultiSourceNonMixedSampler')) tta_model = dict(type='SegTTAModel') val_cfg = dict(type='ValLoop') val_dataloader = dict( batch_size=8, dataset=dict( ann_file='annotations/instances_val2017.json', backend_args=None, data_prefix=dict(img='val2017/'), data_root='data/coco/', pipeline=[ dict(backend_args=None, type='LoadImageFromFile'), dict(keep_ratio=False, scale=( 672, 672, ), type='Resize'), dict(type='LoadAnnotations', with_bbox=True), dict( meta_dict=dict( git_cfg=dict( global_only_image=True, grid_interpolate=True, grid_resolution_perwin=[ 5, 5, ], max_length=30, mode='detection', num_vocal=1426, samples_grids_eachwin=10), head_cfg=dict( max_length=30, num_bins=1344, num_classes=80, num_vocal=1426), task_name='detection'), type='AddMetaInfo'), dict( meta_keys=( 'img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'task_name', 'head_cfg', 'git_cfg', ), type='PackDetInputs'), ], return_classes=True, test_mode=True, type='CocoDataset'), drop_last=False, num_workers=3, persistent_workers=True, sampler=dict(shuffle=False, type='DefaultSampler')) val_evaluator = dict( ann_file='data/coco/annotations/instances_val2017.json', backend_args=None, format_only=False, metric='bbox', type='CocoMetric') vis_backends = [ dict(type='LocalVisBackend'), ] visualizer = dict( name='visualizer', type='DetLocalVisualizer', vis_backends=[ dict(type='LocalVisBackend'), ]) work_dir = './work_dirs/single_detection_base_672_prompt_beta' 2024/07/07 00:23:50 - mmengine - INFO - Hooks will be executed in the following order: before_run: (VERY_HIGH ) RuntimeInfoHook (49 ) EMAHook (BELOW_NORMAL) LoggerHook -------------------- after_load_checkpoint: (49 ) EMAHook -------------------- before_train: (VERY_HIGH ) RuntimeInfoHook (49 ) EMAHook (NORMAL ) IterTimerHook (VERY_LOW ) CheckpointHook -------------------- before_train_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (NORMAL ) DistSamplerSeedHook (NORMAL ) PipelineSwitchHook -------------------- before_train_iter: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook -------------------- after_train_iter: (VERY_HIGH ) RuntimeInfoHook (49 ) EMAHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- after_train_epoch: (NORMAL ) IterTimerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- before_val: (VERY_HIGH ) RuntimeInfoHook -------------------- before_val_epoch: (49 ) EMAHook (NORMAL ) IterTimerHook -------------------- before_val_iter: (NORMAL ) IterTimerHook -------------------- after_val_iter: (NORMAL ) IterTimerHook (NORMAL ) DetVisualizationHook (BELOW_NORMAL) LoggerHook -------------------- after_val_epoch: (VERY_HIGH ) RuntimeInfoHook (49 ) EMAHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- after_val: (VERY_HIGH ) RuntimeInfoHook -------------------- before_save_checkpoint: (49 ) EMAHook -------------------- after_train: (VERY_HIGH ) RuntimeInfoHook (VERY_LOW ) CheckpointHook -------------------- before_test: (VERY_HIGH ) RuntimeInfoHook -------------------- before_test_epoch: (49 ) EMAHook (NORMAL ) IterTimerHook -------------------- before_test_iter: (NORMAL ) IterTimerHook -------------------- after_test_iter: (NORMAL ) IterTimerHook (NORMAL ) DetVisualizationHook (BELOW_NORMAL) LoggerHook -------------------- after_test_epoch: (VERY_HIGH ) RuntimeInfoHook (49 ) EMAHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook -------------------- after_test: (VERY_HIGH ) RuntimeInfoHook -------------------- after_run: (BELOW_NORMAL) LoggerHook -------------------- 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.pos_embed:lr=2e-05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.pos_embed:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.pos_embed:lr_mult=0.1 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.patch_embed.projection.weight:lr=2e-05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.patch_embed.projection.weight:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.patch_embed.projection.weight:lr_mult=0.1 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.patch_embed.projection.bias:lr=2e-05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.patch_embed.projection.bias:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.patch_embed.projection.bias:lr_mult=0.1 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.embed.word_embeddings.weight:lr=0.0002 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.embed.word_embeddings.weight:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.embed.word_embeddings.weight:lr_mult=1.0 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.embed.position_embeddings.weight:lr=0.0002 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.embed.position_embeddings.weight:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.embed.position_embeddings.weight:lr_mult=1.0 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.embed.LayerNorm.weight:lr=0.0002 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.embed.LayerNorm.weight:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.embed.LayerNorm.weight:lr_mult=1.0 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.embed.LayerNorm.bias:lr=0.0002 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.embed.LayerNorm.bias:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.embed.LayerNorm.bias:lr_mult=1.0 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.0.ln1.weight:lr=2e-05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.0.ln1.weight:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.0.ln1.weight:lr_mult=0.1 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.0.ln1.bias:lr=2e-05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.0.ln1.bias:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.0.ln1.bias:lr_mult=0.1 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.0.attn.rel_pos_h:lr=2e-05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.0.attn.rel_pos_h:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.0.attn.rel_pos_h:lr_mult=0.1 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.0.attn.rel_pos_w:lr=2e-05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.0.attn.rel_pos_w:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.0.attn.rel_pos_w:lr_mult=0.1 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.0.attn.qkv.weight:lr=2e-05 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mmengine - INFO - paramwise_options -- backbone.layers.10.attn.rel_pos_h:lr_mult=0.7429 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.10.attn.rel_pos_w:lr=0.00014858000000000002 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.10.attn.rel_pos_w:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.10.attn.rel_pos_w:lr_mult=0.7429 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.10.attn.qkv.weight:lr=0.00014858000000000002 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.10.attn.qkv.weight:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.10.attn.qkv.weight:lr_mult=0.7429 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.10.attn.qkv.bias:lr=0.00014858000000000002 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- 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mmengine - INFO - paramwise_options -- backbone.layers.10.ln2.weight:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.10.ln2.weight:lr_mult=0.7429 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.10.ln2.bias:lr=0.00014858000000000002 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.10.ln2.bias:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.10.ln2.bias:lr_mult=0.7429 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.10.ffn.layers.0.0.weight:lr=0.00014858000000000002 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.10.ffn.layers.0.0.weight:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.10.ffn.layers.0.0.weight:lr_mult=0.7429 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- 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backbone.layers.16.attn.rel_pos_h:lr=0.0002 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.16.attn.rel_pos_h:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.16.attn.rel_pos_h:lr_mult=1.0 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.16.attn.rel_pos_w:lr=0.0002 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.16.attn.rel_pos_w:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.16.attn.rel_pos_w:lr_mult=1.0 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.16.attn.qkv.weight:lr=0.0002 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.16.attn.qkv.weight:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.16.attn.qkv.weight:lr_mult=1.0 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.16.attn.qkv.bias:lr=0.0002 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.16.attn.qkv.bias:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.16.attn.qkv.bias:lr_mult=1.0 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.16.attn.proj.weight:lr=0.0002 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.16.attn.proj.weight:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.16.attn.proj.weight:lr_mult=1.0 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.16.attn.proj.bias:lr=0.0002 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.16.attn.proj.bias:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.16.attn.proj.bias:lr_mult=1.0 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.16.ln2.weight:lr=0.0002 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.16.ln2.weight:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.16.ln2.weight:lr_mult=1.0 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.16.ln2.bias:lr=0.0002 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.16.ln2.bias:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.16.ln2.bias:lr_mult=1.0 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.16.ffn.layers.0.0.weight:lr=0.0002 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.16.ffn.layers.0.0.weight:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.16.ffn.layers.0.0.weight:lr_mult=1.0 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.16.ffn.layers.0.0.bias:lr=0.0002 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.16.ffn.layers.0.0.bias:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.16.ffn.layers.0.0.bias:lr_mult=1.0 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.16.ffn.layers.1.weight:lr=0.0002 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.16.ffn.layers.1.weight:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.16.ffn.layers.1.weight:lr_mult=1.0 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.16.ffn.layers.1.bias:lr=0.0002 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.16.ffn.layers.1.bias:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.16.ffn.layers.1.bias:lr_mult=1.0 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.17.ln1.weight:lr=0.0002 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.17.ln1.weight:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.17.ln1.weight:lr_mult=1.0 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.17.ln1.bias:lr=0.0002 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.17.ln1.bias:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.17.ln1.bias:lr_mult=1.0 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.17.attn.qkv.weight:lr=0.0002 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.17.attn.qkv.weight:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.17.attn.qkv.weight:lr_mult=1.0 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.17.attn.qkv.bias:lr=0.0002 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.17.attn.qkv.bias:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.17.attn.qkv.bias:lr_mult=1.0 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.17.attn.proj.weight:lr=0.0002 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.17.attn.proj.weight:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.17.attn.proj.weight:lr_mult=1.0 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.17.attn.proj.bias:lr=0.0002 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.17.attn.proj.bias:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.17.attn.proj.bias:lr_mult=1.0 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.17.ln2.weight:lr=0.0002 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.17.ln2.weight:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.17.ln2.weight:lr_mult=1.0 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.17.ln2.bias:lr=0.0002 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.17.ln2.bias:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.17.ln2.bias:lr_mult=1.0 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.17.ffn.layers.0.0.weight:lr=0.0002 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.17.ffn.layers.0.0.weight:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.17.ffn.layers.0.0.weight:lr_mult=1.0 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.17.ffn.layers.0.0.bias:lr=0.0002 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.17.ffn.layers.0.0.bias:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.17.ffn.layers.0.0.bias:lr_mult=1.0 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.17.ffn.layers.1.weight:lr=0.0002 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.17.ffn.layers.1.weight:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.17.ffn.layers.1.weight:lr_mult=1.0 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.17.ffn.layers.1.bias:lr=0.0002 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.17.ffn.layers.1.bias:weight_decay=0.05 2024/07/07 00:24:55 - mmengine - INFO - paramwise_options -- backbone.layers.17.ffn.layers.1.bias:lr_mult=1.0 2024/07/07 00:25:00 - mmengine - INFO - load backbone. in model from: ./sam-base-repeat-10.pth 2024/07/07 00:25:00 - mmengine - INFO - Resize the pos_embed shape from torch.Size([1, 64, 64, 768]) to torch.Size([1, 42, 42, 768]). 2024/07/07 00:25:01 - mmengine - INFO - Resize the layers.2.attn.rel_pos_h from torch.Size([127, 64]) to torch.Size([83, 64]) 2024/07/07 00:25:01 - mmengine - INFO - Resize the layers.2.attn.rel_pos_w from torch.Size([127, 64]) to torch.Size([83, 64]) 2024/07/07 00:25:01 - mmengine - INFO - Resize the layers.5.attn.rel_pos_h from torch.Size([127, 64]) to torch.Size([83, 64]) 2024/07/07 00:25:01 - mmengine - INFO - Resize the layers.5.attn.rel_pos_w from torch.Size([127, 64]) to torch.Size([83, 64]) 2024/07/07 00:25:01 - mmengine - INFO - Resize the layers.8.attn.rel_pos_h from torch.Size([127, 64]) to torch.Size([83, 64]) 2024/07/07 00:25:01 - mmengine - INFO - Resize the layers.8.attn.rel_pos_w from torch.Size([127, 64]) to torch.Size([83, 64]) 2024/07/07 00:25:01 - mmengine - INFO - Resize the layers.11.attn.rel_pos_h from torch.Size([127, 64]) to torch.Size([83, 64]) 2024/07/07 00:25:01 - mmengine - INFO - Resize the layers.11.attn.rel_pos_w from torch.Size([127, 64]) to torch.Size([83, 64]) 2024/07/07 00:25:01 - mmengine - WARNING - The model and loaded state dict do not match exactly unexpected key in source state_dict: channel_reduction.0.weight, channel_reduction.1.weight, channel_reduction.1.bias, channel_reduction.2.weight, channel_reduction.3.weight, channel_reduction.3.bias missing keys in source state_dict: embed.position_ids, embed.word_embeddings.weight, embed.position_embeddings.weight, embed.LayerNorm.weight, embed.LayerNorm.bias Name of parameter - Initialization information backbone.pos_embed - torch.Size([1, 42, 42, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.patch_embed.projection.weight - torch.Size([768, 3, 16, 16]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.patch_embed.projection.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.embed.word_embeddings.weight - torch.Size([30524, 768]): The value is the same before and after calling `init_weights` of GiTPromptBeta backbone.embed.position_embeddings.weight - torch.Size([512, 768]): The value is the same before and after calling `init_weights` of GiTPromptBeta backbone.embed.LayerNorm.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of GiTPromptBeta backbone.embed.LayerNorm.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of GiTPromptBeta backbone.layers.0.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.attn.rel_pos_h - torch.Size([83, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.attn.rel_pos_w - torch.Size([83, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.attn.rel_pos_h - torch.Size([83, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.attn.rel_pos_w - torch.Size([83, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.attn.rel_pos_h - torch.Size([83, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.attn.rel_pos_w - torch.Size([83, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.attn.rel_pos_h - torch.Size([83, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.attn.rel_pos_w - torch.Size([83, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth 2024/07/07 00:25:01 - mmengine - INFO - Auto resumed from the latest checkpoint /home/tanghao/mpi/GiT-main/work_dirs/single_detection_base_672_prompt_beta/iter_16000.pth. 2024/07/07 00:25:05 - mmengine - INFO - Load checkpoint from /home/tanghao/mpi/GiT-main/work_dirs/single_detection_base_672_prompt_beta/iter_16000.pth 2024/07/07 00:25:05 - mmengine - INFO - resumed epoch: 0, iter: 16000 2024/07/07 00:25:05 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io 2024/07/07 00:25:05 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. 2024/07/07 00:25:05 - mmengine - INFO - Checkpoints will be saved to /home/tanghao/mpi/GiT-main/work_dirs/single_detection_base_672_prompt_beta. 2024/07/07 00:26:05 - mmengine - INFO - Iter(train) [ 16050/120000] base_lr: 1.9139e-04 lr: 1.9217e-05 eta: 1 day, 11:00:12 time: 1.2214 data_time: 0.0663 memory: 6195 grad_norm: 1.0506 loss: 0.9036 detection_loss_cls: 0.9036 2024/07/07 00:27:05 - mmengine - INFO - Iter(train) [ 16100/120000] base_lr: 1.9134e-04 lr: 1.9212e-05 eta: 1 day, 10:38:25 time: 1.2213 data_time: 0.0663 memory: 6195 grad_norm: 1.0503 loss: 0.9038 detection_loss_cls: 0.9038 2024/07/07 00:28:04 - mmengine - INFO - Iter(train) [ 16150/120000] base_lr: 1.9128e-04 lr: 1.9208e-05 eta: 1 day, 10:29:04 time: 1.2211 data_time: 0.0663 memory: 6195 grad_norm: 1.0499 loss: 0.9040 detection_loss_cls: 0.9040 2024/07/07 00:29:03 - mmengine - INFO - Iter(train) [ 16200/120000] base_lr: 1.9123e-04 lr: 1.9203e-05 eta: 1 day, 10:22:01 time: 1.2208 data_time: 0.0663 memory: 6195 grad_norm: 1.0485 loss: 0.9045 detection_loss_cls: 0.9045 2024/07/07 00:30:02 - mmengine - INFO - Iter(train) [ 16250/120000] base_lr: 1.9118e-04 lr: 1.9198e-05 eta: 1 day, 10:13:54 time: 1.2205 data_time: 0.0663 memory: 6195 grad_norm: 1.0485 loss: 0.9040 detection_loss_cls: 0.9040 2024/07/07 00:31:00 - mmengine - INFO - Iter(train) [ 16300/120000] base_lr: 1.9112e-04 lr: 1.9193e-05 eta: 1 day, 10:05:00 time: 1.2199 data_time: 0.0662 memory: 6195 grad_norm: 1.0486 loss: 0.9043 detection_loss_cls: 0.9043 2024/07/07 00:31:58 - mmengine - INFO - Iter(train) [ 16350/120000] base_lr: 1.9107e-04 lr: 1.9188e-05 eta: 1 day, 10:01:16 time: 1.2195 data_time: 0.0662 memory: 6195 grad_norm: 1.0484 loss: 0.9038 detection_loss_cls: 0.9038 2024/07/07 00:32:57 - mmengine - INFO - Iter(train) [ 16400/120000] base_lr: 1.9102e-04 lr: 1.9183e-05 eta: 1 day, 9:56:58 time: 1.2190 data_time: 0.0662 memory: 6195 grad_norm: 1.0480 loss: 0.9029 detection_loss_cls: 0.9029 2024/07/07 00:33:55 - mmengine - INFO - Iter(train) [ 16450/120000] base_lr: 1.9096e-04 lr: 1.9178e-05 eta: 1 day, 9:52:08 time: 1.2183 data_time: 0.0662 memory: 6195 grad_norm: 1.0476 loss: 0.9039 detection_loss_cls: 0.9039 2024/07/07 00:34:53 - mmengine - INFO - Iter(train) [ 16500/120000] base_lr: 1.9091e-04 lr: 1.9173e-05 eta: 1 day, 9:49:43 time: 1.2178 data_time: 0.0661 memory: 6195 grad_norm: 1.0480 loss: 0.9035 detection_loss_cls: 0.9035 2024/07/07 00:35:51 - mmengine - INFO - Iter(train) [ 16550/120000] base_lr: 1.9085e-04 lr: 1.9168e-05 eta: 1 day, 9:46:06 time: 1.2173 data_time: 0.0661 memory: 6195 grad_norm: 1.0477 loss: 0.9024 detection_loss_cls: 0.9024 2024/07/07 00:36:49 - mmengine - INFO - Iter(train) [ 16600/120000] base_lr: 1.9080e-04 lr: 1.9163e-05 eta: 1 day, 9:44:01 time: 1.2168 data_time: 0.0660 memory: 6195 grad_norm: 1.0478 loss: 0.9019 detection_loss_cls: 0.9019 2024/07/07 00:37:48 - mmengine - INFO - Iter(train) [ 16650/120000] base_lr: 1.9074e-04 lr: 1.9159e-05 eta: 1 day, 9:42:33 time: 1.2161 data_time: 0.0660 memory: 6195 grad_norm: 1.0483 loss: 0.9026 detection_loss_cls: 0.9026 2024/07/07 00:38:46 - 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mmengine - INFO - Iter(train) [ 17900/120000] base_lr: 1.8933e-04 lr: 1.9030e-05 eta: 1 day, 9:27:43 time: 1.2068 data_time: 0.0650 memory: 6195 grad_norm: 1.0438 loss: 0.9000 detection_loss_cls: 0.9000 2024/07/07 01:03:25 - mmengine - INFO - Iter(train) [ 17950/120000] base_lr: 1.8927e-04 lr: 1.9025e-05 eta: 1 day, 9:26:35 time: 1.2062 data_time: 0.0650 memory: 6195 grad_norm: 1.0432 loss: 0.8995 detection_loss_cls: 0.8995 2024/07/07 01:04:25 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 01:04:25 - mmengine - INFO - Iter(train) [ 18000/120000] base_lr: 1.8921e-04 lr: 1.9019e-05 eta: 1 day, 9:26:12 time: 1.2061 data_time: 0.0649 memory: 6195 grad_norm: 1.0444 loss: 0.9001 detection_loss_cls: 0.9001 2024/07/07 01:04:25 - mmengine - INFO - Saving checkpoint at 18000 iterations 2024/07/07 01:05:32 - mmengine - INFO - Iter(train) [ 18050/120000] base_lr: 1.8915e-04 lr: 1.9014e-05 eta: 1 day, 9:32:02 time: 1.2059 data_time: 0.0648 memory: 6195 grad_norm: 1.0443 loss: 0.8982 detection_loss_cls: 0.8982 2024/07/07 01:06:31 - 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mmengine - INFO - Iter(train) [ 18850/120000] base_lr: 1.8819e-04 lr: 1.8926e-05 eta: 1 day, 9:14:14 time: 1.1997 data_time: 0.0639 memory: 6195 grad_norm: 1.0432 loss: 0.8908 detection_loss_cls: 0.8908 2024/07/07 01:22:15 - mmengine - INFO - Iter(train) [ 18900/120000] base_lr: 1.8813e-04 lr: 1.8921e-05 eta: 1 day, 9:13:01 time: 1.1991 data_time: 0.0638 memory: 6195 grad_norm: 1.0430 loss: 0.8906 detection_loss_cls: 0.8906 2024/07/07 01:23:15 - mmengine - INFO - Iter(train) [ 18950/120000] base_lr: 1.8807e-04 lr: 1.8915e-05 eta: 1 day, 9:12:13 time: 1.1990 data_time: 0.0638 memory: 6195 grad_norm: 1.0427 loss: 0.8903 detection_loss_cls: 0.8903 2024/07/07 01:24:14 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 01:24:14 - mmengine - INFO - Iter(train) [ 19000/120000] base_lr: 1.8800e-04 lr: 1.8909e-05 eta: 1 day, 9:11:13 time: 1.1987 data_time: 0.0637 memory: 6195 grad_norm: 1.0435 loss: 0.8897 detection_loss_cls: 0.8897 2024/07/07 01:24:14 - mmengine - INFO - Saving checkpoint at 19000 iterations 2024/07/07 01:25:21 - mmengine - INFO - Iter(train) [ 19050/120000] base_lr: 1.8794e-04 lr: 1.8904e-05 eta: 1 day, 9:14:29 time: 1.1927 data_time: 0.0581 memory: 6195 grad_norm: 1.0429 loss: 0.8904 detection_loss_cls: 0.8904 2024/07/07 01:26:20 - mmengine - INFO - Iter(train) [ 19100/120000] base_lr: 1.8788e-04 lr: 1.8898e-05 eta: 1 day, 9:13:37 time: 1.1925 data_time: 0.0580 memory: 6195 grad_norm: 1.0433 loss: 0.8893 detection_loss_cls: 0.8893 2024/07/07 01:27:19 - mmengine - INFO - Iter(train) [ 19150/120000] base_lr: 1.8782e-04 lr: 1.8893e-05 eta: 1 day, 9:12:12 time: 1.1918 data_time: 0.0579 memory: 6195 grad_norm: 1.0434 loss: 0.8891 detection_loss_cls: 0.8891 2024/07/07 01:28:18 - mmengine - INFO - Iter(train) [ 19200/120000] base_lr: 1.8776e-04 lr: 1.8887e-05 eta: 1 day, 9:11:09 time: 1.1911 data_time: 0.0579 memory: 6195 grad_norm: 1.0434 loss: 0.8892 detection_loss_cls: 0.8892 2024/07/07 01:29:17 - mmengine - INFO - Iter(train) [ 19250/120000] base_lr: 1.8769e-04 lr: 1.8881e-05 eta: 1 day, 9:10:11 time: 1.1907 data_time: 0.0579 memory: 6195 grad_norm: 1.0433 loss: 0.8897 detection_loss_cls: 0.8897 2024/07/07 01:30:16 - 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mmengine - INFO - Iter(train) [ 19800/120000] base_lr: 1.8700e-04 lr: 1.8818e-05 eta: 1 day, 8:58:37 time: 1.1862 data_time: 0.0572 memory: 6195 grad_norm: 1.0411 loss: 0.8846 detection_loss_cls: 0.8846 2024/07/07 01:41:06 - mmengine - INFO - Iter(train) [ 19850/120000] base_lr: 1.8693e-04 lr: 1.8812e-05 eta: 1 day, 8:57:22 time: 1.1857 data_time: 0.0571 memory: 6195 grad_norm: 1.0406 loss: 0.8827 detection_loss_cls: 0.8827 2024/07/07 01:42:05 - mmengine - INFO - Iter(train) [ 19900/120000] base_lr: 1.8687e-04 lr: 1.8806e-05 eta: 1 day, 8:56:18 time: 1.1852 data_time: 0.0571 memory: 6195 grad_norm: 1.0403 loss: 0.8843 detection_loss_cls: 0.8843 2024/07/07 01:43:05 - mmengine - INFO - Iter(train) [ 19950/120000] base_lr: 1.8680e-04 lr: 1.8800e-05 eta: 1 day, 8:55:34 time: 1.1850 data_time: 0.0570 memory: 6195 grad_norm: 1.0394 loss: 0.8833 detection_loss_cls: 0.8833 2024/07/07 01:44:04 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 01:44:04 - mmengine - INFO - Iter(train) [ 20000/120000] base_lr: 1.8674e-04 lr: 1.8794e-05 eta: 1 day, 8:54:21 time: 1.1846 data_time: 0.0570 memory: 6195 grad_norm: 1.0393 loss: 0.8838 detection_loss_cls: 0.8838 2024/07/07 01:44:04 - mmengine - INFO - Saving checkpoint at 20000 iterations 2024/07/07 01:44:52 - mmengine - INFO - Iter(val) [ 50/105] eta: 0:00:44 time: 0.8044 data_time: 0.0308 memory: 6809 2024/07/07 01:45:33 - mmengine - INFO - Iter(val) [100/105] eta: 0:00:04 time: 0.8044 data_time: 0.0305 memory: 6811 2024/07/07 01:45:39 - mmengine - INFO - Evaluating bbox... 2024/07/07 01:46:06 - mmengine - INFO - bbox_mAP_copypaste: 0.351 0.510 0.377 0.151 0.391 0.508 2024/07/07 01:46:07 - mmengine - INFO - Iter(val) [105/105] coco/bbox_mAP: 0.3510 coco/bbox_mAP_50: 0.5100 coco/bbox_mAP_75: 0.3770 coco/bbox_mAP_s: 0.1510 coco/bbox_mAP_m: 0.3910 coco/bbox_mAP_l: 0.5080 data_time: 0.0309 time: 0.8010 2024/07/07 01:47:06 - mmengine - INFO - Iter(train) [ 20050/120000] base_lr: 1.8667e-04 lr: 1.8788e-05 eta: 1 day, 9:06:00 time: 1.1919 data_time: 0.0645 memory: 6805 grad_norm: 1.0390 loss: 0.8834 detection_loss_cls: 0.8834 2024/07/07 01:48:06 - mmengine - INFO - Iter(train) [ 20100/120000] base_lr: 1.8661e-04 lr: 1.8783e-05 eta: 1 day, 9:05:06 time: 1.1921 data_time: 0.0645 memory: 6243 grad_norm: 1.0391 loss: 0.8826 detection_loss_cls: 0.8826 2024/07/07 01:49:05 - mmengine - INFO - Iter(train) [ 20150/120000] base_lr: 1.8654e-04 lr: 1.8777e-05 eta: 1 day, 9:04:03 time: 1.1921 data_time: 0.0645 memory: 6243 grad_norm: 1.0395 loss: 0.8818 detection_loss_cls: 0.8818 2024/07/07 01:50:05 - mmengine - INFO - Iter(train) [ 20200/120000] base_lr: 1.8648e-04 lr: 1.8771e-05 eta: 1 day, 9:02:53 time: 1.1921 data_time: 0.0645 memory: 6243 grad_norm: 1.0396 loss: 0.8818 detection_loss_cls: 0.8818 2024/07/07 01:51:05 - mmengine - INFO - Iter(train) [ 20250/120000] base_lr: 1.8641e-04 lr: 1.8765e-05 eta: 1 day, 9:02:04 time: 1.1925 data_time: 0.0645 memory: 6243 grad_norm: 1.0395 loss: 0.8828 detection_loss_cls: 0.8828 2024/07/07 01:52:04 - mmengine - INFO - Iter(train) [ 20300/120000] base_lr: 1.8635e-04 lr: 1.8759e-05 eta: 1 day, 9:00:55 time: 1.1928 data_time: 0.0645 memory: 6243 grad_norm: 1.0392 loss: 0.8834 detection_loss_cls: 0.8834 2024/07/07 01:53:03 - mmengine - INFO - Iter(train) [ 20350/120000] base_lr: 1.8628e-04 lr: 1.8753e-05 eta: 1 day, 8:59:48 time: 1.1930 data_time: 0.0645 memory: 6243 grad_norm: 1.0391 loss: 0.8834 detection_loss_cls: 0.8834 2024/07/07 01:54:03 - mmengine - INFO - Iter(train) [ 20400/120000] base_lr: 1.8621e-04 lr: 1.8747e-05 eta: 1 day, 8:58:47 time: 1.1933 data_time: 0.0645 memory: 6243 grad_norm: 1.0403 loss: 0.8815 detection_loss_cls: 0.8815 2024/07/07 01:55:02 - mmengine - INFO - Iter(train) [ 20450/120000] base_lr: 1.8615e-04 lr: 1.8741e-05 eta: 1 day, 8:57:49 time: 1.1937 data_time: 0.0644 memory: 6243 grad_norm: 1.0400 loss: 0.8802 detection_loss_cls: 0.8802 2024/07/07 01:56:02 - mmengine - INFO - Iter(train) [ 20500/120000] base_lr: 1.8608e-04 lr: 1.8735e-05 eta: 1 day, 8:56:50 time: 1.1940 data_time: 0.0644 memory: 6243 grad_norm: 1.0394 loss: 0.8794 detection_loss_cls: 0.8794 2024/07/07 01:57:01 - mmengine - INFO - Iter(train) [ 20550/120000] base_lr: 1.8602e-04 lr: 1.8729e-05 eta: 1 day, 8:55:45 time: 1.1943 data_time: 0.0644 memory: 6243 grad_norm: 1.0402 loss: 0.8792 detection_loss_cls: 0.8792 2024/07/07 01:58:01 - mmengine - INFO - Iter(train) [ 20600/120000] base_lr: 1.8595e-04 lr: 1.8723e-05 eta: 1 day, 8:54:38 time: 1.1946 data_time: 0.0644 memory: 6243 grad_norm: 1.0398 loss: 0.8796 detection_loss_cls: 0.8796 2024/07/07 01:59:00 - mmengine - INFO - Iter(train) [ 20650/120000] base_lr: 1.8588e-04 lr: 1.8717e-05 eta: 1 day, 8:53:25 time: 1.1947 data_time: 0.0644 memory: 6243 grad_norm: 1.0393 loss: 0.8789 detection_loss_cls: 0.8789 2024/07/07 01:59:59 - mmengine - INFO - Iter(train) [ 20700/120000] base_lr: 1.8582e-04 lr: 1.8711e-05 eta: 1 day, 8:52:20 time: 1.1950 data_time: 0.0644 memory: 6243 grad_norm: 1.0386 loss: 0.8783 detection_loss_cls: 0.8783 2024/07/07 02:00:59 - mmengine - INFO - Iter(train) [ 20750/120000] base_lr: 1.8575e-04 lr: 1.8704e-05 eta: 1 day, 8:51:21 time: 1.1953 data_time: 0.0644 memory: 6243 grad_norm: 1.0386 loss: 0.8790 detection_loss_cls: 0.8790 2024/07/07 02:01:58 - mmengine - INFO - Iter(train) [ 20800/120000] base_lr: 1.8568e-04 lr: 1.8698e-05 eta: 1 day, 8:50:16 time: 1.1955 data_time: 0.0644 memory: 6243 grad_norm: 1.0381 loss: 0.8779 detection_loss_cls: 0.8779 2024/07/07 02:02:57 - mmengine - INFO - Iter(train) [ 20850/120000] base_lr: 1.8562e-04 lr: 1.8692e-05 eta: 1 day, 8:49:13 time: 1.1958 data_time: 0.0644 memory: 6243 grad_norm: 1.0389 loss: 0.8771 detection_loss_cls: 0.8771 2024/07/07 02:03:57 - mmengine - INFO - Iter(train) [ 20900/120000] base_lr: 1.8555e-04 lr: 1.8686e-05 eta: 1 day, 8:48:08 time: 1.1961 data_time: 0.0644 memory: 6243 grad_norm: 1.0387 loss: 0.8776 detection_loss_cls: 0.8776 2024/07/07 02:04:56 - mmengine - INFO - Iter(train) [ 20950/120000] base_lr: 1.8548e-04 lr: 1.8680e-05 eta: 1 day, 8:46:59 time: 1.1963 data_time: 0.0645 memory: 6243 grad_norm: 1.0386 loss: 0.8778 detection_loss_cls: 0.8778 2024/07/07 02:05:55 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 02:05:55 - mmengine - INFO - Iter(train) [ 21000/120000] base_lr: 1.8541e-04 lr: 1.8674e-05 eta: 1 day, 8:45:59 time: 1.1967 data_time: 0.0645 memory: 6243 grad_norm: 1.0390 loss: 0.8772 detection_loss_cls: 0.8772 2024/07/07 02:05:55 - mmengine - INFO - Saving checkpoint at 21000 iterations 2024/07/07 02:07:03 - mmengine - INFO - Iter(train) [ 21050/120000] base_lr: 1.8534e-04 lr: 1.8668e-05 eta: 1 day, 8:47:37 time: 1.1969 data_time: 0.0646 memory: 6243 grad_norm: 1.0384 loss: 0.8772 detection_loss_cls: 0.8772 2024/07/07 02:08:02 - mmengine - INFO - Iter(train) [ 21100/120000] base_lr: 1.8528e-04 lr: 1.8662e-05 eta: 1 day, 8:46:27 time: 1.1968 data_time: 0.0647 memory: 6243 grad_norm: 1.0385 loss: 0.8770 detection_loss_cls: 0.8770 2024/07/07 02:09:02 - mmengine - INFO - Iter(train) [ 21150/120000] base_lr: 1.8521e-04 lr: 1.8655e-05 eta: 1 day, 8:45:30 time: 1.1971 data_time: 0.0647 memory: 6243 grad_norm: 1.0376 loss: 0.8764 detection_loss_cls: 0.8764 2024/07/07 02:10:01 - mmengine - INFO - Iter(train) [ 21200/120000] base_lr: 1.8514e-04 lr: 1.8649e-05 eta: 1 day, 8:44:20 time: 1.1972 data_time: 0.0647 memory: 6243 grad_norm: 1.0373 loss: 0.8758 detection_loss_cls: 0.8758 2024/07/07 02:11:00 - mmengine - INFO - Iter(train) [ 21250/120000] base_lr: 1.8507e-04 lr: 1.8643e-05 eta: 1 day, 8:43:11 time: 1.1972 data_time: 0.0648 memory: 6243 grad_norm: 1.0382 loss: 0.8764 detection_loss_cls: 0.8764 2024/07/07 02:12:00 - mmengine - INFO - Iter(train) [ 21300/120000] base_lr: 1.8500e-04 lr: 1.8637e-05 eta: 1 day, 8:42:14 time: 1.1974 data_time: 0.0647 memory: 6243 grad_norm: 1.0379 loss: 0.8752 detection_loss_cls: 0.8752 2024/07/07 02:13:00 - mmengine - INFO - Iter(train) [ 21350/120000] base_lr: 1.8494e-04 lr: 1.8630e-05 eta: 1 day, 8:41:19 time: 1.1976 data_time: 0.0646 memory: 6243 grad_norm: 1.0386 loss: 0.8737 detection_loss_cls: 0.8737 2024/07/07 02:13:59 - mmengine - INFO - Iter(train) [ 21400/120000] base_lr: 1.8487e-04 lr: 1.8624e-05 eta: 1 day, 8:40:05 time: 1.1974 data_time: 0.0646 memory: 6243 grad_norm: 1.0384 loss: 0.8733 detection_loss_cls: 0.8733 2024/07/07 02:14:59 - mmengine - INFO - Iter(train) [ 21450/120000] base_lr: 1.8480e-04 lr: 1.8618e-05 eta: 1 day, 8:39:14 time: 1.1976 data_time: 0.0647 memory: 6243 grad_norm: 1.0385 loss: 0.8742 detection_loss_cls: 0.8742 2024/07/07 02:15:58 - mmengine - INFO - Iter(train) [ 21500/120000] base_lr: 1.8473e-04 lr: 1.8612e-05 eta: 1 day, 8:38:03 time: 1.1976 data_time: 0.0647 memory: 6243 grad_norm: 1.0382 loss: 0.8732 detection_loss_cls: 0.8732 2024/07/07 02:16:57 - mmengine - INFO - Iter(train) [ 21550/120000] base_lr: 1.8466e-04 lr: 1.8605e-05 eta: 1 day, 8:37:02 time: 1.1977 data_time: 0.0647 memory: 6243 grad_norm: 1.0385 loss: 0.8724 detection_loss_cls: 0.8724 2024/07/07 02:17:57 - mmengine - INFO - Iter(train) [ 21600/120000] base_lr: 1.8459e-04 lr: 1.8599e-05 eta: 1 day, 8:36:03 time: 1.1979 data_time: 0.0647 memory: 6243 grad_norm: 1.0382 loss: 0.8717 detection_loss_cls: 0.8717 2024/07/07 02:18:57 - mmengine - INFO - Iter(train) [ 21650/120000] base_lr: 1.8452e-04 lr: 1.8593e-05 eta: 1 day, 8:35:10 time: 1.1982 data_time: 0.0648 memory: 6243 grad_norm: 1.0378 loss: 0.8720 detection_loss_cls: 0.8720 2024/07/07 02:19:56 - mmengine - INFO - Iter(train) [ 21700/120000] base_lr: 1.8445e-04 lr: 1.8586e-05 eta: 1 day, 8:33:59 time: 1.1982 data_time: 0.0648 memory: 6243 grad_norm: 1.0377 loss: 0.8716 detection_loss_cls: 0.8716 2024/07/07 02:20:56 - mmengine - INFO - Iter(train) [ 21750/120000] base_lr: 1.8438e-04 lr: 1.8580e-05 eta: 1 day, 8:33:11 time: 1.1985 data_time: 0.0648 memory: 6243 grad_norm: 1.0389 loss: 0.8707 detection_loss_cls: 0.8707 2024/07/07 02:21:56 - mmengine - INFO - Iter(train) [ 21800/120000] base_lr: 1.8431e-04 lr: 1.8574e-05 eta: 1 day, 8:32:09 time: 1.1985 data_time: 0.0648 memory: 6243 grad_norm: 1.0390 loss: 0.8700 detection_loss_cls: 0.8700 2024/07/07 02:22:55 - mmengine - INFO - Iter(train) [ 21850/120000] base_lr: 1.8424e-04 lr: 1.8567e-05 eta: 1 day, 8:31:02 time: 1.1985 data_time: 0.0648 memory: 6243 grad_norm: 1.0381 loss: 0.8703 detection_loss_cls: 0.8703 2024/07/07 02:23:55 - mmengine - INFO - Iter(train) [ 21900/120000] base_lr: 1.8417e-04 lr: 1.8561e-05 eta: 1 day, 8:30:08 time: 1.1989 data_time: 0.0648 memory: 6243 grad_norm: 1.0383 loss: 0.8705 detection_loss_cls: 0.8705 2024/07/07 02:24:55 - mmengine - INFO - Iter(train) [ 21950/120000] base_lr: 1.8410e-04 lr: 1.8555e-05 eta: 1 day, 8:29:04 time: 1.1990 data_time: 0.0648 memory: 6243 grad_norm: 1.0384 loss: 0.8700 detection_loss_cls: 0.8700 2024/07/07 02:25:54 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 02:25:54 - mmengine - INFO - Iter(train) [ 22000/120000] base_lr: 1.8403e-04 lr: 1.8548e-05 eta: 1 day, 8:27:59 time: 1.1989 data_time: 0.0648 memory: 6243 grad_norm: 1.0377 loss: 0.8692 detection_loss_cls: 0.8692 2024/07/07 02:25:54 - mmengine - INFO - Saving checkpoint at 22000 iterations 2024/07/07 02:27:01 - mmengine - INFO - Iter(train) [ 22050/120000] base_lr: 1.8396e-04 lr: 1.8542e-05 eta: 1 day, 8:29:00 time: 1.1989 data_time: 0.0647 memory: 6243 grad_norm: 1.0390 loss: 0.8691 detection_loss_cls: 0.8691 2024/07/07 02:28:00 - mmengine - INFO - Iter(train) [ 22100/120000] base_lr: 1.8389e-04 lr: 1.8535e-05 eta: 1 day, 8:27:51 time: 1.1989 data_time: 0.0647 memory: 6243 grad_norm: 1.0390 loss: 0.8684 detection_loss_cls: 0.8684 2024/07/07 02:28:58 - mmengine - INFO - Iter(train) [ 22150/120000] base_lr: 1.8382e-04 lr: 1.8529e-05 eta: 1 day, 8:26:30 time: 1.1986 data_time: 0.0647 memory: 6243 grad_norm: 1.0395 loss: 0.8684 detection_loss_cls: 0.8684 2024/07/07 02:29:58 - mmengine - INFO - Iter(train) [ 22200/120000] base_lr: 1.8375e-04 lr: 1.8522e-05 eta: 1 day, 8:25:27 time: 1.1988 data_time: 0.0647 memory: 6243 grad_norm: 1.0394 loss: 0.8685 detection_loss_cls: 0.8685 2024/07/07 02:30:57 - mmengine - INFO - Iter(train) [ 22250/120000] base_lr: 1.8368e-04 lr: 1.8516e-05 eta: 1 day, 8:24:13 time: 1.1988 data_time: 0.0647 memory: 6243 grad_norm: 1.0394 loss: 0.8676 detection_loss_cls: 0.8676 2024/07/07 02:31:55 - mmengine - INFO - Iter(train) [ 22300/120000] base_lr: 1.8360e-04 lr: 1.8509e-05 eta: 1 day, 8:22:56 time: 1.1986 data_time: 0.0647 memory: 6243 grad_norm: 1.0388 loss: 0.8675 detection_loss_cls: 0.8675 2024/07/07 02:32:55 - mmengine - INFO - Iter(train) [ 22350/120000] base_lr: 1.8353e-04 lr: 1.8503e-05 eta: 1 day, 8:21:50 time: 1.1986 data_time: 0.0648 memory: 6243 grad_norm: 1.0380 loss: 0.8683 detection_loss_cls: 0.8683 2024/07/07 02:33:54 - mmengine - INFO - Iter(train) [ 22400/120000] base_lr: 1.8346e-04 lr: 1.8496e-05 eta: 1 day, 8:20:42 time: 1.1987 data_time: 0.0648 memory: 6243 grad_norm: 1.0381 loss: 0.8687 detection_loss_cls: 0.8687 2024/07/07 02:34:52 - mmengine - INFO - Iter(train) [ 22450/120000] base_lr: 1.8339e-04 lr: 1.8490e-05 eta: 1 day, 8:19:25 time: 1.1986 data_time: 0.0649 memory: 6243 grad_norm: 1.0377 loss: 0.8681 detection_loss_cls: 0.8681 2024/07/07 02:35:52 - mmengine - INFO - Iter(train) [ 22500/120000] base_lr: 1.8332e-04 lr: 1.8483e-05 eta: 1 day, 8:18:23 time: 1.1987 data_time: 0.0649 memory: 6243 grad_norm: 1.0367 loss: 0.8681 detection_loss_cls: 0.8681 2024/07/07 02:36:50 - mmengine - INFO - Iter(train) [ 22550/120000] base_lr: 1.8324e-04 lr: 1.8477e-05 eta: 1 day, 8:17:08 time: 1.1987 data_time: 0.0650 memory: 6243 grad_norm: 1.0366 loss: 0.8698 detection_loss_cls: 0.8698 2024/07/07 02:37:49 - mmengine - INFO - Iter(train) [ 22600/120000] base_lr: 1.8317e-04 lr: 1.8470e-05 eta: 1 day, 8:15:52 time: 1.1986 data_time: 0.0651 memory: 6243 grad_norm: 1.0367 loss: 0.8705 detection_loss_cls: 0.8705 2024/07/07 02:38:48 - mmengine - INFO - Iter(train) [ 22650/120000] base_lr: 1.8310e-04 lr: 1.8464e-05 eta: 1 day, 8:14:49 time: 1.1987 data_time: 0.0650 memory: 6243 grad_norm: 1.0376 loss: 0.8699 detection_loss_cls: 0.8699 2024/07/07 02:39:47 - mmengine - INFO - Iter(train) [ 22700/120000] base_lr: 1.8303e-04 lr: 1.8457e-05 eta: 1 day, 8:13:39 time: 1.1987 data_time: 0.0651 memory: 6243 grad_norm: 1.0379 loss: 0.8697 detection_loss_cls: 0.8697 2024/07/07 02:40:46 - mmengine - INFO - Iter(train) [ 22750/120000] base_lr: 1.8296e-04 lr: 1.8450e-05 eta: 1 day, 8:12:24 time: 1.1986 data_time: 0.0650 memory: 6243 grad_norm: 1.0381 loss: 0.8682 detection_loss_cls: 0.8682 2024/07/07 02:41:45 - mmengine - INFO - Iter(train) [ 22800/120000] base_lr: 1.8288e-04 lr: 1.8444e-05 eta: 1 day, 8:11:21 time: 1.1986 data_time: 0.0649 memory: 6243 grad_norm: 1.0384 loss: 0.8668 detection_loss_cls: 0.8668 2024/07/07 02:42:43 - mmengine - INFO - Iter(train) [ 22850/120000] base_lr: 1.8281e-04 lr: 1.8437e-05 eta: 1 day, 8:10:03 time: 1.1985 data_time: 0.0649 memory: 6243 grad_norm: 1.0387 loss: 0.8665 detection_loss_cls: 0.8665 2024/07/07 02:43:42 - mmengine - INFO - Iter(train) [ 22900/120000] base_lr: 1.8274e-04 lr: 1.8431e-05 eta: 1 day, 8:08:48 time: 1.1984 data_time: 0.0649 memory: 6243 grad_norm: 1.0385 loss: 0.8667 detection_loss_cls: 0.8667 2024/07/07 02:44:41 - mmengine - INFO - Iter(train) [ 22950/120000] base_lr: 1.8266e-04 lr: 1.8424e-05 eta: 1 day, 8:07:47 time: 1.1984 data_time: 0.0650 memory: 6243 grad_norm: 1.0391 loss: 0.8668 detection_loss_cls: 0.8668 2024/07/07 02:45:40 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 02:45:40 - mmengine - INFO - Iter(train) [ 23000/120000] base_lr: 1.8259e-04 lr: 1.8417e-05 eta: 1 day, 8:06:33 time: 1.1983 data_time: 0.0650 memory: 6243 grad_norm: 1.0393 loss: 0.8665 detection_loss_cls: 0.8665 2024/07/07 02:45:40 - mmengine - INFO - Saving checkpoint at 23000 iterations 2024/07/07 02:46:47 - mmengine - INFO - Iter(train) [ 23050/120000] base_lr: 1.8252e-04 lr: 1.8411e-05 eta: 1 day, 8:07:15 time: 1.1983 data_time: 0.0649 memory: 6243 grad_norm: 1.0430 loss: 0.8652 detection_loss_cls: 0.8652 2024/07/07 02:47:47 - mmengine - INFO - Iter(train) [ 23100/120000] base_lr: 1.8244e-04 lr: 1.8404e-05 eta: 1 day, 8:06:20 time: 1.1984 data_time: 0.0649 memory: 6243 grad_norm: 1.0436 loss: 0.8653 detection_loss_cls: 0.8653 2024/07/07 02:48:46 - mmengine - INFO - Iter(train) [ 23150/120000] base_lr: 1.8237e-04 lr: 1.8397e-05 eta: 1 day, 8:05:16 time: 1.1986 data_time: 0.0649 memory: 6243 grad_norm: 1.0433 loss: 0.8652 detection_loss_cls: 0.8652 2024/07/07 02:49:45 - mmengine - INFO - Iter(train) [ 23200/120000] base_lr: 1.8230e-04 lr: 1.8390e-05 eta: 1 day, 8:04:09 time: 1.1986 data_time: 0.0649 memory: 6243 grad_norm: 1.0433 loss: 0.8646 detection_loss_cls: 0.8646 2024/07/07 02:50:45 - mmengine - INFO - Iter(train) [ 23250/120000] base_lr: 1.8222e-04 lr: 1.8384e-05 eta: 1 day, 8:03:12 time: 1.1988 data_time: 0.0649 memory: 6243 grad_norm: 1.0434 loss: 0.8649 detection_loss_cls: 0.8649 2024/07/07 02:51:44 - mmengine - INFO - Iter(train) [ 23300/120000] base_lr: 1.8215e-04 lr: 1.8377e-05 eta: 1 day, 8:02:06 time: 1.1988 data_time: 0.0649 memory: 6243 grad_norm: 1.0431 loss: 0.8647 detection_loss_cls: 0.8647 2024/07/07 02:52:44 - mmengine - INFO - Iter(train) [ 23350/120000] base_lr: 1.8207e-04 lr: 1.8370e-05 eta: 1 day, 8:01:01 time: 1.1988 data_time: 0.0649 memory: 6243 grad_norm: 1.0433 loss: 0.8644 detection_loss_cls: 0.8644 2024/07/07 02:53:43 - mmengine - INFO - Iter(train) [ 23400/120000] base_lr: 1.8200e-04 lr: 1.8363e-05 eta: 1 day, 8:00:03 time: 1.1990 data_time: 0.0650 memory: 6243 grad_norm: 1.0429 loss: 0.8666 detection_loss_cls: 0.8666 2024/07/07 02:54:43 - mmengine - INFO - Iter(train) [ 23450/120000] base_lr: 1.8192e-04 lr: 1.8357e-05 eta: 1 day, 7:59:01 time: 1.1993 data_time: 0.0650 memory: 6243 grad_norm: 1.0426 loss: 0.8655 detection_loss_cls: 0.8655 2024/07/07 02:55:43 - mmengine - INFO - Iter(train) [ 23500/120000] base_lr: 1.8185e-04 lr: 1.8350e-05 eta: 1 day, 7:58:03 time: 1.1993 data_time: 0.0650 memory: 6243 grad_norm: 1.0429 loss: 0.8654 detection_loss_cls: 0.8654 2024/07/07 02:56:42 - mmengine - INFO - Iter(train) [ 23550/120000] base_lr: 1.8177e-04 lr: 1.8343e-05 eta: 1 day, 7:57:03 time: 1.1994 data_time: 0.0650 memory: 6243 grad_norm: 1.0433 loss: 0.8654 detection_loss_cls: 0.8654 2024/07/07 02:57:41 - mmengine - INFO - Iter(train) [ 23600/120000] base_lr: 1.8170e-04 lr: 1.8336e-05 eta: 1 day, 7:55:59 time: 1.1994 data_time: 0.0650 memory: 6243 grad_norm: 1.0432 loss: 0.8649 detection_loss_cls: 0.8649 2024/07/07 02:58:41 - mmengine - INFO - Iter(train) [ 23650/120000] base_lr: 1.8162e-04 lr: 1.8329e-05 eta: 1 day, 7:55:01 time: 1.1994 data_time: 0.0650 memory: 6243 grad_norm: 1.0428 loss: 0.8644 detection_loss_cls: 0.8644 2024/07/07 02:59:41 - mmengine - INFO - Iter(train) [ 23700/120000] base_lr: 1.8155e-04 lr: 1.8323e-05 eta: 1 day, 7:54:06 time: 1.1997 data_time: 0.0650 memory: 6243 grad_norm: 1.0427 loss: 0.8642 detection_loss_cls: 0.8642 2024/07/07 03:00:41 - mmengine - INFO - Iter(train) [ 23750/120000] base_lr: 1.8147e-04 lr: 1.8316e-05 eta: 1 day, 7:53:03 time: 1.1998 data_time: 0.0650 memory: 6243 grad_norm: 1.0408 loss: 0.8631 detection_loss_cls: 0.8631 2024/07/07 03:01:40 - mmengine - INFO - Iter(train) [ 23800/120000] base_lr: 1.8140e-04 lr: 1.8309e-05 eta: 1 day, 7:52:00 time: 1.1999 data_time: 0.0650 memory: 6243 grad_norm: 1.0403 loss: 0.8630 detection_loss_cls: 0.8630 2024/07/07 03:02:39 - mmengine - INFO - Iter(train) [ 23850/120000] base_lr: 1.8132e-04 lr: 1.8302e-05 eta: 1 day, 7:50:56 time: 1.2000 data_time: 0.0650 memory: 6243 grad_norm: 1.0404 loss: 0.8625 detection_loss_cls: 0.8625 2024/07/07 03:03:38 - mmengine - INFO - Iter(train) [ 23900/120000] base_lr: 1.8125e-04 lr: 1.8295e-05 eta: 1 day, 7:49:47 time: 1.2000 data_time: 0.0650 memory: 6243 grad_norm: 1.0407 loss: 0.8614 detection_loss_cls: 0.8614 2024/07/07 03:04:38 - mmengine - INFO - Iter(train) [ 23950/120000] base_lr: 1.8117e-04 lr: 1.8288e-05 eta: 1 day, 7:48:45 time: 1.1999 data_time: 0.0650 memory: 6243 grad_norm: 1.0406 loss: 0.8607 detection_loss_cls: 0.8607 2024/07/07 03:05:38 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 03:05:38 - mmengine - INFO - Iter(train) [ 24000/120000] base_lr: 1.8109e-04 lr: 1.8281e-05 eta: 1 day, 7:47:49 time: 1.2002 data_time: 0.0650 memory: 6243 grad_norm: 1.0410 loss: 0.8607 detection_loss_cls: 0.8607 2024/07/07 03:05:38 - mmengine - INFO - Saving checkpoint at 24000 iterations 2024/07/07 03:06:45 - mmengine - INFO - Iter(train) [ 24050/120000] base_lr: 1.8102e-04 lr: 1.8274e-05 eta: 1 day, 7:48:21 time: 1.1945 data_time: 0.0594 memory: 6243 grad_norm: 1.0411 loss: 0.8608 detection_loss_cls: 0.8608 2024/07/07 03:07:44 - mmengine - INFO - Iter(train) [ 24100/120000] base_lr: 1.8094e-04 lr: 1.8267e-05 eta: 1 day, 7:47:10 time: 1.1942 data_time: 0.0594 memory: 6243 grad_norm: 1.0407 loss: 0.8608 detection_loss_cls: 0.8608 2024/07/07 03:08:44 - mmengine - INFO - Iter(train) [ 24150/120000] base_lr: 1.8087e-04 lr: 1.8260e-05 eta: 1 day, 7:46:16 time: 1.1944 data_time: 0.0594 memory: 6243 grad_norm: 1.0406 loss: 0.8610 detection_loss_cls: 0.8610 2024/07/07 03:09:43 - mmengine - INFO - Iter(train) [ 24200/120000] base_lr: 1.8079e-04 lr: 1.8253e-05 eta: 1 day, 7:45:10 time: 1.1944 data_time: 0.0594 memory: 6243 grad_norm: 1.0406 loss: 0.8599 detection_loss_cls: 0.8599 2024/07/07 03:10:42 - mmengine - INFO - Iter(train) [ 24250/120000] base_lr: 1.8071e-04 lr: 1.8247e-05 eta: 1 day, 7:44:06 time: 1.1942 data_time: 0.0593 memory: 6243 grad_norm: 1.0410 loss: 0.8594 detection_loss_cls: 0.8594 2024/07/07 03:11:42 - mmengine - INFO - Iter(train) [ 24300/120000] base_lr: 1.8063e-04 lr: 1.8240e-05 eta: 1 day, 7:43:08 time: 1.1943 data_time: 0.0593 memory: 6243 grad_norm: 1.0411 loss: 0.8594 detection_loss_cls: 0.8594 2024/07/07 03:12:42 - mmengine - INFO - Iter(train) [ 24350/120000] base_lr: 1.8056e-04 lr: 1.8233e-05 eta: 1 day, 7:42:07 time: 1.1944 data_time: 0.0594 memory: 6243 grad_norm: 1.0408 loss: 0.8596 detection_loss_cls: 0.8596 2024/07/07 03:13:41 - mmengine - INFO - Iter(train) [ 24400/120000] base_lr: 1.8048e-04 lr: 1.8225e-05 eta: 1 day, 7:41:06 time: 1.1944 data_time: 0.0594 memory: 6243 grad_norm: 1.0404 loss: 0.8590 detection_loss_cls: 0.8590 2024/07/07 03:14:41 - mmengine - INFO - Iter(train) [ 24450/120000] base_lr: 1.8040e-04 lr: 1.8218e-05 eta: 1 day, 7:40:10 time: 1.1945 data_time: 0.0593 memory: 6243 grad_norm: 1.0411 loss: 0.8587 detection_loss_cls: 0.8587 2024/07/07 03:15:40 - mmengine - INFO - Iter(train) [ 24500/120000] base_lr: 1.8033e-04 lr: 1.8211e-05 eta: 1 day, 7:39:04 time: 1.1943 data_time: 0.0593 memory: 6243 grad_norm: 1.0418 loss: 0.8583 detection_loss_cls: 0.8583 2024/07/07 03:16:39 - mmengine - INFO - Iter(train) [ 24550/120000] base_lr: 1.8025e-04 lr: 1.8204e-05 eta: 1 day, 7:37:55 time: 1.1942 data_time: 0.0593 memory: 6243 grad_norm: 1.0409 loss: 0.8579 detection_loss_cls: 0.8579 2024/07/07 03:17:39 - mmengine - INFO - Iter(train) [ 24600/120000] base_lr: 1.8017e-04 lr: 1.8197e-05 eta: 1 day, 7:37:00 time: 1.1944 data_time: 0.0592 memory: 6243 grad_norm: 1.0414 loss: 0.8574 detection_loss_cls: 0.8574 2024/07/07 03:18:39 - mmengine - INFO - Iter(train) [ 24650/120000] base_lr: 1.8009e-04 lr: 1.8190e-05 eta: 1 day, 7:36:02 time: 1.1946 data_time: 0.0592 memory: 6243 grad_norm: 1.0415 loss: 0.8565 detection_loss_cls: 0.8565 2024/07/07 03:19:38 - mmengine - INFO - Iter(train) [ 24700/120000] base_lr: 1.8001e-04 lr: 1.8183e-05 eta: 1 day, 7:34:52 time: 1.1945 data_time: 0.0592 memory: 6243 grad_norm: 1.0415 loss: 0.8564 detection_loss_cls: 0.8564 2024/07/07 03:20:38 - mmengine - INFO - Iter(train) [ 24750/120000] base_lr: 1.7994e-04 lr: 1.8176e-05 eta: 1 day, 7:33:58 time: 1.1946 data_time: 0.0592 memory: 6243 grad_norm: 1.0414 loss: 0.8555 detection_loss_cls: 0.8555 2024/07/07 03:21:37 - mmengine - INFO - Iter(train) [ 24800/120000] base_lr: 1.7986e-04 lr: 1.8169e-05 eta: 1 day, 7:32:59 time: 1.1947 data_time: 0.0593 memory: 6243 grad_norm: 1.0418 loss: 0.8555 detection_loss_cls: 0.8555 2024/07/07 03:22:37 - mmengine - INFO - Iter(train) [ 24850/120000] base_lr: 1.7978e-04 lr: 1.8162e-05 eta: 1 day, 7:31:56 time: 1.1947 data_time: 0.0593 memory: 6243 grad_norm: 1.0406 loss: 0.8564 detection_loss_cls: 0.8564 2024/07/07 03:23:37 - mmengine - INFO - Iter(train) [ 24900/120000] base_lr: 1.7970e-04 lr: 1.8155e-05 eta: 1 day, 7:30:57 time: 1.1948 data_time: 0.0592 memory: 6243 grad_norm: 1.0412 loss: 0.8550 detection_loss_cls: 0.8550 2024/07/07 03:24:36 - mmengine - INFO - Iter(train) [ 24950/120000] base_lr: 1.7962e-04 lr: 1.8147e-05 eta: 1 day, 7:29:58 time: 1.1949 data_time: 0.0592 memory: 6243 grad_norm: 1.0411 loss: 0.8546 detection_loss_cls: 0.8546 2024/07/07 03:25:36 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 03:25:36 - mmengine - INFO - Iter(train) [ 25000/120000] base_lr: 1.7954e-04 lr: 1.8140e-05 eta: 1 day, 7:28:58 time: 1.1949 data_time: 0.0591 memory: 6243 grad_norm: 1.0405 loss: 0.8537 detection_loss_cls: 0.8537 2024/07/07 03:25:36 - mmengine - INFO - Saving checkpoint at 25000 iterations 2024/07/07 03:26:24 - mmengine - INFO - Iter(val) [ 50/105] eta: 0:00:44 time: 0.8040 data_time: 0.0304 memory: 6808 2024/07/07 03:27:05 - mmengine - INFO - Iter(val) [100/105] eta: 0:00:04 time: 0.8049 data_time: 0.0302 memory: 6807 2024/07/07 03:27:11 - mmengine - INFO - Evaluating bbox... 2024/07/07 03:27:39 - mmengine - INFO - bbox_mAP_copypaste: 0.365 0.527 0.390 0.162 0.404 0.529 2024/07/07 03:27:39 - mmengine - INFO - Iter(val) [105/105] coco/bbox_mAP: 0.3650 coco/bbox_mAP_50: 0.5270 coco/bbox_mAP_75: 0.3900 coco/bbox_mAP_s: 0.1620 coco/bbox_mAP_m: 0.4040 coco/bbox_mAP_l: 0.5290 data_time: 0.0288 time: 0.8024 2024/07/07 03:28:39 - mmengine - INFO - Iter(train) [ 25050/120000] base_lr: 1.7946e-04 lr: 1.8133e-05 eta: 1 day, 7:33:21 time: 1.2006 data_time: 0.0648 memory: 6803 grad_norm: 1.0405 loss: 0.8528 detection_loss_cls: 0.8528 2024/07/07 03:29:39 - mmengine - INFO - Iter(train) [ 25100/120000] base_lr: 1.7939e-04 lr: 1.8126e-05 eta: 1 day, 7:32:24 time: 1.2009 data_time: 0.0648 memory: 6238 grad_norm: 1.0407 loss: 0.8532 detection_loss_cls: 0.8532 2024/07/07 03:30:39 - mmengine - INFO - Iter(train) [ 25150/120000] base_lr: 1.7931e-04 lr: 1.8119e-05 eta: 1 day, 7:31:29 time: 1.2010 data_time: 0.0648 memory: 6238 grad_norm: 1.0409 loss: 0.8526 detection_loss_cls: 0.8526 2024/07/07 03:31:40 - mmengine - INFO - Iter(train) [ 25200/120000] base_lr: 1.7923e-04 lr: 1.8112e-05 eta: 1 day, 7:30:38 time: 1.2014 data_time: 0.0648 memory: 6238 grad_norm: 1.0414 loss: 0.8531 detection_loss_cls: 0.8531 2024/07/07 03:32:39 - mmengine - INFO - Iter(train) [ 25250/120000] base_lr: 1.7915e-04 lr: 1.8104e-05 eta: 1 day, 7:29:36 time: 1.2015 data_time: 0.0647 memory: 6238 grad_norm: 1.0409 loss: 0.8512 detection_loss_cls: 0.8512 2024/07/07 03:33:40 - mmengine - INFO - Iter(train) [ 25300/120000] base_lr: 1.7907e-04 lr: 1.8097e-05 eta: 1 day, 7:28:44 time: 1.2017 data_time: 0.0647 memory: 6238 grad_norm: 1.0411 loss: 0.8506 detection_loss_cls: 0.8506 2024/07/07 03:34:41 - mmengine - INFO - Iter(train) [ 25350/120000] base_lr: 1.7899e-04 lr: 1.8090e-05 eta: 1 day, 7:27:53 time: 1.2019 data_time: 0.0648 memory: 6238 grad_norm: 1.0404 loss: 0.8511 detection_loss_cls: 0.8511 2024/07/07 03:35:41 - mmengine - INFO - Iter(train) [ 25400/120000] base_lr: 1.7891e-04 lr: 1.8083e-05 eta: 1 day, 7:26:55 time: 1.2022 data_time: 0.0648 memory: 6238 grad_norm: 1.0400 loss: 0.8495 detection_loss_cls: 0.8495 2024/07/07 03:36:41 - mmengine - INFO - Iter(train) [ 25450/120000] base_lr: 1.7883e-04 lr: 1.8075e-05 eta: 1 day, 7:26:02 time: 1.2023 data_time: 0.0647 memory: 6238 grad_norm: 1.0397 loss: 0.8477 detection_loss_cls: 0.8477 2024/07/07 03:37:42 - mmengine - INFO - Iter(train) [ 25500/120000] base_lr: 1.7875e-04 lr: 1.8068e-05 eta: 1 day, 7:25:06 time: 1.2026 data_time: 0.0647 memory: 6238 grad_norm: 1.0398 loss: 0.8485 detection_loss_cls: 0.8485 2024/07/07 03:38:41 - mmengine - INFO - Iter(train) [ 25550/120000] base_lr: 1.7867e-04 lr: 1.8061e-05 eta: 1 day, 7:24:02 time: 1.2026 data_time: 0.0647 memory: 6238 grad_norm: 1.0398 loss: 0.8490 detection_loss_cls: 0.8490 2024/07/07 03:39:41 - mmengine - INFO - Iter(train) [ 25600/120000] base_lr: 1.7859e-04 lr: 1.8053e-05 eta: 1 day, 7:23:04 time: 1.2027 data_time: 0.0647 memory: 6238 grad_norm: 1.0399 loss: 0.8496 detection_loss_cls: 0.8496 2024/07/07 03:40:42 - mmengine - INFO - Iter(train) [ 25650/120000] base_lr: 1.7851e-04 lr: 1.8046e-05 eta: 1 day, 7:22:13 time: 1.2029 data_time: 0.0647 memory: 6238 grad_norm: 1.0396 loss: 0.8499 detection_loss_cls: 0.8499 2024/07/07 03:41:42 - mmengine - INFO - Iter(train) [ 25700/120000] base_lr: 1.7843e-04 lr: 1.8039e-05 eta: 1 day, 7:21:16 time: 1.2032 data_time: 0.0647 memory: 6238 grad_norm: 1.0409 loss: 0.8485 detection_loss_cls: 0.8485 2024/07/07 03:42:42 - mmengine - INFO - Iter(train) [ 25750/120000] base_lr: 1.7835e-04 lr: 1.8031e-05 eta: 1 day, 7:20:22 time: 1.2032 data_time: 0.0647 memory: 6238 grad_norm: 1.0398 loss: 0.8488 detection_loss_cls: 0.8488 2024/07/07 03:43:44 - mmengine - INFO - Iter(train) [ 25800/120000] base_lr: 1.7826e-04 lr: 1.8024e-05 eta: 1 day, 7:19:35 time: 1.2036 data_time: 0.0647 memory: 6238 grad_norm: 1.0387 loss: 0.8497 detection_loss_cls: 0.8497 2024/07/07 03:44:44 - mmengine - INFO - Iter(train) [ 25850/120000] base_lr: 1.7818e-04 lr: 1.8017e-05 eta: 1 day, 7:18:40 time: 1.2039 data_time: 0.0647 memory: 6238 grad_norm: 1.0384 loss: 0.8485 detection_loss_cls: 0.8485 2024/07/07 03:45:44 - mmengine - INFO - Iter(train) [ 25900/120000] base_lr: 1.7810e-04 lr: 1.8009e-05 eta: 1 day, 7:17:41 time: 1.2039 data_time: 0.0647 memory: 6238 grad_norm: 1.0390 loss: 0.8469 detection_loss_cls: 0.8469 2024/07/07 03:46:45 - mmengine - INFO - Iter(train) [ 25950/120000] base_lr: 1.7802e-04 lr: 1.8002e-05 eta: 1 day, 7:16:49 time: 1.2042 data_time: 0.0647 memory: 6238 grad_norm: 1.0385 loss: 0.8469 detection_loss_cls: 0.8469 2024/07/07 03:47:45 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 03:47:45 - mmengine - INFO - Iter(train) [ 26000/120000] base_lr: 1.7794e-04 lr: 1.7994e-05 eta: 1 day, 7:15:52 time: 1.2044 data_time: 0.0647 memory: 6238 grad_norm: 1.0380 loss: 0.8470 detection_loss_cls: 0.8470 2024/07/07 03:47:45 - mmengine - INFO - Saving checkpoint at 26000 iterations 2024/07/07 03:48:52 - mmengine - INFO - Iter(train) [ 26050/120000] base_lr: 1.7786e-04 lr: 1.7987e-05 eta: 1 day, 7:15:58 time: 1.2044 data_time: 0.0647 memory: 6238 grad_norm: 1.0362 loss: 0.8465 detection_loss_cls: 0.8465 2024/07/07 03:49:52 - mmengine - INFO - Iter(train) [ 26100/120000] base_lr: 1.7778e-04 lr: 1.7980e-05 eta: 1 day, 7:14:58 time: 1.2046 data_time: 0.0647 memory: 6238 grad_norm: 1.0366 loss: 0.8471 detection_loss_cls: 0.8471 2024/07/07 03:50:51 - 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mmengine - INFO - Iter(train) [ 27850/120000] base_lr: 1.7483e-04 lr: 1.7712e-05 eta: 1 day, 6:40:43 time: 1.2104 data_time: 0.0647 memory: 6238 grad_norm: 1.0311 loss: 0.8376 detection_loss_cls: 0.8376 2024/07/07 04:25:54 - mmengine - INFO - Iter(train) [ 27900/120000] base_lr: 1.7475e-04 lr: 1.7704e-05 eta: 1 day, 6:39:46 time: 1.2107 data_time: 0.0647 memory: 6238 grad_norm: 1.0311 loss: 0.8370 detection_loss_cls: 0.8370 2024/07/07 04:26:54 - mmengine - INFO - Iter(train) [ 27950/120000] base_lr: 1.7466e-04 lr: 1.7696e-05 eta: 1 day, 6:38:44 time: 1.2108 data_time: 0.0647 memory: 6238 grad_norm: 1.0312 loss: 0.8372 detection_loss_cls: 0.8372 2024/07/07 04:27:54 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 04:27:54 - mmengine - INFO - Iter(train) [ 28000/120000] base_lr: 1.7457e-04 lr: 1.7688e-05 eta: 1 day, 6:37:48 time: 1.2109 data_time: 0.0647 memory: 6238 grad_norm: 1.0307 loss: 0.8365 detection_loss_cls: 0.8365 2024/07/07 04:27:54 - mmengine - INFO - Saving checkpoint at 28000 iterations 2024/07/07 04:29:02 - mmengine - INFO - Iter(train) [ 28050/120000] base_lr: 1.7449e-04 lr: 1.7681e-05 eta: 1 day, 6:37:50 time: 1.2111 data_time: 0.0645 memory: 6238 grad_norm: 1.0309 loss: 0.8356 detection_loss_cls: 0.8356 2024/07/07 04:30:03 - mmengine - INFO - Iter(train) [ 28100/120000] base_lr: 1.7440e-04 lr: 1.7673e-05 eta: 1 day, 6:36:53 time: 1.2115 data_time: 0.0646 memory: 6238 grad_norm: 1.0312 loss: 0.8362 detection_loss_cls: 0.8362 2024/07/07 04:31:03 - mmengine - INFO - Iter(train) [ 28150/120000] base_lr: 1.7431e-04 lr: 1.7665e-05 eta: 1 day, 6:35:54 time: 1.2115 data_time: 0.0645 memory: 6238 grad_norm: 1.0309 loss: 0.8353 detection_loss_cls: 0.8353 2024/07/07 04:32:03 - mmengine - INFO - Iter(train) [ 28200/120000] base_lr: 1.7423e-04 lr: 1.7657e-05 eta: 1 day, 6:34:57 time: 1.2118 data_time: 0.0645 memory: 6238 grad_norm: 1.0304 loss: 0.8354 detection_loss_cls: 0.8354 2024/07/07 04:33:04 - mmengine - INFO - Iter(train) [ 28250/120000] base_lr: 1.7414e-04 lr: 1.7649e-05 eta: 1 day, 6:34:01 time: 1.2121 data_time: 0.0645 memory: 6238 grad_norm: 1.0300 loss: 0.8358 detection_loss_cls: 0.8358 2024/07/07 04:34:04 - 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mmengine - INFO - Iter(train) [ 29700/120000] base_lr: 1.7155e-04 lr: 1.7414e-05 eta: 1 day, 6:06:59 time: 1.2095 data_time: 0.0592 memory: 6238 grad_norm: 1.0339 loss: 0.8397 detection_loss_cls: 0.8397 2024/07/07 05:03:21 - mmengine - INFO - Iter(train) [ 29750/120000] base_lr: 1.7146e-04 lr: 1.7406e-05 eta: 1 day, 6:06:01 time: 1.2095 data_time: 0.0592 memory: 6238 grad_norm: 1.0349 loss: 0.8390 detection_loss_cls: 0.8390 2024/07/07 05:04:22 - mmengine - INFO - Iter(train) [ 29800/120000] base_lr: 1.7137e-04 lr: 1.7397e-05 eta: 1 day, 6:05:03 time: 1.2093 data_time: 0.0592 memory: 6238 grad_norm: 1.0349 loss: 0.8388 detection_loss_cls: 0.8388 2024/07/07 05:05:22 - mmengine - INFO - Iter(train) [ 29850/120000] base_lr: 1.7128e-04 lr: 1.7389e-05 eta: 1 day, 6:04:05 time: 1.2093 data_time: 0.0592 memory: 6238 grad_norm: 1.0351 loss: 0.8400 detection_loss_cls: 0.8400 2024/07/07 05:06:23 - mmengine - INFO - Iter(train) [ 29900/120000] base_lr: 1.7119e-04 lr: 1.7381e-05 eta: 1 day, 6:03:09 time: 1.2095 data_time: 0.0592 memory: 6238 grad_norm: 1.0344 loss: 0.8405 detection_loss_cls: 0.8405 2024/07/07 05:07:23 - mmengine - INFO - Iter(train) [ 29950/120000] base_lr: 1.7110e-04 lr: 1.7372e-05 eta: 1 day, 6:02:12 time: 1.2094 data_time: 0.0593 memory: 6238 grad_norm: 1.0349 loss: 0.8416 detection_loss_cls: 0.8416 2024/07/07 05:08:24 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 05:08:24 - mmengine - INFO - Iter(train) [ 30000/120000] base_lr: 1.7101e-04 lr: 1.7364e-05 eta: 1 day, 6:01:16 time: 1.2096 data_time: 0.0593 memory: 6238 grad_norm: 1.0352 loss: 0.8408 detection_loss_cls: 0.8408 2024/07/07 05:08:24 - mmengine - INFO - Saving checkpoint at 30000 iterations 2024/07/07 05:09:12 - mmengine - INFO - Iter(val) [ 50/105] eta: 0:00:44 time: 0.8036 data_time: 0.0300 memory: 6807 2024/07/07 05:09:52 - mmengine - INFO - Iter(val) [100/105] eta: 0:00:04 time: 0.8035 data_time: 0.0298 memory: 6806 2024/07/07 05:09:58 - mmengine - INFO - Evaluating bbox... 2024/07/07 05:10:25 - mmengine - INFO - bbox_mAP_copypaste: 0.375 0.538 0.401 0.172 0.413 0.543 2024/07/07 05:10:26 - mmengine - INFO - Iter(val) [105/105] coco/bbox_mAP: 0.3750 coco/bbox_mAP_50: 0.5380 coco/bbox_mAP_75: 0.4010 coco/bbox_mAP_s: 0.1720 coco/bbox_mAP_m: 0.4130 coco/bbox_mAP_l: 0.5430 data_time: 0.0279 time: 0.7925 2024/07/07 05:11:26 - mmengine - INFO - Iter(train) [ 30050/120000] base_lr: 1.7091e-04 lr: 1.7356e-05 eta: 1 day, 6:03:25 time: 1.2152 data_time: 0.0648 memory: 6805 grad_norm: 1.0356 loss: 0.8410 detection_loss_cls: 0.8410 2024/07/07 05:12:26 - mmengine - INFO - Iter(train) [ 30100/120000] base_lr: 1.7082e-04 lr: 1.7347e-05 eta: 1 day, 6:02:27 time: 1.2154 data_time: 0.0648 memory: 6241 grad_norm: 1.0349 loss: 0.8400 detection_loss_cls: 0.8400 2024/07/07 05:13:26 - mmengine - INFO - Iter(train) [ 30150/120000] base_lr: 1.7073e-04 lr: 1.7339e-05 eta: 1 day, 6:01:27 time: 1.2156 data_time: 0.0648 memory: 6241 grad_norm: 1.0352 loss: 0.8394 detection_loss_cls: 0.8394 2024/07/07 05:14:26 - mmengine - INFO - Iter(train) [ 30200/120000] base_lr: 1.7064e-04 lr: 1.7331e-05 eta: 1 day, 6:00:23 time: 1.2155 data_time: 0.0648 memory: 6241 grad_norm: 1.0347 loss: 0.8393 detection_loss_cls: 0.8393 2024/07/07 05:15:27 - mmengine - INFO - Iter(train) [ 30250/120000] base_lr: 1.7055e-04 lr: 1.7322e-05 eta: 1 day, 5:59:28 time: 1.2158 data_time: 0.0647 memory: 6241 grad_norm: 1.0341 loss: 0.8380 detection_loss_cls: 0.8380 2024/07/07 05:16:27 - mmengine - INFO - Iter(train) [ 30300/120000] base_lr: 1.7045e-04 lr: 1.7314e-05 eta: 1 day, 5:58:27 time: 1.2160 data_time: 0.0648 memory: 6241 grad_norm: 1.0344 loss: 0.8381 detection_loss_cls: 0.8381 2024/07/07 05:17:27 - mmengine - INFO - Iter(train) [ 30350/120000] base_lr: 1.7036e-04 lr: 1.7306e-05 eta: 1 day, 5:57:27 time: 1.2162 data_time: 0.0648 memory: 6241 grad_norm: 1.0347 loss: 0.8378 detection_loss_cls: 0.8378 2024/07/07 05:18:27 - mmengine - INFO - Iter(train) [ 30400/120000] base_lr: 1.7027e-04 lr: 1.7297e-05 eta: 1 day, 5:56:27 time: 1.2162 data_time: 0.0647 memory: 6241 grad_norm: 1.0346 loss: 0.8383 detection_loss_cls: 0.8383 2024/07/07 05:19:28 - mmengine - INFO - Iter(train) [ 30450/120000] base_lr: 1.7018e-04 lr: 1.7289e-05 eta: 1 day, 5:55:28 time: 1.2165 data_time: 0.0648 memory: 6241 grad_norm: 1.0344 loss: 0.8390 detection_loss_cls: 0.8390 2024/07/07 05:20:27 - mmengine - INFO - Iter(train) [ 30500/120000] base_lr: 1.7008e-04 lr: 1.7280e-05 eta: 1 day, 5:54:23 time: 1.2164 data_time: 0.0648 memory: 6241 grad_norm: 1.0343 loss: 0.8392 detection_loss_cls: 0.8392 2024/07/07 05:21:27 - mmengine - INFO - Iter(train) [ 30550/120000] base_lr: 1.6999e-04 lr: 1.7272e-05 eta: 1 day, 5:53:23 time: 1.2166 data_time: 0.0647 memory: 6241 grad_norm: 1.0340 loss: 0.8387 detection_loss_cls: 0.8387 2024/07/07 05:22:27 - mmengine - INFO - Iter(train) [ 30600/120000] base_lr: 1.6990e-04 lr: 1.7263e-05 eta: 1 day, 5:52:21 time: 1.2166 data_time: 0.0647 memory: 6241 grad_norm: 1.0336 loss: 0.8380 detection_loss_cls: 0.8380 2024/07/07 05:23:27 - mmengine - INFO - Iter(train) [ 30650/120000] base_lr: 1.6980e-04 lr: 1.7255e-05 eta: 1 day, 5:51:20 time: 1.2168 data_time: 0.0647 memory: 6241 grad_norm: 1.0335 loss: 0.8378 detection_loss_cls: 0.8378 2024/07/07 05:24:27 - mmengine - INFO - Iter(train) [ 30700/120000] base_lr: 1.6971e-04 lr: 1.7246e-05 eta: 1 day, 5:50:22 time: 1.2170 data_time: 0.0647 memory: 6241 grad_norm: 1.0325 loss: 0.8369 detection_loss_cls: 0.8369 2024/07/07 05:25:28 - mmengine - INFO - Iter(train) [ 30750/120000] base_lr: 1.6962e-04 lr: 1.7238e-05 eta: 1 day, 5:49:25 time: 1.2173 data_time: 0.0647 memory: 6241 grad_norm: 1.0328 loss: 0.8370 detection_loss_cls: 0.8370 2024/07/07 05:26:28 - mmengine - INFO - Iter(train) [ 30800/120000] base_lr: 1.6952e-04 lr: 1.7229e-05 eta: 1 day, 5:48:21 time: 1.2173 data_time: 0.0647 memory: 6241 grad_norm: 1.0327 loss: 0.8373 detection_loss_cls: 0.8373 2024/07/07 05:27:28 - mmengine - INFO - Iter(train) [ 30850/120000] base_lr: 1.6943e-04 lr: 1.7221e-05 eta: 1 day, 5:47:23 time: 1.2175 data_time: 0.0647 memory: 6241 grad_norm: 1.0328 loss: 0.8376 detection_loss_cls: 0.8376 2024/07/07 05:28:28 - mmengine - INFO - Iter(train) [ 30900/120000] base_lr: 1.6934e-04 lr: 1.7212e-05 eta: 1 day, 5:46:23 time: 1.2177 data_time: 0.0648 memory: 6241 grad_norm: 1.0326 loss: 0.8386 detection_loss_cls: 0.8386 2024/07/07 05:29:29 - mmengine - INFO - Iter(train) [ 30950/120000] base_lr: 1.6924e-04 lr: 1.7204e-05 eta: 1 day, 5:45:23 time: 1.2179 data_time: 0.0647 memory: 6241 grad_norm: 1.0323 loss: 0.8379 detection_loss_cls: 0.8379 2024/07/07 05:30:29 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 05:30:29 - mmengine - INFO - Iter(train) [ 31000/120000] base_lr: 1.6915e-04 lr: 1.7195e-05 eta: 1 day, 5:44:25 time: 1.2181 data_time: 0.0648 memory: 6241 grad_norm: 1.0325 loss: 0.8387 detection_loss_cls: 0.8387 2024/07/07 05:30:29 - mmengine - INFO - Saving checkpoint at 31000 iterations 2024/07/07 05:31:37 - mmengine - INFO - Iter(train) [ 31050/120000] base_lr: 1.6905e-04 lr: 1.7187e-05 eta: 1 day, 5:44:08 time: 1.2181 data_time: 0.0648 memory: 6241 grad_norm: 1.0325 loss: 0.8389 detection_loss_cls: 0.8389 2024/07/07 05:32:36 - mmengine - INFO - Iter(train) [ 31100/120000] base_lr: 1.6896e-04 lr: 1.7178e-05 eta: 1 day, 5:43:03 time: 1.2179 data_time: 0.0648 memory: 6241 grad_norm: 1.0323 loss: 0.8392 detection_loss_cls: 0.8392 2024/07/07 05:33:36 - mmengine - INFO - Iter(train) [ 31150/120000] base_lr: 1.6887e-04 lr: 1.7170e-05 eta: 1 day, 5:42:05 time: 1.2178 data_time: 0.0648 memory: 6241 grad_norm: 1.0315 loss: 0.8384 detection_loss_cls: 0.8384 2024/07/07 05:34:37 - mmengine - INFO - Iter(train) [ 31200/120000] base_lr: 1.6877e-04 lr: 1.7161e-05 eta: 1 day, 5:41:05 time: 1.2179 data_time: 0.0648 memory: 6241 grad_norm: 1.0314 loss: 0.8376 detection_loss_cls: 0.8376 2024/07/07 05:35:36 - mmengine - INFO - Iter(train) [ 31250/120000] base_lr: 1.6868e-04 lr: 1.7153e-05 eta: 1 day, 5:40:02 time: 1.2176 data_time: 0.0647 memory: 6241 grad_norm: 1.0320 loss: 0.8366 detection_loss_cls: 0.8366 2024/07/07 05:36:37 - mmengine - INFO - Iter(train) [ 31300/120000] base_lr: 1.6858e-04 lr: 1.7144e-05 eta: 1 day, 5:39:03 time: 1.2176 data_time: 0.0647 memory: 6241 grad_norm: 1.0317 loss: 0.8367 detection_loss_cls: 0.8367 2024/07/07 05:37:37 - mmengine - INFO - Iter(train) [ 31350/120000] base_lr: 1.6849e-04 lr: 1.7135e-05 eta: 1 day, 5:38:02 time: 1.2177 data_time: 0.0648 memory: 6241 grad_norm: 1.0325 loss: 0.8380 detection_loss_cls: 0.8380 2024/07/07 05:38:36 - mmengine - INFO - Iter(train) [ 31400/120000] base_lr: 1.6839e-04 lr: 1.7127e-05 eta: 1 day, 5:36:58 time: 1.2176 data_time: 0.0648 memory: 6241 grad_norm: 1.0323 loss: 0.8389 detection_loss_cls: 0.8389 2024/07/07 05:39:36 - mmengine - INFO - Iter(train) [ 31450/120000] base_lr: 1.6830e-04 lr: 1.7118e-05 eta: 1 day, 5:35:58 time: 1.2176 data_time: 0.0648 memory: 6241 grad_norm: 1.0325 loss: 0.8389 detection_loss_cls: 0.8389 2024/07/07 05:40:36 - mmengine - INFO - Iter(train) [ 31500/120000] base_lr: 1.6820e-04 lr: 1.7109e-05 eta: 1 day, 5:34:56 time: 1.2177 data_time: 0.0648 memory: 6241 grad_norm: 1.0328 loss: 0.8390 detection_loss_cls: 0.8390 2024/07/07 05:41:36 - mmengine - INFO - Iter(train) [ 31550/120000] base_lr: 1.6811e-04 lr: 1.7101e-05 eta: 1 day, 5:33:51 time: 1.2175 data_time: 0.0648 memory: 6241 grad_norm: 1.0324 loss: 0.8392 detection_loss_cls: 0.8392 2024/07/07 05:42:36 - mmengine - INFO - Iter(train) [ 31600/120000] base_lr: 1.6801e-04 lr: 1.7092e-05 eta: 1 day, 5:32:51 time: 1.2174 data_time: 0.0648 memory: 6241 grad_norm: 1.0319 loss: 0.8385 detection_loss_cls: 0.8385 2024/07/07 05:43:36 - mmengine - INFO - Iter(train) [ 31650/120000] base_lr: 1.6792e-04 lr: 1.7083e-05 eta: 1 day, 5:31:51 time: 1.2175 data_time: 0.0648 memory: 6241 grad_norm: 1.0312 loss: 0.8388 detection_loss_cls: 0.8388 2024/07/07 05:44:36 - mmengine - INFO - Iter(train) [ 31700/120000] base_lr: 1.6782e-04 lr: 1.7075e-05 eta: 1 day, 5:30:49 time: 1.2174 data_time: 0.0649 memory: 6241 grad_norm: 1.0311 loss: 0.8394 detection_loss_cls: 0.8394 2024/07/07 05:45:37 - mmengine - INFO - Iter(train) [ 31750/120000] base_lr: 1.6773e-04 lr: 1.7066e-05 eta: 1 day, 5:29:53 time: 1.2175 data_time: 0.0648 memory: 6241 grad_norm: 1.0315 loss: 0.8387 detection_loss_cls: 0.8387 2024/07/07 05:46:37 - mmengine - INFO - Iter(train) [ 31800/120000] base_lr: 1.6763e-04 lr: 1.7057e-05 eta: 1 day, 5:28:53 time: 1.2176 data_time: 0.0648 memory: 6241 grad_norm: 1.0308 loss: 0.8383 detection_loss_cls: 0.8383 2024/07/07 05:47:37 - mmengine - INFO - Iter(train) [ 31850/120000] base_lr: 1.6753e-04 lr: 1.7049e-05 eta: 1 day, 5:27:51 time: 1.2175 data_time: 0.0648 memory: 6241 grad_norm: 1.0304 loss: 0.8384 detection_loss_cls: 0.8384 2024/07/07 05:48:38 - mmengine - INFO - Iter(train) [ 31900/120000] base_lr: 1.6744e-04 lr: 1.7040e-05 eta: 1 day, 5:26:54 time: 1.2176 data_time: 0.0648 memory: 6241 grad_norm: 1.0302 loss: 0.8384 detection_loss_cls: 0.8384 2024/07/07 05:49:38 - mmengine - INFO - Iter(train) [ 31950/120000] base_lr: 1.6734e-04 lr: 1.7031e-05 eta: 1 day, 5:25:54 time: 1.2177 data_time: 0.0649 memory: 6241 grad_norm: 1.0304 loss: 0.8388 detection_loss_cls: 0.8388 2024/07/07 05:50:37 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 05:50:37 - mmengine - INFO - Iter(train) [ 32000/120000] base_lr: 1.6725e-04 lr: 1.7022e-05 eta: 1 day, 5:24:50 time: 1.2175 data_time: 0.0649 memory: 6241 grad_norm: 1.0305 loss: 0.8392 detection_loss_cls: 0.8392 2024/07/07 05:50:37 - mmengine - INFO - Saving checkpoint at 32000 iterations 2024/07/07 05:51:45 - mmengine - INFO - Iter(train) [ 32050/120000] base_lr: 1.6715e-04 lr: 1.7014e-05 eta: 1 day, 5:24:34 time: 1.2175 data_time: 0.0651 memory: 6241 grad_norm: 1.0304 loss: 0.8401 detection_loss_cls: 0.8401 2024/07/07 05:52:46 - mmengine - INFO - Iter(train) [ 32100/120000] base_lr: 1.6705e-04 lr: 1.7005e-05 eta: 1 day, 5:23:32 time: 1.2174 data_time: 0.0651 memory: 6241 grad_norm: 1.0315 loss: 0.8395 detection_loss_cls: 0.8395 2024/07/07 05:53:46 - 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mmengine - INFO - Iter(train) [ 32900/120000] base_lr: 1.6549e-04 lr: 1.6863e-05 eta: 1 day, 5:07:20 time: 1.2166 data_time: 0.0649 memory: 6241 grad_norm: 1.0307 loss: 0.8324 detection_loss_cls: 0.8324 2024/07/07 06:09:47 - mmengine - INFO - Iter(train) [ 32950/120000] base_lr: 1.6540e-04 lr: 1.6854e-05 eta: 1 day, 5:06:22 time: 1.2167 data_time: 0.0649 memory: 6241 grad_norm: 1.0305 loss: 0.8319 detection_loss_cls: 0.8319 2024/07/07 06:10:47 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 06:10:47 - mmengine - INFO - Iter(train) [ 33000/120000] base_lr: 1.6530e-04 lr: 1.6845e-05 eta: 1 day, 5:05:20 time: 1.2167 data_time: 0.0649 memory: 6241 grad_norm: 1.0323 loss: 0.8319 detection_loss_cls: 0.8319 2024/07/07 06:10:47 - mmengine - INFO - Saving checkpoint at 33000 iterations 2024/07/07 06:11:55 - mmengine - INFO - Iter(train) [ 33050/120000] base_lr: 1.6520e-04 lr: 1.6836e-05 eta: 1 day, 5:05:00 time: 1.2166 data_time: 0.0650 memory: 6241 grad_norm: 1.0323 loss: 0.8313 detection_loss_cls: 0.8313 2024/07/07 06:12:55 - 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mmengine - INFO - Iter(train) [ 33350/120000] base_lr: 1.6460e-04 lr: 1.6782e-05 eta: 1 day, 4:58:55 time: 1.2162 data_time: 0.0650 memory: 6241 grad_norm: 1.0331 loss: 0.8297 detection_loss_cls: 0.8297 2024/07/07 06:18:56 - mmengine - INFO - Iter(train) [ 33400/120000] base_lr: 1.6451e-04 lr: 1.6773e-05 eta: 1 day, 4:57:55 time: 1.2162 data_time: 0.0650 memory: 6241 grad_norm: 1.0331 loss: 0.8300 detection_loss_cls: 0.8300 2024/07/07 06:19:57 - mmengine - INFO - Iter(train) [ 33450/120000] base_lr: 1.6441e-04 lr: 1.6764e-05 eta: 1 day, 4:56:56 time: 1.2162 data_time: 0.0651 memory: 6241 grad_norm: 1.0332 loss: 0.8305 detection_loss_cls: 0.8305 2024/07/07 06:20:57 - mmengine - INFO - Iter(train) [ 33500/120000] base_lr: 1.6431e-04 lr: 1.6755e-05 eta: 1 day, 4:55:54 time: 1.2162 data_time: 0.0650 memory: 6241 grad_norm: 1.0333 loss: 0.8296 detection_loss_cls: 0.8296 2024/07/07 06:21:57 - mmengine - INFO - Iter(train) [ 33550/120000] base_lr: 1.6421e-04 lr: 1.6746e-05 eta: 1 day, 4:54:53 time: 1.2160 data_time: 0.0650 memory: 6241 grad_norm: 1.0329 loss: 0.8292 detection_loss_cls: 0.8292 2024/07/07 06:22:57 - mmengine - INFO - Iter(train) [ 33600/120000] base_lr: 1.6411e-04 lr: 1.6737e-05 eta: 1 day, 4:53:52 time: 1.2159 data_time: 0.0650 memory: 6241 grad_norm: 1.0330 loss: 0.8297 detection_loss_cls: 0.8297 2024/07/07 06:23:57 - mmengine - INFO - Iter(train) [ 33650/120000] base_lr: 1.6401e-04 lr: 1.6728e-05 eta: 1 day, 4:52:51 time: 1.2158 data_time: 0.0651 memory: 6241 grad_norm: 1.0332 loss: 0.8299 detection_loss_cls: 0.8299 2024/07/07 06:24:57 - mmengine - INFO - Iter(train) [ 33700/120000] base_lr: 1.6391e-04 lr: 1.6719e-05 eta: 1 day, 4:51:53 time: 1.2159 data_time: 0.0651 memory: 6241 grad_norm: 1.0323 loss: 0.8309 detection_loss_cls: 0.8309 2024/07/07 06:25:58 - mmengine - INFO - Iter(train) [ 33750/120000] base_lr: 1.6381e-04 lr: 1.6710e-05 eta: 1 day, 4:50:57 time: 1.2160 data_time: 0.0652 memory: 6241 grad_norm: 1.0314 loss: 0.8319 detection_loss_cls: 0.8319 2024/07/07 06:26:59 - mmengine - INFO - Iter(train) [ 33800/120000] base_lr: 1.6371e-04 lr: 1.6701e-05 eta: 1 day, 4:49:57 time: 1.2160 data_time: 0.0652 memory: 6241 grad_norm: 1.0318 loss: 0.8316 detection_loss_cls: 0.8316 2024/07/07 06:27:59 - mmengine - INFO - Iter(train) [ 33850/120000] base_lr: 1.6361e-04 lr: 1.6691e-05 eta: 1 day, 4:48:59 time: 1.2161 data_time: 0.0652 memory: 6241 grad_norm: 1.0318 loss: 0.8310 detection_loss_cls: 0.8310 2024/07/07 06:29:00 - mmengine - INFO - Iter(train) [ 33900/120000] base_lr: 1.6351e-04 lr: 1.6682e-05 eta: 1 day, 4:48:01 time: 1.2161 data_time: 0.0653 memory: 6241 grad_norm: 1.0319 loss: 0.8315 detection_loss_cls: 0.8315 2024/07/07 06:30:00 - mmengine - INFO - Iter(train) [ 33950/120000] base_lr: 1.6341e-04 lr: 1.6673e-05 eta: 1 day, 4:47:01 time: 1.2161 data_time: 0.0652 memory: 6241 grad_norm: 1.0317 loss: 0.8299 detection_loss_cls: 0.8299 2024/07/07 06:31:00 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 06:31:00 - mmengine - INFO - Iter(train) [ 34000/120000] base_lr: 1.6330e-04 lr: 1.6664e-05 eta: 1 day, 4:45:59 time: 1.2158 data_time: 0.0652 memory: 6241 grad_norm: 1.0313 loss: 0.8302 detection_loss_cls: 0.8302 2024/07/07 06:31:00 - mmengine - INFO - Saving checkpoint at 34000 iterations 2024/07/07 06:32:07 - mmengine - INFO - Iter(train) [ 34050/120000] base_lr: 1.6320e-04 lr: 1.6655e-05 eta: 1 day, 4:45:32 time: 1.2102 data_time: 0.0597 memory: 6241 grad_norm: 1.0309 loss: 0.8308 detection_loss_cls: 0.8308 2024/07/07 06:33:06 - mmengine - INFO - Iter(train) [ 34100/120000] base_lr: 1.6310e-04 lr: 1.6646e-05 eta: 1 day, 4:44:26 time: 1.2099 data_time: 0.0597 memory: 6241 grad_norm: 1.0320 loss: 0.8298 detection_loss_cls: 0.8298 2024/07/07 06:34:06 - mmengine - INFO - Iter(train) [ 34150/120000] base_lr: 1.6300e-04 lr: 1.6637e-05 eta: 1 day, 4:43:21 time: 1.2096 data_time: 0.0597 memory: 6241 grad_norm: 1.0319 loss: 0.8294 detection_loss_cls: 0.8294 2024/07/07 06:35:05 - mmengine - INFO - Iter(train) [ 34200/120000] base_lr: 1.6290e-04 lr: 1.6627e-05 eta: 1 day, 4:42:18 time: 1.2097 data_time: 0.0597 memory: 6241 grad_norm: 1.0315 loss: 0.8297 detection_loss_cls: 0.8297 2024/07/07 06:36:04 - mmengine - INFO - Iter(train) [ 34250/120000] base_lr: 1.6280e-04 lr: 1.6618e-05 eta: 1 day, 4:41:13 time: 1.2092 data_time: 0.0598 memory: 6241 grad_norm: 1.0313 loss: 0.8298 detection_loss_cls: 0.8298 2024/07/07 06:37:04 - mmengine - INFO - Iter(train) [ 34300/120000] base_lr: 1.6270e-04 lr: 1.6609e-05 eta: 1 day, 4:40:09 time: 1.2091 data_time: 0.0598 memory: 6241 grad_norm: 1.0304 loss: 0.8298 detection_loss_cls: 0.8298 2024/07/07 06:38:04 - mmengine - INFO - Iter(train) [ 34350/120000] base_lr: 1.6260e-04 lr: 1.6600e-05 eta: 1 day, 4:39:07 time: 1.2090 data_time: 0.0598 memory: 6241 grad_norm: 1.0299 loss: 0.8306 detection_loss_cls: 0.8306 2024/07/07 06:39:03 - mmengine - INFO - Iter(train) [ 34400/120000] base_lr: 1.6250e-04 lr: 1.6591e-05 eta: 1 day, 4:38:02 time: 1.2087 data_time: 0.0599 memory: 6241 grad_norm: 1.0299 loss: 0.8307 detection_loss_cls: 0.8307 2024/07/07 06:40:02 - mmengine - INFO - Iter(train) [ 34450/120000] base_lr: 1.6239e-04 lr: 1.6581e-05 eta: 1 day, 4:36:57 time: 1.2085 data_time: 0.0599 memory: 6241 grad_norm: 1.0299 loss: 0.8302 detection_loss_cls: 0.8302 2024/07/07 06:41:01 - mmengine - INFO - Iter(train) [ 34500/120000] base_lr: 1.6229e-04 lr: 1.6572e-05 eta: 1 day, 4:35:51 time: 1.2083 data_time: 0.0598 memory: 6241 grad_norm: 1.0295 loss: 0.8288 detection_loss_cls: 0.8288 2024/07/07 06:42:00 - mmengine - INFO - Iter(train) [ 34550/120000] base_lr: 1.6219e-04 lr: 1.6563e-05 eta: 1 day, 4:34:47 time: 1.2081 data_time: 0.0599 memory: 6241 grad_norm: 1.0294 loss: 0.8290 detection_loss_cls: 0.8290 2024/07/07 06:43:00 - mmengine - INFO - Iter(train) [ 34600/120000] base_lr: 1.6209e-04 lr: 1.6554e-05 eta: 1 day, 4:33:44 time: 1.2081 data_time: 0.0599 memory: 6241 grad_norm: 1.0297 loss: 0.8298 detection_loss_cls: 0.8298 2024/07/07 06:44:00 - mmengine - INFO - Iter(train) [ 34650/120000] base_lr: 1.6199e-04 lr: 1.6544e-05 eta: 1 day, 4:32:40 time: 1.2079 data_time: 0.0599 memory: 6241 grad_norm: 1.0321 loss: 0.8295 detection_loss_cls: 0.8295 2024/07/07 06:44:59 - mmengine - INFO - Iter(train) [ 34700/120000] base_lr: 1.6188e-04 lr: 1.6535e-05 eta: 1 day, 4:31:35 time: 1.2076 data_time: 0.0600 memory: 6241 grad_norm: 1.0323 loss: 0.8314 detection_loss_cls: 0.8314 2024/07/07 06:45:58 - mmengine - INFO - Iter(train) [ 34750/120000] base_lr: 1.6178e-04 lr: 1.6526e-05 eta: 1 day, 4:30:32 time: 1.2073 data_time: 0.0601 memory: 6241 grad_norm: 1.0324 loss: 0.8309 detection_loss_cls: 0.8309 2024/07/07 06:46:58 - mmengine - INFO - Iter(train) [ 34800/120000] base_lr: 1.6168e-04 lr: 1.6516e-05 eta: 1 day, 4:29:28 time: 1.2073 data_time: 0.0601 memory: 6241 grad_norm: 1.0320 loss: 0.8306 detection_loss_cls: 0.8306 2024/07/07 06:47:57 - mmengine - INFO - Iter(train) [ 34850/120000] base_lr: 1.6158e-04 lr: 1.6507e-05 eta: 1 day, 4:28:25 time: 1.2070 data_time: 0.0601 memory: 6241 grad_norm: 1.0322 loss: 0.8302 detection_loss_cls: 0.8302 2024/07/07 06:48:56 - mmengine - INFO - Iter(train) [ 34900/120000] base_lr: 1.6147e-04 lr: 1.6498e-05 eta: 1 day, 4:27:21 time: 1.2068 data_time: 0.0600 memory: 6241 grad_norm: 1.0325 loss: 0.8292 detection_loss_cls: 0.8292 2024/07/07 06:49:57 - mmengine - INFO - Iter(train) [ 34950/120000] base_lr: 1.6137e-04 lr: 1.6488e-05 eta: 1 day, 4:26:20 time: 1.2068 data_time: 0.0601 memory: 6241 grad_norm: 1.0335 loss: 0.8300 detection_loss_cls: 0.8300 2024/07/07 06:50:56 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 06:50:56 - mmengine - INFO - Iter(train) [ 35000/120000] base_lr: 1.6127e-04 lr: 1.6479e-05 eta: 1 day, 4:25:15 time: 1.2065 data_time: 0.0601 memory: 6241 grad_norm: 1.0338 loss: 0.8299 detection_loss_cls: 0.8299 2024/07/07 06:50:56 - mmengine - INFO - Saving checkpoint at 35000 iterations 2024/07/07 06:51:44 - mmengine - INFO - Iter(val) [ 50/105] eta: 0:00:44 time: 0.8030 data_time: 0.0299 memory: 6809 2024/07/07 06:52:24 - mmengine - INFO - Iter(val) [100/105] eta: 0:00:04 time: 0.8030 data_time: 0.0298 memory: 6808 2024/07/07 06:52:30 - mmengine - INFO - Evaluating bbox... 2024/07/07 06:52:58 - mmengine - INFO - bbox_mAP_copypaste: 0.384 0.546 0.411 0.178 0.422 0.554 2024/07/07 06:52:59 - mmengine - INFO - Iter(val) [105/105] coco/bbox_mAP: 0.3840 coco/bbox_mAP_50: 0.5460 coco/bbox_mAP_75: 0.4110 coco/bbox_mAP_s: 0.1780 coco/bbox_mAP_m: 0.4220 coco/bbox_mAP_l: 0.5540 data_time: 0.0293 time: 0.7974 2024/07/07 06:53:58 - mmengine - INFO - Iter(train) [ 35050/120000] base_lr: 1.6117e-04 lr: 1.6470e-05 eta: 1 day, 4:26:29 time: 1.2122 data_time: 0.0660 memory: 6804 grad_norm: 1.0343 loss: 0.8300 detection_loss_cls: 0.8300 2024/07/07 06:54:59 - mmengine - INFO - Iter(train) [ 35100/120000] base_lr: 1.6106e-04 lr: 1.6460e-05 eta: 1 day, 4:25:31 time: 1.2125 data_time: 0.0660 memory: 6237 grad_norm: 1.0345 loss: 0.8301 detection_loss_cls: 0.8301 2024/07/07 06:56:00 - mmengine - INFO - Iter(train) [ 35150/120000] base_lr: 1.6096e-04 lr: 1.6451e-05 eta: 1 day, 4:24:32 time: 1.2125 data_time: 0.0660 memory: 6237 grad_norm: 1.0353 loss: 0.8303 detection_loss_cls: 0.8303 2024/07/07 06:57:01 - mmengine - INFO - Iter(train) [ 35200/120000] base_lr: 1.6086e-04 lr: 1.6442e-05 eta: 1 day, 4:23:38 time: 1.2129 data_time: 0.0660 memory: 6237 grad_norm: 1.0355 loss: 0.8297 detection_loss_cls: 0.8297 2024/07/07 06:58:02 - mmengine - INFO - Iter(train) [ 35250/120000] base_lr: 1.6075e-04 lr: 1.6432e-05 eta: 1 day, 4:22:40 time: 1.2131 data_time: 0.0661 memory: 6237 grad_norm: 1.0342 loss: 0.8305 detection_loss_cls: 0.8305 2024/07/07 06:59:02 - mmengine - INFO - Iter(train) [ 35300/120000] base_lr: 1.6065e-04 lr: 1.6423e-05 eta: 1 day, 4:21:39 time: 1.2131 data_time: 0.0661 memory: 6237 grad_norm: 1.0347 loss: 0.8304 detection_loss_cls: 0.8304 2024/07/07 07:00:04 - mmengine - INFO - Iter(train) [ 35350/120000] base_lr: 1.6055e-04 lr: 1.6413e-05 eta: 1 day, 4:20:47 time: 1.2136 data_time: 0.0660 memory: 6237 grad_norm: 1.0335 loss: 0.8281 detection_loss_cls: 0.8281 2024/07/07 07:01:05 - mmengine - INFO - Iter(train) [ 35400/120000] base_lr: 1.6044e-04 lr: 1.6404e-05 eta: 1 day, 4:19:48 time: 1.2139 data_time: 0.0661 memory: 6237 grad_norm: 1.0328 loss: 0.8279 detection_loss_cls: 0.8279 2024/07/07 07:02:05 - mmengine - INFO - Iter(train) [ 35450/120000] base_lr: 1.6034e-04 lr: 1.6395e-05 eta: 1 day, 4:18:48 time: 1.2139 data_time: 0.0661 memory: 6237 grad_norm: 1.0328 loss: 0.8275 detection_loss_cls: 0.8275 2024/07/07 07:03:06 - mmengine - INFO - Iter(train) [ 35500/120000] base_lr: 1.6024e-04 lr: 1.6385e-05 eta: 1 day, 4:17:52 time: 1.2143 data_time: 0.0661 memory: 6237 grad_norm: 1.0320 loss: 0.8275 detection_loss_cls: 0.8275 2024/07/07 07:04:07 - mmengine - INFO - Iter(train) [ 35550/120000] base_lr: 1.6013e-04 lr: 1.6376e-05 eta: 1 day, 4:16:52 time: 1.2145 data_time: 0.0660 memory: 6237 grad_norm: 1.0322 loss: 0.8263 detection_loss_cls: 0.8263 2024/07/07 07:05:07 - 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mmengine - INFO - Saving checkpoint at 36000 iterations 2024/07/07 07:14:23 - mmengine - INFO - Iter(train) [ 36050/120000] base_lr: 1.5909e-04 lr: 1.6281e-05 eta: 1 day, 4:07:44 time: 1.2161 data_time: 0.0662 memory: 6237 grad_norm: 1.0329 loss: 0.8237 detection_loss_cls: 0.8237 2024/07/07 07:15:24 - mmengine - INFO - Iter(train) [ 36100/120000] base_lr: 1.5898e-04 lr: 1.6271e-05 eta: 1 day, 4:06:46 time: 1.2163 data_time: 0.0662 memory: 6237 grad_norm: 1.0314 loss: 0.8234 detection_loss_cls: 0.8234 2024/07/07 07:16:25 - mmengine - INFO - Iter(train) [ 36150/120000] base_lr: 1.5888e-04 lr: 1.6262e-05 eta: 1 day, 4:05:49 time: 1.2164 data_time: 0.0661 memory: 6237 grad_norm: 1.0317 loss: 0.8231 detection_loss_cls: 0.8231 2024/07/07 07:17:25 - mmengine - INFO - Iter(train) [ 36200/120000] base_lr: 1.5877e-04 lr: 1.6252e-05 eta: 1 day, 4:04:50 time: 1.2166 data_time: 0.0662 memory: 6237 grad_norm: 1.0318 loss: 0.8232 detection_loss_cls: 0.8232 2024/07/07 07:18:26 - mmengine - INFO - Iter(train) [ 36250/120000] base_lr: 1.5867e-04 lr: 1.6242e-05 eta: 1 day, 4:03:52 time: 1.2168 data_time: 0.0661 memory: 6237 grad_norm: 1.0325 loss: 0.8226 detection_loss_cls: 0.8226 2024/07/07 07:19:27 - 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mmengine - INFO - Iter(train) [ 37000/120000] base_lr: 1.5708e-04 lr: 1.6098e-05 eta: 1 day, 3:49:22 time: 1.2199 data_time: 0.0666 memory: 6237 grad_norm: 1.0275 loss: 0.8237 detection_loss_cls: 0.8237 2024/07/07 07:33:40 - mmengine - INFO - Saving checkpoint at 37000 iterations 2024/07/07 07:34:49 - mmengine - INFO - Iter(train) [ 37050/120000] base_lr: 1.5697e-04 lr: 1.6088e-05 eta: 1 day, 3:48:53 time: 1.2200 data_time: 0.0666 memory: 6237 grad_norm: 1.0281 loss: 0.8234 detection_loss_cls: 0.8234 2024/07/07 07:35:49 - mmengine - INFO - Iter(train) [ 37100/120000] base_lr: 1.5686e-04 lr: 1.6078e-05 eta: 1 day, 3:47:52 time: 1.2201 data_time: 0.0667 memory: 6237 grad_norm: 1.0281 loss: 0.8229 detection_loss_cls: 0.8229 2024/07/07 07:36:50 - mmengine - INFO - Iter(train) [ 37150/120000] base_lr: 1.5676e-04 lr: 1.6069e-05 eta: 1 day, 3:46:56 time: 1.2205 data_time: 0.0668 memory: 6237 grad_norm: 1.0279 loss: 0.8244 detection_loss_cls: 0.8244 2024/07/07 07:37:51 - mmengine - INFO - Iter(train) [ 37200/120000] base_lr: 1.5665e-04 lr: 1.6059e-05 eta: 1 day, 3:45:56 time: 1.2205 data_time: 0.0667 memory: 6237 grad_norm: 1.0278 loss: 0.8236 detection_loss_cls: 0.8236 2024/07/07 07:38:51 - 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mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 07:54:05 - mmengine - INFO - Iter(train) [ 38000/120000] base_lr: 1.5492e-04 lr: 1.5902e-05 eta: 1 day, 3:30:21 time: 1.2229 data_time: 0.0670 memory: 6237 grad_norm: 1.0266 loss: 0.8190 detection_loss_cls: 0.8190 2024/07/07 07:54:05 - mmengine - INFO - Saving checkpoint at 38000 iterations 2024/07/07 07:55:14 - mmengine - INFO - Iter(train) [ 38050/120000] base_lr: 1.5481e-04 lr: 1.5892e-05 eta: 1 day, 3:29:52 time: 1.2233 data_time: 0.0669 memory: 6237 grad_norm: 1.0265 loss: 0.8179 detection_loss_cls: 0.8179 2024/07/07 07:56:15 - mmengine - INFO - Iter(train) [ 38100/120000] base_lr: 1.5470e-04 lr: 1.5882e-05 eta: 1 day, 3:28:54 time: 1.2238 data_time: 0.0670 memory: 6237 grad_norm: 1.0257 loss: 0.8188 detection_loss_cls: 0.8188 2024/07/07 07:57:15 - mmengine - INFO - Iter(train) [ 38150/120000] base_lr: 1.5459e-04 lr: 1.5872e-05 eta: 1 day, 3:27:54 time: 1.2241 data_time: 0.0670 memory: 6237 grad_norm: 1.0260 loss: 0.8186 detection_loss_cls: 0.8186 2024/07/07 07:58:16 - 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mmengine - INFO - Iter(train) [ 38950/120000] base_lr: 1.5284e-04 lr: 1.5713e-05 eta: 1 day, 3:12:03 time: 1.2291 data_time: 0.0669 memory: 6237 grad_norm: 1.0263 loss: 0.8144 detection_loss_cls: 0.8144 2024/07/07 08:14:26 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 08:14:26 - mmengine - INFO - Iter(train) [ 39000/120000] base_lr: 1.5273e-04 lr: 1.5703e-05 eta: 1 day, 3:11:02 time: 1.2293 data_time: 0.0669 memory: 6237 grad_norm: 1.0258 loss: 0.8134 detection_loss_cls: 0.8134 2024/07/07 08:14:26 - mmengine - INFO - Saving checkpoint at 39000 iterations 2024/07/07 08:15:35 - mmengine - INFO - Iter(train) [ 39050/120000] base_lr: 1.5262e-04 lr: 1.5693e-05 eta: 1 day, 3:10:32 time: 1.2240 data_time: 0.0612 memory: 6237 grad_norm: 1.0250 loss: 0.8139 detection_loss_cls: 0.8139 2024/07/07 08:16:36 - mmengine - INFO - Iter(train) [ 39100/120000] base_lr: 1.5251e-04 lr: 1.5683e-05 eta: 1 day, 3:09:33 time: 1.2241 data_time: 0.0612 memory: 6237 grad_norm: 1.0249 loss: 0.8137 detection_loss_cls: 0.8137 2024/07/07 08:17:37 - 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mmengine - INFO - Iter(train) [ 39400/120000] base_lr: 1.5184e-04 lr: 1.5622e-05 eta: 1 day, 3:03:38 time: 1.2238 data_time: 0.0611 memory: 6237 grad_norm: 1.0272 loss: 0.8117 detection_loss_cls: 0.8117 2024/07/07 08:23:42 - mmengine - INFO - Iter(train) [ 39450/120000] base_lr: 1.5173e-04 lr: 1.5612e-05 eta: 1 day, 3:02:37 time: 1.2238 data_time: 0.0612 memory: 6237 grad_norm: 1.0277 loss: 0.8121 detection_loss_cls: 0.8121 2024/07/07 08:24:43 - mmengine - INFO - Iter(train) [ 39500/120000] base_lr: 1.5162e-04 lr: 1.5602e-05 eta: 1 day, 3:01:40 time: 1.2239 data_time: 0.0613 memory: 6237 grad_norm: 1.0280 loss: 0.8132 detection_loss_cls: 0.8132 2024/07/07 08:25:44 - mmengine - INFO - Iter(train) [ 39550/120000] base_lr: 1.5151e-04 lr: 1.5592e-05 eta: 1 day, 3:00:42 time: 1.2241 data_time: 0.0613 memory: 6237 grad_norm: 1.0281 loss: 0.8136 detection_loss_cls: 0.8136 2024/07/07 08:26:45 - mmengine - INFO - Iter(train) [ 39600/120000] base_lr: 1.5140e-04 lr: 1.5582e-05 eta: 1 day, 2:59:42 time: 1.2241 data_time: 0.0613 memory: 6237 grad_norm: 1.0280 loss: 0.8139 detection_loss_cls: 0.8139 2024/07/07 08:27:45 - mmengine - INFO - Iter(train) [ 39650/120000] base_lr: 1.5129e-04 lr: 1.5571e-05 eta: 1 day, 2:58:41 time: 1.2238 data_time: 0.0613 memory: 6237 grad_norm: 1.0279 loss: 0.8138 detection_loss_cls: 0.8138 2024/07/07 08:28:46 - mmengine - INFO - Iter(train) [ 39700/120000] base_lr: 1.5117e-04 lr: 1.5561e-05 eta: 1 day, 2:57:44 time: 1.2240 data_time: 0.0613 memory: 6237 grad_norm: 1.0272 loss: 0.8135 detection_loss_cls: 0.8135 2024/07/07 08:29:47 - mmengine - INFO - Iter(train) [ 39750/120000] base_lr: 1.5106e-04 lr: 1.5551e-05 eta: 1 day, 2:56:44 time: 1.2240 data_time: 0.0613 memory: 6237 grad_norm: 1.0269 loss: 0.8139 detection_loss_cls: 0.8139 2024/07/07 08:30:48 - mmengine - INFO - Iter(train) [ 39800/120000] base_lr: 1.5095e-04 lr: 1.5541e-05 eta: 1 day, 2:55:45 time: 1.2239 data_time: 0.0613 memory: 6237 grad_norm: 1.0264 loss: 0.8141 detection_loss_cls: 0.8141 2024/07/07 08:31:49 - mmengine - INFO - Iter(train) [ 39850/120000] base_lr: 1.5084e-04 lr: 1.5531e-05 eta: 1 day, 2:54:47 time: 1.2241 data_time: 0.0613 memory: 6237 grad_norm: 1.0272 loss: 0.8135 detection_loss_cls: 0.8135 2024/07/07 08:32:50 - mmengine - INFO - Iter(train) [ 39900/120000] base_lr: 1.5073e-04 lr: 1.5521e-05 eta: 1 day, 2:53:47 time: 1.2240 data_time: 0.0613 memory: 6237 grad_norm: 1.0271 loss: 0.8138 detection_loss_cls: 0.8138 2024/07/07 08:33:51 - mmengine - INFO - Iter(train) [ 39950/120000] base_lr: 1.5061e-04 lr: 1.5510e-05 eta: 1 day, 2:52:48 time: 1.2239 data_time: 0.0613 memory: 6237 grad_norm: 1.0278 loss: 0.8144 detection_loss_cls: 0.8144 2024/07/07 08:34:52 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 08:34:52 - mmengine - INFO - Iter(train) [ 40000/120000] base_lr: 1.5050e-04 lr: 1.5500e-05 eta: 1 day, 2:51:50 time: 1.2240 data_time: 0.0613 memory: 6237 grad_norm: 1.0281 loss: 0.8133 detection_loss_cls: 0.8133 2024/07/07 08:34:52 - mmengine - INFO - Saving checkpoint at 40000 iterations 2024/07/07 08:35:40 - mmengine - INFO - Iter(val) [ 50/105] eta: 0:00:44 time: 0.8031 data_time: 0.0298 memory: 6808 2024/07/07 08:36:20 - mmengine - INFO - Iter(val) [100/105] eta: 0:00:04 time: 0.8032 data_time: 0.0297 memory: 6807 2024/07/07 08:36:26 - mmengine - INFO - Evaluating bbox... 2024/07/07 08:36:53 - mmengine - INFO - bbox_mAP_copypaste: 0.392 0.557 0.420 0.183 0.432 0.564 2024/07/07 08:36:53 - mmengine - INFO - Iter(val) [105/105] coco/bbox_mAP: 0.3920 coco/bbox_mAP_50: 0.5570 coco/bbox_mAP_75: 0.4200 coco/bbox_mAP_s: 0.1830 coco/bbox_mAP_m: 0.4320 coco/bbox_mAP_l: 0.5640 data_time: 0.0284 time: 0.7984 2024/07/07 08:37:54 - mmengine - INFO - Iter(train) [ 40050/120000] base_lr: 1.5039e-04 lr: 1.5490e-05 eta: 1 day, 2:52:27 time: 1.2294 data_time: 0.0668 memory: 6806 grad_norm: 1.0283 loss: 0.8140 detection_loss_cls: 0.8140 2024/07/07 08:38:55 - mmengine - INFO - Iter(train) [ 40100/120000] base_lr: 1.5028e-04 lr: 1.5480e-05 eta: 1 day, 2:51:28 time: 1.2294 data_time: 0.0668 memory: 6241 grad_norm: 1.0286 loss: 0.8149 detection_loss_cls: 0.8149 2024/07/07 08:39:57 - mmengine - INFO - Iter(train) [ 40150/120000] base_lr: 1.5017e-04 lr: 1.5470e-05 eta: 1 day, 2:50:33 time: 1.2297 data_time: 0.0668 memory: 6241 grad_norm: 1.0284 loss: 0.8148 detection_loss_cls: 0.8148 2024/07/07 08:40:57 - mmengine - INFO - Iter(train) [ 40200/120000] base_lr: 1.5005e-04 lr: 1.5459e-05 eta: 1 day, 2:49:32 time: 1.2297 data_time: 0.0669 memory: 6241 grad_norm: 1.0279 loss: 0.8153 detection_loss_cls: 0.8153 2024/07/07 08:41:58 - mmengine - INFO - Iter(train) [ 40250/120000] base_lr: 1.4994e-04 lr: 1.5449e-05 eta: 1 day, 2:48:34 time: 1.2297 data_time: 0.0669 memory: 6241 grad_norm: 1.0275 loss: 0.8150 detection_loss_cls: 0.8150 2024/07/07 08:43:00 - mmengine - INFO - Iter(train) [ 40300/120000] base_lr: 1.4983e-04 lr: 1.5439e-05 eta: 1 day, 2:47:38 time: 1.2299 data_time: 0.0669 memory: 6241 grad_norm: 1.0271 loss: 0.8149 detection_loss_cls: 0.8149 2024/07/07 08:44:02 - mmengine - INFO - Iter(train) [ 40350/120000] base_lr: 1.4971e-04 lr: 1.5429e-05 eta: 1 day, 2:46:40 time: 1.2301 data_time: 0.0669 memory: 6241 grad_norm: 1.0268 loss: 0.8147 detection_loss_cls: 0.8147 2024/07/07 08:45:03 - mmengine - INFO - Iter(train) [ 40400/120000] base_lr: 1.4960e-04 lr: 1.5418e-05 eta: 1 day, 2:45:41 time: 1.2300 data_time: 0.0669 memory: 6241 grad_norm: 1.0268 loss: 0.8149 detection_loss_cls: 0.8149 2024/07/07 08:46:04 - mmengine - INFO - Iter(train) [ 40450/120000] base_lr: 1.4949e-04 lr: 1.5408e-05 eta: 1 day, 2:44:43 time: 1.2302 data_time: 0.0668 memory: 6241 grad_norm: 1.0271 loss: 0.8138 detection_loss_cls: 0.8138 2024/07/07 08:47:04 - mmengine - INFO - Iter(train) [ 40500/120000] base_lr: 1.4938e-04 lr: 1.5398e-05 eta: 1 day, 2:43:43 time: 1.2301 data_time: 0.0668 memory: 6241 grad_norm: 1.0273 loss: 0.8122 detection_loss_cls: 0.8122 2024/07/07 08:48:05 - mmengine - INFO - Iter(train) [ 40550/120000] base_lr: 1.4926e-04 lr: 1.5388e-05 eta: 1 day, 2:42:43 time: 1.2300 data_time: 0.0668 memory: 6241 grad_norm: 1.0273 loss: 0.8129 detection_loss_cls: 0.8129 2024/07/07 08:49:06 - mmengine - INFO - Iter(train) [ 40600/120000] base_lr: 1.4915e-04 lr: 1.5377e-05 eta: 1 day, 2:41:43 time: 1.2301 data_time: 0.0668 memory: 6241 grad_norm: 1.0276 loss: 0.8131 detection_loss_cls: 0.8131 2024/07/07 08:50:07 - mmengine - INFO - Iter(train) [ 40650/120000] base_lr: 1.4904e-04 lr: 1.5367e-05 eta: 1 day, 2:40:43 time: 1.2301 data_time: 0.0668 memory: 6241 grad_norm: 1.0276 loss: 0.8130 detection_loss_cls: 0.8130 2024/07/07 08:51:08 - mmengine - INFO - Iter(train) [ 40700/120000] base_lr: 1.4892e-04 lr: 1.5357e-05 eta: 1 day, 2:39:44 time: 1.2300 data_time: 0.0668 memory: 6241 grad_norm: 1.0279 loss: 0.8130 detection_loss_cls: 0.8130 2024/07/07 08:52:09 - mmengine - INFO - Iter(train) [ 40750/120000] base_lr: 1.4881e-04 lr: 1.5346e-05 eta: 1 day, 2:38:46 time: 1.2302 data_time: 0.0668 memory: 6241 grad_norm: 1.0281 loss: 0.8133 detection_loss_cls: 0.8133 2024/07/07 08:53:10 - mmengine - INFO - Iter(train) [ 40800/120000] base_lr: 1.4870e-04 lr: 1.5336e-05 eta: 1 day, 2:37:46 time: 1.2301 data_time: 0.0668 memory: 6241 grad_norm: 1.0273 loss: 0.8127 detection_loss_cls: 0.8127 2024/07/07 08:54:11 - mmengine - INFO - Iter(train) [ 40850/120000] base_lr: 1.4858e-04 lr: 1.5326e-05 eta: 1 day, 2:36:47 time: 1.2301 data_time: 0.0669 memory: 6241 grad_norm: 1.0261 loss: 0.8132 detection_loss_cls: 0.8132 2024/07/07 08:55:12 - mmengine - INFO - Iter(train) [ 40900/120000] base_lr: 1.4847e-04 lr: 1.5315e-05 eta: 1 day, 2:35:48 time: 1.2299 data_time: 0.0668 memory: 6241 grad_norm: 1.0259 loss: 0.8129 detection_loss_cls: 0.8129 2024/07/07 08:56:13 - mmengine - INFO - Iter(train) [ 40950/120000] base_lr: 1.4835e-04 lr: 1.5305e-05 eta: 1 day, 2:34:51 time: 1.2301 data_time: 0.0668 memory: 6241 grad_norm: 1.0259 loss: 0.8130 detection_loss_cls: 0.8130 2024/07/07 08:57:14 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 08:57:14 - mmengine - INFO - Iter(train) [ 41000/120000] base_lr: 1.4824e-04 lr: 1.5295e-05 eta: 1 day, 2:33:52 time: 1.2302 data_time: 0.0668 memory: 6241 grad_norm: 1.0252 loss: 0.8118 detection_loss_cls: 0.8118 2024/07/07 08:57:14 - mmengine - INFO - Saving checkpoint at 41000 iterations 2024/07/07 08:58:23 - mmengine - INFO - Iter(train) [ 41050/120000] base_lr: 1.4813e-04 lr: 1.5284e-05 eta: 1 day, 2:33:16 time: 1.2303 data_time: 0.0667 memory: 6241 grad_norm: 1.0237 loss: 0.8108 detection_loss_cls: 0.8108 2024/07/07 08:59:23 - mmengine - INFO - Iter(train) [ 41100/120000] base_lr: 1.4801e-04 lr: 1.5274e-05 eta: 1 day, 2:32:15 time: 1.2303 data_time: 0.0667 memory: 6241 grad_norm: 1.0237 loss: 0.8105 detection_loss_cls: 0.8105 2024/07/07 09:00:23 - mmengine - INFO - Iter(train) [ 41150/120000] base_lr: 1.4790e-04 lr: 1.5264e-05 eta: 1 day, 2:31:14 time: 1.2300 data_time: 0.0667 memory: 6241 grad_norm: 1.0235 loss: 0.8099 detection_loss_cls: 0.8099 2024/07/07 09:01:24 - mmengine - INFO - Iter(train) [ 41200/120000] base_lr: 1.4778e-04 lr: 1.5253e-05 eta: 1 day, 2:30:15 time: 1.2302 data_time: 0.0667 memory: 6241 grad_norm: 1.0237 loss: 0.8105 detection_loss_cls: 0.8105 2024/07/07 09:02:25 - mmengine - INFO - Iter(train) [ 41250/120000] base_lr: 1.4767e-04 lr: 1.5243e-05 eta: 1 day, 2:29:14 time: 1.2301 data_time: 0.0666 memory: 6241 grad_norm: 1.0234 loss: 0.8100 detection_loss_cls: 0.8100 2024/07/07 09:03:25 - mmengine - INFO - Iter(train) [ 41300/120000] base_lr: 1.4756e-04 lr: 1.5232e-05 eta: 1 day, 2:28:13 time: 1.2298 data_time: 0.0665 memory: 6241 grad_norm: 1.0233 loss: 0.8084 detection_loss_cls: 0.8084 2024/07/07 09:04:27 - mmengine - INFO - Iter(train) [ 41350/120000] base_lr: 1.4744e-04 lr: 1.5222e-05 eta: 1 day, 2:27:15 time: 1.2300 data_time: 0.0666 memory: 6241 grad_norm: 1.0234 loss: 0.8088 detection_loss_cls: 0.8088 2024/07/07 09:05:27 - mmengine - INFO - Iter(train) [ 41400/120000] base_lr: 1.4733e-04 lr: 1.5212e-05 eta: 1 day, 2:26:15 time: 1.2299 data_time: 0.0666 memory: 6241 grad_norm: 1.0242 loss: 0.8089 detection_loss_cls: 0.8089 2024/07/07 09:06:28 - mmengine - INFO - Iter(train) [ 41450/120000] base_lr: 1.4721e-04 lr: 1.5201e-05 eta: 1 day, 2:25:14 time: 1.2298 data_time: 0.0665 memory: 6241 grad_norm: 1.0238 loss: 0.8093 detection_loss_cls: 0.8093 2024/07/07 09:07:29 - mmengine - INFO - Iter(train) [ 41500/120000] base_lr: 1.4710e-04 lr: 1.5191e-05 eta: 1 day, 2:24:14 time: 1.2298 data_time: 0.0665 memory: 6241 grad_norm: 1.0239 loss: 0.8085 detection_loss_cls: 0.8085 2024/07/07 09:08:30 - mmengine - INFO - Iter(train) [ 41550/120000] base_lr: 1.4698e-04 lr: 1.5180e-05 eta: 1 day, 2:23:15 time: 1.2299 data_time: 0.0665 memory: 6241 grad_norm: 1.0236 loss: 0.8086 detection_loss_cls: 0.8086 2024/07/07 09:09:30 - mmengine - INFO - Iter(train) [ 41600/120000] base_lr: 1.4687e-04 lr: 1.5170e-05 eta: 1 day, 2:22:15 time: 1.2298 data_time: 0.0665 memory: 6241 grad_norm: 1.0262 loss: 0.8091 detection_loss_cls: 0.8091 2024/07/07 09:10:32 - mmengine - INFO - Iter(train) [ 41650/120000] base_lr: 1.4675e-04 lr: 1.5159e-05 eta: 1 day, 2:21:16 time: 1.2300 data_time: 0.0666 memory: 6241 grad_norm: 1.0261 loss: 0.8101 detection_loss_cls: 0.8101 2024/07/07 09:11:32 - mmengine - INFO - Iter(train) [ 41700/120000] base_lr: 1.4664e-04 lr: 1.5149e-05 eta: 1 day, 2:20:15 time: 1.2298 data_time: 0.0665 memory: 6241 grad_norm: 1.0265 loss: 0.8099 detection_loss_cls: 0.8099 2024/07/07 09:12:33 - mmengine - INFO - Iter(train) [ 41750/120000] base_lr: 1.4652e-04 lr: 1.5139e-05 eta: 1 day, 2:19:16 time: 1.2298 data_time: 0.0665 memory: 6241 grad_norm: 1.0262 loss: 0.8099 detection_loss_cls: 0.8099 2024/07/07 09:13:34 - mmengine - INFO - Iter(train) [ 41800/120000] base_lr: 1.4641e-04 lr: 1.5128e-05 eta: 1 day, 2:18:17 time: 1.2299 data_time: 0.0666 memory: 6241 grad_norm: 1.0262 loss: 0.8102 detection_loss_cls: 0.8102 2024/07/07 09:14:35 - mmengine - INFO - Iter(train) [ 41850/120000] base_lr: 1.4629e-04 lr: 1.5118e-05 eta: 1 day, 2:17:18 time: 1.2300 data_time: 0.0665 memory: 6241 grad_norm: 1.0260 loss: 0.8096 detection_loss_cls: 0.8096 2024/07/07 09:15:36 - mmengine - INFO - Iter(train) [ 41900/120000] base_lr: 1.4618e-04 lr: 1.5107e-05 eta: 1 day, 2:16:19 time: 1.2300 data_time: 0.0665 memory: 6241 grad_norm: 1.0267 loss: 0.8090 detection_loss_cls: 0.8090 2024/07/07 09:16:37 - mmengine - INFO - Iter(train) [ 41950/120000] base_lr: 1.4606e-04 lr: 1.5097e-05 eta: 1 day, 2:15:21 time: 1.2302 data_time: 0.0665 memory: 6241 grad_norm: 1.0271 loss: 0.8093 detection_loss_cls: 0.8093 2024/07/07 09:17:39 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 09:17:39 - mmengine - INFO - Iter(train) [ 42000/120000] base_lr: 1.4595e-04 lr: 1.5086e-05 eta: 1 day, 2:14:23 time: 1.2302 data_time: 0.0664 memory: 6241 grad_norm: 1.0276 loss: 0.8081 detection_loss_cls: 0.8081 2024/07/07 09:17:39 - mmengine - INFO - Saving checkpoint at 42000 iterations 2024/07/07 09:18:47 - mmengine - INFO - Iter(train) [ 42050/120000] base_lr: 1.4583e-04 lr: 1.5076e-05 eta: 1 day, 2:13:44 time: 1.2300 data_time: 0.0665 memory: 6241 grad_norm: 1.0277 loss: 0.8088 detection_loss_cls: 0.8088 2024/07/07 09:19:47 - mmengine - INFO - Iter(train) [ 42100/120000] base_lr: 1.4572e-04 lr: 1.5065e-05 eta: 1 day, 2:12:43 time: 1.2299 data_time: 0.0665 memory: 6241 grad_norm: 1.0278 loss: 0.8092 detection_loss_cls: 0.8092 2024/07/07 09:20:47 - mmengine - INFO - Iter(train) [ 42150/120000] base_lr: 1.4560e-04 lr: 1.5055e-05 eta: 1 day, 2:11:41 time: 1.2298 data_time: 0.0665 memory: 6241 grad_norm: 1.0274 loss: 0.8083 detection_loss_cls: 0.8083 2024/07/07 09:21:47 - mmengine - INFO - Iter(train) [ 42200/120000] base_lr: 1.4548e-04 lr: 1.5044e-05 eta: 1 day, 2:10:38 time: 1.2294 data_time: 0.0665 memory: 6241 grad_norm: 1.0286 loss: 0.8093 detection_loss_cls: 0.8093 2024/07/07 09:22:48 - mmengine - INFO - Iter(train) [ 42250/120000] base_lr: 1.4537e-04 lr: 1.5034e-05 eta: 1 day, 2:09:39 time: 1.2297 data_time: 0.0666 memory: 6241 grad_norm: 1.0290 loss: 0.8101 detection_loss_cls: 0.8101 2024/07/07 09:23:48 - mmengine - INFO - Iter(train) [ 42300/120000] base_lr: 1.4525e-04 lr: 1.5023e-05 eta: 1 day, 2:08:37 time: 1.2297 data_time: 0.0666 memory: 6241 grad_norm: 1.0284 loss: 0.8101 detection_loss_cls: 0.8101 2024/07/07 09:24:49 - mmengine - INFO - Iter(train) [ 42350/120000] base_lr: 1.4514e-04 lr: 1.5012e-05 eta: 1 day, 2:07:36 time: 1.2295 data_time: 0.0667 memory: 6241 grad_norm: 1.0284 loss: 0.8105 detection_loss_cls: 0.8105 2024/07/07 09:25:50 - mmengine - INFO - Iter(train) [ 42400/120000] base_lr: 1.4502e-04 lr: 1.5002e-05 eta: 1 day, 2:06:36 time: 1.2296 data_time: 0.0667 memory: 6241 grad_norm: 1.0283 loss: 0.8114 detection_loss_cls: 0.8114 2024/07/07 09:26:49 - mmengine - INFO - Iter(train) [ 42450/120000] base_lr: 1.4491e-04 lr: 1.4991e-05 eta: 1 day, 2:05:34 time: 1.2295 data_time: 0.0667 memory: 6241 grad_norm: 1.0279 loss: 0.8110 detection_loss_cls: 0.8110 2024/07/07 09:27:49 - mmengine - INFO - Iter(train) [ 42500/120000] base_lr: 1.4479e-04 lr: 1.4981e-05 eta: 1 day, 2:04:31 time: 1.2292 data_time: 0.0667 memory: 6241 grad_norm: 1.0273 loss: 0.8115 detection_loss_cls: 0.8115 2024/07/07 09:28:51 - mmengine - INFO - Iter(train) [ 42550/120000] base_lr: 1.4467e-04 lr: 1.4970e-05 eta: 1 day, 2:03:33 time: 1.2295 data_time: 0.0667 memory: 6241 grad_norm: 1.0288 loss: 0.8114 detection_loss_cls: 0.8114 2024/07/07 09:29:51 - 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mmengine - INFO - Iter(train) [ 42850/120000] base_lr: 1.4397e-04 lr: 1.4907e-05 eta: 1 day, 1:57:24 time: 1.2286 data_time: 0.0666 memory: 6241 grad_norm: 1.0295 loss: 0.8094 detection_loss_cls: 0.8094 2024/07/07 09:35:52 - mmengine - INFO - Iter(train) [ 42900/120000] base_lr: 1.4386e-04 lr: 1.4896e-05 eta: 1 day, 1:56:21 time: 1.2284 data_time: 0.0667 memory: 6241 grad_norm: 1.0296 loss: 0.8095 detection_loss_cls: 0.8095 2024/07/07 09:36:52 - mmengine - INFO - Iter(train) [ 42950/120000] base_lr: 1.4374e-04 lr: 1.4885e-05 eta: 1 day, 1:55:20 time: 1.2283 data_time: 0.0666 memory: 6241 grad_norm: 1.0298 loss: 0.8087 detection_loss_cls: 0.8087 2024/07/07 09:37:53 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 09:37:53 - mmengine - INFO - Iter(train) [ 43000/120000] base_lr: 1.4362e-04 lr: 1.4875e-05 eta: 1 day, 1:54:20 time: 1.2284 data_time: 0.0666 memory: 6241 grad_norm: 1.0307 loss: 0.8093 detection_loss_cls: 0.8093 2024/07/07 09:37:53 - mmengine - INFO - Saving checkpoint at 43000 iterations 2024/07/07 09:39:01 - mmengine - INFO - Iter(train) [ 43050/120000] base_lr: 1.4351e-04 lr: 1.4864e-05 eta: 1 day, 1:53:40 time: 1.2281 data_time: 0.0666 memory: 6241 grad_norm: 1.0310 loss: 0.8081 detection_loss_cls: 0.8081 2024/07/07 09:40:01 - mmengine - INFO - Iter(train) [ 43100/120000] base_lr: 1.4339e-04 lr: 1.4854e-05 eta: 1 day, 1:52:37 time: 1.2278 data_time: 0.0666 memory: 6241 grad_norm: 1.0315 loss: 0.8084 detection_loss_cls: 0.8084 2024/07/07 09:41:02 - mmengine - INFO - Iter(train) [ 43150/120000] base_lr: 1.4327e-04 lr: 1.4843e-05 eta: 1 day, 1:51:37 time: 1.2279 data_time: 0.0666 memory: 6241 grad_norm: 1.0303 loss: 0.8084 detection_loss_cls: 0.8084 2024/07/07 09:42:01 - mmengine - INFO - Iter(train) [ 43200/120000] base_lr: 1.4315e-04 lr: 1.4832e-05 eta: 1 day, 1:50:34 time: 1.2277 data_time: 0.0665 memory: 6241 grad_norm: 1.0297 loss: 0.8080 detection_loss_cls: 0.8080 2024/07/07 09:43:02 - mmengine - INFO - Iter(train) [ 43250/120000] base_lr: 1.4304e-04 lr: 1.4822e-05 eta: 1 day, 1:49:33 time: 1.2275 data_time: 0.0665 memory: 6241 grad_norm: 1.0290 loss: 0.8082 detection_loss_cls: 0.8082 2024/07/07 09:44:02 - mmengine - INFO - Iter(train) [ 43300/120000] base_lr: 1.4292e-04 lr: 1.4811e-05 eta: 1 day, 1:48:33 time: 1.2275 data_time: 0.0666 memory: 6241 grad_norm: 1.0290 loss: 0.8093 detection_loss_cls: 0.8093 2024/07/07 09:45:03 - mmengine - INFO - Iter(train) [ 43350/120000] base_lr: 1.4280e-04 lr: 1.4800e-05 eta: 1 day, 1:47:31 time: 1.2274 data_time: 0.0665 memory: 6241 grad_norm: 1.0285 loss: 0.8090 detection_loss_cls: 0.8090 2024/07/07 09:46:02 - mmengine - INFO - Iter(train) [ 43400/120000] base_lr: 1.4268e-04 lr: 1.4790e-05 eta: 1 day, 1:46:29 time: 1.2271 data_time: 0.0665 memory: 6241 grad_norm: 1.0288 loss: 0.8089 detection_loss_cls: 0.8089 2024/07/07 09:47:04 - mmengine - INFO - Iter(train) [ 43450/120000] base_lr: 1.4257e-04 lr: 1.4779e-05 eta: 1 day, 1:45:30 time: 1.2272 data_time: 0.0665 memory: 6241 grad_norm: 1.0282 loss: 0.8080 detection_loss_cls: 0.8080 2024/07/07 09:48:03 - mmengine - INFO - Iter(train) [ 43500/120000] base_lr: 1.4245e-04 lr: 1.4768e-05 eta: 1 day, 1:44:27 time: 1.2269 data_time: 0.0664 memory: 6241 grad_norm: 1.0280 loss: 0.8055 detection_loss_cls: 0.8055 2024/07/07 09:49:03 - mmengine - INFO - Iter(train) [ 43550/120000] base_lr: 1.4233e-04 lr: 1.4757e-05 eta: 1 day, 1:43:25 time: 1.2265 data_time: 0.0664 memory: 6241 grad_norm: 1.0276 loss: 0.8046 detection_loss_cls: 0.8046 2024/07/07 09:50:04 - mmengine - INFO - Iter(train) [ 43600/120000] base_lr: 1.4221e-04 lr: 1.4747e-05 eta: 1 day, 1:42:23 time: 1.2264 data_time: 0.0664 memory: 6241 grad_norm: 1.0271 loss: 0.8041 detection_loss_cls: 0.8041 2024/07/07 09:51:05 - mmengine - INFO - Iter(train) [ 43650/120000] base_lr: 1.4210e-04 lr: 1.4736e-05 eta: 1 day, 1:41:24 time: 1.2266 data_time: 0.0664 memory: 6241 grad_norm: 1.0271 loss: 0.8038 detection_loss_cls: 0.8038 2024/07/07 09:52:05 - mmengine - INFO - Iter(train) [ 43700/120000] base_lr: 1.4198e-04 lr: 1.4725e-05 eta: 1 day, 1:40:22 time: 1.2263 data_time: 0.0664 memory: 6241 grad_norm: 1.0278 loss: 0.8040 detection_loss_cls: 0.8040 2024/07/07 09:53:06 - mmengine - INFO - Iter(train) [ 43750/120000] base_lr: 1.4186e-04 lr: 1.4715e-05 eta: 1 day, 1:39:23 time: 1.2265 data_time: 0.0663 memory: 6241 grad_norm: 1.0282 loss: 0.8031 detection_loss_cls: 0.8031 2024/07/07 09:54:06 - mmengine - INFO - Iter(train) [ 43800/120000] base_lr: 1.4174e-04 lr: 1.4704e-05 eta: 1 day, 1:38:22 time: 1.2263 data_time: 0.0664 memory: 6241 grad_norm: 1.0284 loss: 0.8035 detection_loss_cls: 0.8035 2024/07/07 09:55:07 - mmengine - INFO - Iter(train) [ 43850/120000] base_lr: 1.4162e-04 lr: 1.4693e-05 eta: 1 day, 1:37:21 time: 1.2261 data_time: 0.0663 memory: 6241 grad_norm: 1.0279 loss: 0.8034 detection_loss_cls: 0.8034 2024/07/07 09:56:08 - mmengine - INFO - Iter(train) [ 43900/120000] base_lr: 1.4151e-04 lr: 1.4682e-05 eta: 1 day, 1:36:21 time: 1.2262 data_time: 0.0664 memory: 6241 grad_norm: 1.0280 loss: 0.8036 detection_loss_cls: 0.8036 2024/07/07 09:57:08 - mmengine - INFO - Iter(train) [ 43950/120000] base_lr: 1.4139e-04 lr: 1.4672e-05 eta: 1 day, 1:35:20 time: 1.2261 data_time: 0.0663 memory: 6241 grad_norm: 1.0276 loss: 0.8026 detection_loss_cls: 0.8026 2024/07/07 09:58:08 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 09:58:08 - mmengine - INFO - Iter(train) [ 44000/120000] base_lr: 1.4127e-04 lr: 1.4661e-05 eta: 1 day, 1:34:18 time: 1.2258 data_time: 0.0664 memory: 6241 grad_norm: 1.0274 loss: 0.8037 detection_loss_cls: 0.8037 2024/07/07 09:58:08 - mmengine - INFO - Saving checkpoint at 44000 iterations 2024/07/07 09:59:17 - mmengine - INFO - Iter(train) [ 44050/120000] base_lr: 1.4115e-04 lr: 1.4650e-05 eta: 1 day, 1:33:41 time: 1.2206 data_time: 0.0609 memory: 6241 grad_norm: 1.0273 loss: 0.8034 detection_loss_cls: 0.8034 2024/07/07 10:00:17 - mmengine - INFO - Iter(train) [ 44100/120000] base_lr: 1.4103e-04 lr: 1.4639e-05 eta: 1 day, 1:32:39 time: 1.2205 data_time: 0.0608 memory: 6241 grad_norm: 1.0275 loss: 0.8017 detection_loss_cls: 0.8017 2024/07/07 10:01:18 - mmengine - INFO - Iter(train) [ 44150/120000] base_lr: 1.4091e-04 lr: 1.4629e-05 eta: 1 day, 1:31:38 time: 1.2200 data_time: 0.0608 memory: 6241 grad_norm: 1.0277 loss: 0.8010 detection_loss_cls: 0.8010 2024/07/07 10:02:19 - mmengine - INFO - Iter(train) [ 44200/120000] base_lr: 1.4080e-04 lr: 1.4618e-05 eta: 1 day, 1:30:38 time: 1.2201 data_time: 0.0608 memory: 6241 grad_norm: 1.0280 loss: 0.7994 detection_loss_cls: 0.7994 2024/07/07 10:03:18 - mmengine - INFO - Iter(train) [ 44250/120000] base_lr: 1.4068e-04 lr: 1.4607e-05 eta: 1 day, 1:29:36 time: 1.2198 data_time: 0.0608 memory: 6241 grad_norm: 1.0279 loss: 0.7993 detection_loss_cls: 0.7993 2024/07/07 10:04:19 - mmengine - INFO - Iter(train) [ 44300/120000] base_lr: 1.4056e-04 lr: 1.4596e-05 eta: 1 day, 1:28:34 time: 1.2193 data_time: 0.0608 memory: 6241 grad_norm: 1.0282 loss: 0.7987 detection_loss_cls: 0.7987 2024/07/07 10:05:21 - mmengine - INFO - Iter(train) [ 44350/120000] base_lr: 1.4044e-04 lr: 1.4585e-05 eta: 1 day, 1:27:38 time: 1.2195 data_time: 0.0608 memory: 6241 grad_norm: 1.0281 loss: 0.7998 detection_loss_cls: 0.7998 2024/07/07 10:06:21 - mmengine - INFO - Iter(train) [ 44400/120000] base_lr: 1.4032e-04 lr: 1.4575e-05 eta: 1 day, 1:26:35 time: 1.2193 data_time: 0.0608 memory: 6241 grad_norm: 1.0278 loss: 0.7998 detection_loss_cls: 0.7998 2024/07/07 10:07:21 - mmengine - INFO - Iter(train) [ 44450/120000] base_lr: 1.4020e-04 lr: 1.4564e-05 eta: 1 day, 1:25:34 time: 1.2190 data_time: 0.0609 memory: 6241 grad_norm: 1.0276 loss: 0.8007 detection_loss_cls: 0.8007 2024/07/07 10:08:22 - mmengine - INFO - Iter(train) [ 44500/120000] base_lr: 1.4008e-04 lr: 1.4553e-05 eta: 1 day, 1:24:33 time: 1.2191 data_time: 0.0609 memory: 6241 grad_norm: 1.0277 loss: 0.8013 detection_loss_cls: 0.8013 2024/07/07 10:09:22 - mmengine - INFO - Iter(train) [ 44550/120000] base_lr: 1.3996e-04 lr: 1.4542e-05 eta: 1 day, 1:23:32 time: 1.2189 data_time: 0.0608 memory: 6241 grad_norm: 1.0281 loss: 0.8006 detection_loss_cls: 0.8006 2024/07/07 10:10:22 - mmengine - INFO - Iter(train) [ 44600/120000] base_lr: 1.3984e-04 lr: 1.4531e-05 eta: 1 day, 1:22:30 time: 1.2188 data_time: 0.0608 memory: 6241 grad_norm: 1.0283 loss: 0.8009 detection_loss_cls: 0.8009 2024/07/07 10:11:23 - mmengine - INFO - Iter(train) [ 44650/120000] base_lr: 1.3972e-04 lr: 1.4520e-05 eta: 1 day, 1:21:30 time: 1.2188 data_time: 0.0608 memory: 6241 grad_norm: 1.0286 loss: 0.8004 detection_loss_cls: 0.8004 2024/07/07 10:12:23 - mmengine - INFO - Iter(train) [ 44700/120000] base_lr: 1.3961e-04 lr: 1.4510e-05 eta: 1 day, 1:20:28 time: 1.2186 data_time: 0.0609 memory: 6241 grad_norm: 1.0289 loss: 0.8003 detection_loss_cls: 0.8003 2024/07/07 10:13:23 - mmengine - INFO - Iter(train) [ 44750/120000] base_lr: 1.3949e-04 lr: 1.4499e-05 eta: 1 day, 1:19:26 time: 1.2182 data_time: 0.0609 memory: 6241 grad_norm: 1.0290 loss: 0.8001 detection_loss_cls: 0.8001 2024/07/07 10:14:24 - mmengine - INFO - Iter(train) [ 44800/120000] base_lr: 1.3937e-04 lr: 1.4488e-05 eta: 1 day, 1:18:27 time: 1.2184 data_time: 0.0610 memory: 6241 grad_norm: 1.0295 loss: 0.8011 detection_loss_cls: 0.8011 2024/07/07 10:15:24 - mmengine - INFO - Iter(train) [ 44850/120000] base_lr: 1.3925e-04 lr: 1.4477e-05 eta: 1 day, 1:17:26 time: 1.2182 data_time: 0.0609 memory: 6241 grad_norm: 1.0299 loss: 0.8010 detection_loss_cls: 0.8010 2024/07/07 10:16:25 - mmengine - INFO - Iter(train) [ 44900/120000] base_lr: 1.3913e-04 lr: 1.4466e-05 eta: 1 day, 1:16:25 time: 1.2181 data_time: 0.0610 memory: 6241 grad_norm: 1.0299 loss: 0.8020 detection_loss_cls: 0.8020 2024/07/07 10:17:26 - mmengine - INFO - Iter(train) [ 44950/120000] base_lr: 1.3901e-04 lr: 1.4455e-05 eta: 1 day, 1:15:26 time: 1.2180 data_time: 0.0610 memory: 6241 grad_norm: 1.0307 loss: 0.8019 detection_loss_cls: 0.8019 2024/07/07 10:18:26 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 10:18:26 - mmengine - INFO - Iter(train) [ 45000/120000] base_lr: 1.3889e-04 lr: 1.4444e-05 eta: 1 day, 1:14:24 time: 1.2177 data_time: 0.0610 memory: 6241 grad_norm: 1.0300 loss: 0.8023 detection_loss_cls: 0.8023 2024/07/07 10:18:26 - mmengine - INFO - Saving checkpoint at 45000 iterations 2024/07/07 10:19:14 - mmengine - INFO - Iter(val) [ 50/105] eta: 0:00:44 time: 0.8030 data_time: 0.0296 memory: 6808 2024/07/07 10:19:55 - mmengine - INFO - Iter(val) [100/105] eta: 0:00:04 time: 0.8031 data_time: 0.0296 memory: 6810 2024/07/07 10:20:01 - mmengine - INFO - Evaluating bbox... 2024/07/07 10:20:27 - mmengine - INFO - bbox_mAP_copypaste: 0.398 0.565 0.426 0.187 0.441 0.572 2024/07/07 10:20:28 - mmengine - INFO - Iter(val) [105/105] coco/bbox_mAP: 0.3980 coco/bbox_mAP_50: 0.5650 coco/bbox_mAP_75: 0.4260 coco/bbox_mAP_s: 0.1870 coco/bbox_mAP_m: 0.4410 coco/bbox_mAP_l: 0.5720 data_time: 0.0285 time: 0.7988 2024/07/07 10:21:28 - mmengine - INFO - Iter(train) [ 45050/120000] base_lr: 1.3877e-04 lr: 1.4433e-05 eta: 1 day, 1:14:39 time: 1.2231 data_time: 0.0665 memory: 6804 grad_norm: 1.0303 loss: 0.8029 detection_loss_cls: 0.8029 2024/07/07 10:22:29 - mmengine - INFO - Iter(train) [ 45100/120000] base_lr: 1.3865e-04 lr: 1.4423e-05 eta: 1 day, 1:13:40 time: 1.2233 data_time: 0.0665 memory: 6238 grad_norm: 1.0304 loss: 0.8029 detection_loss_cls: 0.8029 2024/07/07 10:23:29 - mmengine - INFO - Iter(train) [ 45150/120000] base_lr: 1.3853e-04 lr: 1.4412e-05 eta: 1 day, 1:12:37 time: 1.2232 data_time: 0.0665 memory: 6238 grad_norm: 1.0311 loss: 0.8023 detection_loss_cls: 0.8023 2024/07/07 10:24:30 - mmengine - INFO - Iter(train) [ 45200/120000] base_lr: 1.3841e-04 lr: 1.4401e-05 eta: 1 day, 1:11:36 time: 1.2230 data_time: 0.0665 memory: 6238 grad_norm: 1.0308 loss: 0.8018 detection_loss_cls: 0.8018 2024/07/07 10:25:31 - mmengine - INFO - Iter(train) [ 45250/120000] base_lr: 1.3829e-04 lr: 1.4390e-05 eta: 1 day, 1:10:37 time: 1.2232 data_time: 0.0665 memory: 6238 grad_norm: 1.0308 loss: 0.8016 detection_loss_cls: 0.8016 2024/07/07 10:26:31 - 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mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 11:00:50 - mmengine - INFO - Iter(train) [ 47000/120000] base_lr: 1.3405e-04 lr: 1.4004e-05 eta: 1 day, 0:35:08 time: 1.2211 data_time: 0.0665 memory: 6238 grad_norm: 1.0230 loss: 0.7891 detection_loss_cls: 0.7891 2024/07/07 11:00:50 - mmengine - INFO - Saving checkpoint at 47000 iterations 2024/07/07 11:02:00 - mmengine - INFO - Iter(train) [ 47050/120000] base_lr: 1.3393e-04 lr: 1.3993e-05 eta: 1 day, 0:34:28 time: 1.2214 data_time: 0.0666 memory: 6238 grad_norm: 1.0229 loss: 0.7892 detection_loss_cls: 0.7892 2024/07/07 11:03:00 - mmengine - INFO - Iter(train) [ 47100/120000] base_lr: 1.3380e-04 lr: 1.3982e-05 eta: 1 day, 0:33:25 time: 1.2215 data_time: 0.0666 memory: 6238 grad_norm: 1.0223 loss: 0.7879 detection_loss_cls: 0.7879 2024/07/07 11:04:00 - mmengine - INFO - Iter(train) [ 47150/120000] base_lr: 1.3368e-04 lr: 1.3971e-05 eta: 1 day, 0:32:24 time: 1.2213 data_time: 0.0666 memory: 6238 grad_norm: 1.0232 loss: 0.7877 detection_loss_cls: 0.7877 2024/07/07 11:05:01 - 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mmengine - INFO - Iter(train) [ 47450/120000] base_lr: 1.3295e-04 lr: 1.3904e-05 eta: 1 day, 0:26:17 time: 1.2214 data_time: 0.0666 memory: 6238 grad_norm: 1.0254 loss: 0.7885 detection_loss_cls: 0.7885 2024/07/07 11:11:04 - mmengine - INFO - Iter(train) [ 47500/120000] base_lr: 1.3282e-04 lr: 1.3893e-05 eta: 1 day, 0:25:18 time: 1.2218 data_time: 0.0667 memory: 6238 grad_norm: 1.0255 loss: 0.7901 detection_loss_cls: 0.7901 2024/07/07 11:12:04 - mmengine - INFO - Iter(train) [ 47550/120000] base_lr: 1.3270e-04 lr: 1.3882e-05 eta: 1 day, 0:24:16 time: 1.2218 data_time: 0.0667 memory: 6238 grad_norm: 1.0255 loss: 0.7911 detection_loss_cls: 0.7911 2024/07/07 11:13:04 - mmengine - INFO - Iter(train) [ 47600/120000] base_lr: 1.3258e-04 lr: 1.3871e-05 eta: 1 day, 0:23:14 time: 1.2218 data_time: 0.0667 memory: 6238 grad_norm: 1.0257 loss: 0.7901 detection_loss_cls: 0.7901 2024/07/07 11:14:05 - mmengine - INFO - Iter(train) [ 47650/120000] base_lr: 1.3246e-04 lr: 1.3860e-05 eta: 1 day, 0:22:15 time: 1.2219 data_time: 0.0667 memory: 6238 grad_norm: 1.0260 loss: 0.7906 detection_loss_cls: 0.7906 2024/07/07 11:15:05 - mmengine - INFO - Iter(train) [ 47700/120000] base_lr: 1.3233e-04 lr: 1.3849e-05 eta: 1 day, 0:21:13 time: 1.2219 data_time: 0.0667 memory: 6238 grad_norm: 1.0257 loss: 0.7900 detection_loss_cls: 0.7900 2024/07/07 11:16:06 - mmengine - INFO - Iter(train) [ 47750/120000] base_lr: 1.3221e-04 lr: 1.3837e-05 eta: 1 day, 0:20:12 time: 1.2217 data_time: 0.0667 memory: 6238 grad_norm: 1.0264 loss: 0.7911 detection_loss_cls: 0.7911 2024/07/07 11:17:07 - mmengine - INFO - Iter(train) [ 47800/120000] base_lr: 1.3209e-04 lr: 1.3826e-05 eta: 1 day, 0:19:12 time: 1.2218 data_time: 0.0667 memory: 6238 grad_norm: 1.0263 loss: 0.7899 detection_loss_cls: 0.7899 2024/07/07 11:18:07 - mmengine - INFO - Iter(train) [ 47850/120000] base_lr: 1.3196e-04 lr: 1.3815e-05 eta: 1 day, 0:18:11 time: 1.2219 data_time: 0.0667 memory: 6238 grad_norm: 1.0260 loss: 0.7901 detection_loss_cls: 0.7901 2024/07/07 11:19:08 - mmengine - INFO - Iter(train) [ 47900/120000] base_lr: 1.3184e-04 lr: 1.3804e-05 eta: 1 day, 0:17:10 time: 1.2217 data_time: 0.0667 memory: 6238 grad_norm: 1.0258 loss: 0.7893 detection_loss_cls: 0.7893 2024/07/07 11:20:09 - mmengine - INFO - Iter(train) [ 47950/120000] base_lr: 1.3172e-04 lr: 1.3793e-05 eta: 1 day, 0:16:10 time: 1.2219 data_time: 0.0667 memory: 6238 grad_norm: 1.0253 loss: 0.7907 detection_loss_cls: 0.7907 2024/07/07 11:21:09 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 11:21:09 - mmengine - INFO - Iter(train) [ 48000/120000] base_lr: 1.3160e-04 lr: 1.3781e-05 eta: 1 day, 0:15:10 time: 1.2221 data_time: 0.0666 memory: 6238 grad_norm: 1.0255 loss: 0.7897 detection_loss_cls: 0.7897 2024/07/07 11:21:09 - mmengine - INFO - Saving checkpoint at 48000 iterations 2024/07/07 11:22:18 - mmengine - INFO - Iter(train) [ 48050/120000] base_lr: 1.3147e-04 lr: 1.3770e-05 eta: 1 day, 0:14:27 time: 1.2219 data_time: 0.0667 memory: 6238 grad_norm: 1.0253 loss: 0.7899 detection_loss_cls: 0.7899 2024/07/07 11:23:20 - mmengine - INFO - Iter(train) [ 48100/120000] base_lr: 1.3135e-04 lr: 1.3759e-05 eta: 1 day, 0:13:30 time: 1.2224 data_time: 0.0668 memory: 6238 grad_norm: 1.0253 loss: 0.7906 detection_loss_cls: 0.7906 2024/07/07 11:24:21 - 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mmengine - INFO - Iter(train) [ 48400/120000] base_lr: 1.3061e-04 lr: 1.3692e-05 eta: 1 day, 0:07:32 time: 1.2232 data_time: 0.0668 memory: 6238 grad_norm: 1.0258 loss: 0.7914 detection_loss_cls: 0.7914 2024/07/07 11:30:27 - mmengine - INFO - Iter(train) [ 48450/120000] base_lr: 1.3048e-04 lr: 1.3680e-05 eta: 1 day, 0:06:30 time: 1.2233 data_time: 0.0668 memory: 6238 grad_norm: 1.0259 loss: 0.7908 detection_loss_cls: 0.7908 2024/07/07 11:31:28 - mmengine - INFO - Iter(train) [ 48500/120000] base_lr: 1.3036e-04 lr: 1.3669e-05 eta: 1 day, 0:05:30 time: 1.2233 data_time: 0.0668 memory: 6238 grad_norm: 1.0259 loss: 0.7911 detection_loss_cls: 0.7911 2024/07/07 11:32:29 - mmengine - INFO - Iter(train) [ 48550/120000] base_lr: 1.3024e-04 lr: 1.3658e-05 eta: 1 day, 0:04:31 time: 1.2236 data_time: 0.0668 memory: 6238 grad_norm: 1.0258 loss: 0.7910 detection_loss_cls: 0.7910 2024/07/07 11:33:30 - mmengine - INFO - Iter(train) [ 48600/120000] base_lr: 1.3011e-04 lr: 1.3647e-05 eta: 1 day, 0:03:31 time: 1.2237 data_time: 0.0668 memory: 6238 grad_norm: 1.0254 loss: 0.7899 detection_loss_cls: 0.7899 2024/07/07 11:34:31 - 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mmengine - INFO - Iter(train) [ 49600/120000] base_lr: 1.2763e-04 lr: 1.3421e-05 eta: 23:43:51 time: 1.2214 data_time: 0.0613 memory: 6238 grad_norm: 1.0287 loss: 0.7892 detection_loss_cls: 0.7892 2024/07/07 11:55:00 - mmengine - INFO - Iter(train) [ 49650/120000] base_lr: 1.2750e-04 lr: 1.3409e-05 eta: 23:42:51 time: 1.2214 data_time: 0.0613 memory: 6238 grad_norm: 1.0279 loss: 0.7882 detection_loss_cls: 0.7882 2024/07/07 11:56:01 - mmengine - INFO - Iter(train) [ 49700/120000] base_lr: 1.2738e-04 lr: 1.3398e-05 eta: 23:41:50 time: 1.2212 data_time: 0.0613 memory: 6238 grad_norm: 1.0277 loss: 0.7875 detection_loss_cls: 0.7875 2024/07/07 11:57:03 - mmengine - INFO - Iter(train) [ 49750/120000] base_lr: 1.2725e-04 lr: 1.3386e-05 eta: 23:40:52 time: 1.2218 data_time: 0.0613 memory: 6238 grad_norm: 1.0279 loss: 0.7885 detection_loss_cls: 0.7885 2024/07/07 11:58:03 - mmengine - INFO - Iter(train) [ 49800/120000] base_lr: 1.2713e-04 lr: 1.3375e-05 eta: 23:39:51 time: 1.2220 data_time: 0.0613 memory: 6238 grad_norm: 1.0290 loss: 0.7884 detection_loss_cls: 0.7884 2024/07/07 11:59:04 - mmengine - INFO - Iter(train) [ 49850/120000] base_lr: 1.2700e-04 lr: 1.3364e-05 eta: 23:38:51 time: 1.2219 data_time: 0.0613 memory: 6238 grad_norm: 1.0288 loss: 0.7889 detection_loss_cls: 0.7889 2024/07/07 12:00:06 - mmengine - INFO - Iter(train) [ 49900/120000] base_lr: 1.2688e-04 lr: 1.3352e-05 eta: 23:37:52 time: 1.2223 data_time: 0.0612 memory: 6238 grad_norm: 1.0297 loss: 0.7877 detection_loss_cls: 0.7877 2024/07/07 12:01:06 - mmengine - INFO - Iter(train) [ 49950/120000] base_lr: 1.2675e-04 lr: 1.3341e-05 eta: 23:36:51 time: 1.2225 data_time: 0.0612 memory: 6238 grad_norm: 1.0297 loss: 0.7875 detection_loss_cls: 0.7875 2024/07/07 12:02:07 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 12:02:07 - mmengine - INFO - Iter(train) [ 50000/120000] base_lr: 1.2663e-04 lr: 1.3330e-05 eta: 23:35:50 time: 1.2223 data_time: 0.0612 memory: 6238 grad_norm: 1.0295 loss: 0.7878 detection_loss_cls: 0.7878 2024/07/07 12:02:07 - mmengine - INFO - Saving checkpoint at 50000 iterations 2024/07/07 12:02:55 - mmengine - INFO - Iter(val) [ 50/105] eta: 0:00:44 time: 0.8028 data_time: 0.0295 memory: 6805 2024/07/07 12:03:35 - mmengine - INFO - Iter(val) [100/105] eta: 0:00:04 time: 0.8026 data_time: 0.0295 memory: 6809 2024/07/07 12:03:41 - mmengine - INFO - Evaluating bbox... 2024/07/07 12:04:07 - mmengine - INFO - bbox_mAP_copypaste: 0.401 0.568 0.429 0.187 0.442 0.579 2024/07/07 12:04:08 - mmengine - INFO - Iter(val) [105/105] coco/bbox_mAP: 0.4010 coco/bbox_mAP_50: 0.5680 coco/bbox_mAP_75: 0.4290 coco/bbox_mAP_s: 0.1870 coco/bbox_mAP_m: 0.4420 coco/bbox_mAP_l: 0.5790 data_time: 0.0281 time: 0.7931 2024/07/07 12:05:09 - mmengine - INFO - Iter(train) [ 50050/120000] base_lr: 1.2650e-04 lr: 1.3318e-05 eta: 23:35:50 time: 1.2280 data_time: 0.0666 memory: 6806 grad_norm: 1.0288 loss: 0.7878 detection_loss_cls: 0.7878 2024/07/07 12:06:09 - mmengine - INFO - Iter(train) [ 50100/120000] base_lr: 1.2638e-04 lr: 1.3307e-05 eta: 23:34:48 time: 1.2280 data_time: 0.0666 memory: 6243 grad_norm: 1.0288 loss: 0.7888 detection_loss_cls: 0.7888 2024/07/07 12:07:09 - mmengine - INFO - Iter(train) [ 50150/120000] base_lr: 1.2625e-04 lr: 1.3295e-05 eta: 23:33:47 time: 1.2279 data_time: 0.0666 memory: 6243 grad_norm: 1.0298 loss: 0.7888 detection_loss_cls: 0.7888 2024/07/07 12:08:10 - mmengine - INFO - Iter(train) [ 50200/120000] base_lr: 1.2612e-04 lr: 1.3284e-05 eta: 23:32:47 time: 1.2279 data_time: 0.0666 memory: 6243 grad_norm: 1.0307 loss: 0.7894 detection_loss_cls: 0.7894 2024/07/07 12:09:10 - mmengine - INFO - Iter(train) [ 50250/120000] base_lr: 1.2600e-04 lr: 1.3273e-05 eta: 23:31:44 time: 1.2279 data_time: 0.0666 memory: 6243 grad_norm: 1.0310 loss: 0.7894 detection_loss_cls: 0.7894 2024/07/07 12:10:10 - mmengine - INFO - Iter(train) [ 50300/120000] base_lr: 1.2587e-04 lr: 1.3261e-05 eta: 23:30:43 time: 1.2278 data_time: 0.0666 memory: 6243 grad_norm: 1.0310 loss: 0.7897 detection_loss_cls: 0.7897 2024/07/07 12:11:12 - mmengine - INFO - Iter(train) [ 50350/120000] base_lr: 1.2575e-04 lr: 1.3250e-05 eta: 23:29:42 time: 1.2280 data_time: 0.0667 memory: 6243 grad_norm: 1.0310 loss: 0.7905 detection_loss_cls: 0.7905 2024/07/07 12:12:11 - mmengine - INFO - Iter(train) [ 50400/120000] base_lr: 1.2562e-04 lr: 1.3238e-05 eta: 23:28:40 time: 1.2279 data_time: 0.0667 memory: 6243 grad_norm: 1.0314 loss: 0.7909 detection_loss_cls: 0.7909 2024/07/07 12:13:11 - mmengine - INFO - Iter(train) [ 50450/120000] base_lr: 1.2550e-04 lr: 1.3227e-05 eta: 23:27:38 time: 1.2277 data_time: 0.0667 memory: 6243 grad_norm: 1.0325 loss: 0.7903 detection_loss_cls: 0.7903 2024/07/07 12:14:13 - mmengine - INFO - Iter(train) [ 50500/120000] base_lr: 1.2537e-04 lr: 1.3216e-05 eta: 23:26:38 time: 1.2279 data_time: 0.0666 memory: 6243 grad_norm: 1.0328 loss: 0.7893 detection_loss_cls: 0.7893 2024/07/07 12:15:13 - mmengine - INFO - Iter(train) [ 50550/120000] base_lr: 1.2525e-04 lr: 1.3204e-05 eta: 23:25:38 time: 1.2281 data_time: 0.0666 memory: 6243 grad_norm: 1.0334 loss: 0.7885 detection_loss_cls: 0.7885 2024/07/07 12:16:14 - mmengine - INFO - Iter(train) [ 50600/120000] base_lr: 1.2512e-04 lr: 1.3193e-05 eta: 23:24:36 time: 1.2280 data_time: 0.0666 memory: 6243 grad_norm: 1.0340 loss: 0.7887 detection_loss_cls: 0.7887 2024/07/07 12:17:15 - mmengine - INFO - Iter(train) [ 50650/120000] base_lr: 1.2499e-04 lr: 1.3181e-05 eta: 23:23:35 time: 1.2281 data_time: 0.0666 memory: 6243 grad_norm: 1.0332 loss: 0.7883 detection_loss_cls: 0.7883 2024/07/07 12:18:15 - mmengine - INFO - Iter(train) [ 50700/120000] base_lr: 1.2487e-04 lr: 1.3170e-05 eta: 23:22:34 time: 1.2283 data_time: 0.0666 memory: 6243 grad_norm: 1.0334 loss: 0.7895 detection_loss_cls: 0.7895 2024/07/07 12:19:15 - mmengine - INFO - Iter(train) [ 50750/120000] base_lr: 1.2474e-04 lr: 1.3158e-05 eta: 23:21:31 time: 1.2280 data_time: 0.0666 memory: 6243 grad_norm: 1.0330 loss: 0.7898 detection_loss_cls: 0.7898 2024/07/07 12:20:16 - mmengine - INFO - Iter(train) [ 50800/120000] base_lr: 1.2462e-04 lr: 1.3147e-05 eta: 23:20:31 time: 1.2284 data_time: 0.0666 memory: 6243 grad_norm: 1.0333 loss: 0.7895 detection_loss_cls: 0.7895 2024/07/07 12:21:16 - mmengine - INFO - Iter(train) [ 50850/120000] base_lr: 1.2449e-04 lr: 1.3136e-05 eta: 23:19:30 time: 1.2285 data_time: 0.0666 memory: 6243 grad_norm: 1.0337 loss: 0.7899 detection_loss_cls: 0.7899 2024/07/07 12:22:17 - mmengine - INFO - Iter(train) [ 50900/120000] base_lr: 1.2437e-04 lr: 1.3124e-05 eta: 23:18:29 time: 1.2285 data_time: 0.0666 memory: 6243 grad_norm: 1.0338 loss: 0.7904 detection_loss_cls: 0.7904 2024/07/07 12:23:18 - mmengine - INFO - Iter(train) [ 50950/120000] base_lr: 1.2424e-04 lr: 1.3113e-05 eta: 23:17:28 time: 1.2287 data_time: 0.0666 memory: 6243 grad_norm: 1.0339 loss: 0.7901 detection_loss_cls: 0.7901 2024/07/07 12:24:18 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 12:24:18 - mmengine - INFO - Iter(train) [ 51000/120000] base_lr: 1.2411e-04 lr: 1.3101e-05 eta: 23:16:27 time: 1.2288 data_time: 0.0665 memory: 6243 grad_norm: 1.0338 loss: 0.7898 detection_loss_cls: 0.7898 2024/07/07 12:24:18 - mmengine - INFO - Saving checkpoint at 51000 iterations 2024/07/07 12:25:27 - mmengine - INFO - Iter(train) [ 51050/120000] base_lr: 1.2399e-04 lr: 1.3090e-05 eta: 23:15:42 time: 1.2286 data_time: 0.0664 memory: 6243 grad_norm: 1.0341 loss: 0.7896 detection_loss_cls: 0.7896 2024/07/07 12:26:28 - mmengine - INFO - Iter(train) [ 51100/120000] base_lr: 1.2386e-04 lr: 1.3078e-05 eta: 23:14:42 time: 1.2289 data_time: 0.0664 memory: 6243 grad_norm: 1.0343 loss: 0.7900 detection_loss_cls: 0.7900 2024/07/07 12:27:28 - mmengine - INFO - Iter(train) [ 51150/120000] base_lr: 1.2374e-04 lr: 1.3067e-05 eta: 23:13:41 time: 1.2291 data_time: 0.0663 memory: 6243 grad_norm: 1.0337 loss: 0.7895 detection_loss_cls: 0.7895 2024/07/07 12:28:30 - mmengine - INFO - Iter(train) [ 51200/120000] base_lr: 1.2361e-04 lr: 1.3055e-05 eta: 23:12:41 time: 1.2290 data_time: 0.0664 memory: 6243 grad_norm: 1.0336 loss: 0.7897 detection_loss_cls: 0.7897 2024/07/07 12:29:31 - mmengine - INFO - Iter(train) [ 51250/120000] base_lr: 1.2348e-04 lr: 1.3044e-05 eta: 23:11:42 time: 1.2294 data_time: 0.0663 memory: 6243 grad_norm: 1.0340 loss: 0.7885 detection_loss_cls: 0.7885 2024/07/07 12:30:32 - mmengine - INFO - Iter(train) [ 51300/120000] base_lr: 1.2336e-04 lr: 1.3032e-05 eta: 23:10:41 time: 1.2295 data_time: 0.0663 memory: 6243 grad_norm: 1.0341 loss: 0.7881 detection_loss_cls: 0.7881 2024/07/07 12:31:32 - 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mmengine - INFO - Iter(train) [ 51850/120000] base_lr: 1.2197e-04 lr: 1.2906e-05 eta: 22:59:40 time: 1.2310 data_time: 0.0663 memory: 6243 grad_norm: 1.0348 loss: 0.7853 detection_loss_cls: 0.7853 2024/07/07 12:42:44 - mmengine - INFO - Iter(train) [ 51900/120000] base_lr: 1.2184e-04 lr: 1.2894e-05 eta: 22:58:40 time: 1.2311 data_time: 0.0663 memory: 6243 grad_norm: 1.0351 loss: 0.7859 detection_loss_cls: 0.7859 2024/07/07 12:43:45 - mmengine - INFO - Iter(train) [ 51950/120000] base_lr: 1.2171e-04 lr: 1.2883e-05 eta: 22:57:39 time: 1.2310 data_time: 0.0663 memory: 6243 grad_norm: 1.0370 loss: 0.7859 detection_loss_cls: 0.7859 2024/07/07 12:44:47 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 12:44:47 - mmengine - INFO - Iter(train) [ 52000/120000] base_lr: 1.2159e-04 lr: 1.2871e-05 eta: 22:56:40 time: 1.2313 data_time: 0.0663 memory: 6243 grad_norm: 1.0373 loss: 0.7854 detection_loss_cls: 0.7854 2024/07/07 12:44:47 - mmengine - INFO - Saving checkpoint at 52000 iterations 2024/07/07 12:45:55 - 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mmengine - INFO - Iter(train) [ 52550/120000] base_lr: 1.2019e-04 lr: 1.2744e-05 eta: 22:45:52 time: 1.2310 data_time: 0.0662 memory: 6243 grad_norm: 1.0393 loss: 0.7849 detection_loss_cls: 0.7849 2024/07/07 12:57:07 - mmengine - INFO - Iter(train) [ 52600/120000] base_lr: 1.2006e-04 lr: 1.2733e-05 eta: 22:44:52 time: 1.2311 data_time: 0.0662 memory: 6243 grad_norm: 1.0414 loss: 0.7851 detection_loss_cls: 0.7851 2024/07/07 12:58:08 - mmengine - INFO - Iter(train) [ 52650/120000] base_lr: 1.1994e-04 lr: 1.2721e-05 eta: 22:43:51 time: 1.2310 data_time: 0.0662 memory: 6243 grad_norm: 1.0416 loss: 0.7859 detection_loss_cls: 0.7859 2024/07/07 12:59:09 - mmengine - INFO - Iter(train) [ 52700/120000] base_lr: 1.1981e-04 lr: 1.2710e-05 eta: 22:42:50 time: 1.2308 data_time: 0.0662 memory: 6243 grad_norm: 1.0427 loss: 0.7856 detection_loss_cls: 0.7856 2024/07/07 13:00:10 - mmengine - INFO - Iter(train) [ 52750/120000] base_lr: 1.1968e-04 lr: 1.2698e-05 eta: 22:41:50 time: 1.2309 data_time: 0.0661 memory: 6243 grad_norm: 1.0431 loss: 0.7854 detection_loss_cls: 0.7854 2024/07/07 13:01:11 - 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mmengine - INFO - Saving checkpoint at 53000 iterations 2024/07/07 13:06:24 - mmengine - INFO - Iter(train) [ 53050/120000] base_lr: 1.1892e-04 lr: 1.2629e-05 eta: 22:36:03 time: 1.2311 data_time: 0.0663 memory: 6243 grad_norm: 1.0446 loss: 0.7866 detection_loss_cls: 0.7866 2024/07/07 13:07:25 - mmengine - INFO - Iter(train) [ 53100/120000] base_lr: 1.1879e-04 lr: 1.2617e-05 eta: 22:35:02 time: 1.2311 data_time: 0.0662 memory: 6243 grad_norm: 1.0443 loss: 0.7856 detection_loss_cls: 0.7856 2024/07/07 13:08:25 - mmengine - INFO - Iter(train) [ 53150/120000] base_lr: 1.1866e-04 lr: 1.2606e-05 eta: 22:34:00 time: 1.2309 data_time: 0.0662 memory: 6243 grad_norm: 1.0449 loss: 0.7847 detection_loss_cls: 0.7847 2024/07/07 13:09:26 - mmengine - INFO - Iter(train) [ 53200/120000] base_lr: 1.1853e-04 lr: 1.2594e-05 eta: 22:33:01 time: 1.2309 data_time: 0.0662 memory: 6243 grad_norm: 1.0452 loss: 0.7848 detection_loss_cls: 0.7848 2024/07/07 13:10:27 - mmengine - INFO - Iter(train) [ 53250/120000] base_lr: 1.1841e-04 lr: 1.2582e-05 eta: 22:31:59 time: 1.2308 data_time: 0.0662 memory: 6243 grad_norm: 1.0455 loss: 0.7844 detection_loss_cls: 0.7844 2024/07/07 13:11:28 - 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mmengine - INFO - Iter(train) [ 53550/120000] base_lr: 1.1764e-04 lr: 1.2513e-05 eta: 22:25:56 time: 1.2305 data_time: 0.0661 memory: 6243 grad_norm: 1.0476 loss: 0.7835 detection_loss_cls: 0.7835 2024/07/07 13:17:33 - mmengine - INFO - Iter(train) [ 53600/120000] base_lr: 1.1751e-04 lr: 1.2501e-05 eta: 22:24:56 time: 1.2303 data_time: 0.0661 memory: 6243 grad_norm: 1.0476 loss: 0.7834 detection_loss_cls: 0.7834 2024/07/07 13:18:34 - mmengine - INFO - Iter(train) [ 53650/120000] base_lr: 1.1738e-04 lr: 1.2490e-05 eta: 22:23:55 time: 1.2302 data_time: 0.0661 memory: 6243 grad_norm: 1.0483 loss: 0.7841 detection_loss_cls: 0.7841 2024/07/07 13:19:34 - mmengine - INFO - Iter(train) [ 53700/120000] base_lr: 1.1726e-04 lr: 1.2478e-05 eta: 22:22:54 time: 1.2303 data_time: 0.0661 memory: 6243 grad_norm: 1.0488 loss: 0.7846 detection_loss_cls: 0.7846 2024/07/07 13:20:34 - mmengine - INFO - Iter(train) [ 53750/120000] base_lr: 1.1713e-04 lr: 1.2466e-05 eta: 22:21:51 time: 1.2298 data_time: 0.0661 memory: 6243 grad_norm: 1.0491 loss: 0.7831 detection_loss_cls: 0.7831 2024/07/07 13:21:36 - mmengine - INFO - Iter(train) [ 53800/120000] base_lr: 1.1700e-04 lr: 1.2455e-05 eta: 22:20:52 time: 1.2299 data_time: 0.0661 memory: 6243 grad_norm: 1.0478 loss: 0.7837 detection_loss_cls: 0.7837 2024/07/07 13:22:36 - mmengine - INFO - Iter(train) [ 53850/120000] base_lr: 1.1687e-04 lr: 1.2443e-05 eta: 22:19:51 time: 1.2299 data_time: 0.0661 memory: 6243 grad_norm: 1.0493 loss: 0.7847 detection_loss_cls: 0.7847 2024/07/07 13:23:37 - mmengine - INFO - Iter(train) [ 53900/120000] base_lr: 1.1675e-04 lr: 1.2431e-05 eta: 22:18:50 time: 1.2297 data_time: 0.0661 memory: 6243 grad_norm: 1.0487 loss: 0.7854 detection_loss_cls: 0.7854 2024/07/07 13:24:39 - mmengine - INFO - Iter(train) [ 53950/120000] base_lr: 1.1662e-04 lr: 1.2420e-05 eta: 22:17:50 time: 1.2299 data_time: 0.0662 memory: 6243 grad_norm: 1.0486 loss: 0.7848 detection_loss_cls: 0.7848 2024/07/07 13:25:39 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 13:25:39 - mmengine - INFO - Iter(train) [ 54000/120000] base_lr: 1.1649e-04 lr: 1.2408e-05 eta: 22:16:49 time: 1.2299 data_time: 0.0661 memory: 6243 grad_norm: 1.0489 loss: 0.7844 detection_loss_cls: 0.7844 2024/07/07 13:25:39 - mmengine - INFO - Saving checkpoint at 54000 iterations 2024/07/07 13:26:47 - mmengine - INFO - Iter(train) [ 54050/120000] base_lr: 1.1636e-04 lr: 1.2397e-05 eta: 22:16:01 time: 1.2243 data_time: 0.0607 memory: 6243 grad_norm: 1.0492 loss: 0.7837 detection_loss_cls: 0.7837 2024/07/07 13:27:48 - mmengine - INFO - Iter(train) [ 54100/120000] base_lr: 1.1623e-04 lr: 1.2385e-05 eta: 22:15:00 time: 1.2245 data_time: 0.0607 memory: 6243 grad_norm: 1.0490 loss: 0.7837 detection_loss_cls: 0.7837 2024/07/07 13:28:48 - mmengine - INFO - Iter(train) [ 54150/120000] base_lr: 1.1611e-04 lr: 1.2373e-05 eta: 22:13:58 time: 1.2244 data_time: 0.0607 memory: 6243 grad_norm: 1.0479 loss: 0.7832 detection_loss_cls: 0.7832 2024/07/07 13:29:49 - mmengine - INFO - Iter(train) [ 54200/120000] base_lr: 1.1598e-04 lr: 1.2362e-05 eta: 22:12:58 time: 1.2244 data_time: 0.0607 memory: 6243 grad_norm: 1.0475 loss: 0.7833 detection_loss_cls: 0.7833 2024/07/07 13:30:50 - mmengine - INFO - Iter(train) [ 54250/120000] base_lr: 1.1585e-04 lr: 1.2350e-05 eta: 22:11:57 time: 1.2247 data_time: 0.0607 memory: 6243 grad_norm: 1.0472 loss: 0.7825 detection_loss_cls: 0.7825 2024/07/07 13:31:50 - mmengine - INFO - Iter(train) [ 54300/120000] base_lr: 1.1572e-04 lr: 1.2338e-05 eta: 22:10:55 time: 1.2246 data_time: 0.0607 memory: 6243 grad_norm: 1.0484 loss: 0.7829 detection_loss_cls: 0.7829 2024/07/07 13:32:50 - mmengine - INFO - Iter(train) [ 54350/120000] base_lr: 1.1559e-04 lr: 1.2327e-05 eta: 22:09:54 time: 1.2245 data_time: 0.0607 memory: 6243 grad_norm: 1.0483 loss: 0.7835 detection_loss_cls: 0.7835 2024/07/07 13:33:52 - mmengine - INFO - Iter(train) [ 54400/120000] base_lr: 1.1546e-04 lr: 1.2315e-05 eta: 22:08:55 time: 1.2249 data_time: 0.0607 memory: 6243 grad_norm: 1.0479 loss: 0.7827 detection_loss_cls: 0.7827 2024/07/07 13:34:53 - mmengine - INFO - Iter(train) [ 54450/120000] base_lr: 1.1534e-04 lr: 1.2303e-05 eta: 22:07:53 time: 1.2250 data_time: 0.0606 memory: 6243 grad_norm: 1.0477 loss: 0.7831 detection_loss_cls: 0.7831 2024/07/07 13:35:53 - mmengine - INFO - Iter(train) [ 54500/120000] base_lr: 1.1521e-04 lr: 1.2292e-05 eta: 22:06:52 time: 1.2249 data_time: 0.0607 memory: 6243 grad_norm: 1.0479 loss: 0.7829 detection_loss_cls: 0.7829 2024/07/07 13:36:54 - mmengine - INFO - Iter(train) [ 54550/120000] base_lr: 1.1508e-04 lr: 1.2280e-05 eta: 22:05:52 time: 1.2250 data_time: 0.0606 memory: 6243 grad_norm: 1.0478 loss: 0.7829 detection_loss_cls: 0.7829 2024/07/07 13:37:54 - mmengine - INFO - Iter(train) [ 54600/120000] base_lr: 1.1495e-04 lr: 1.2268e-05 eta: 22:04:50 time: 1.2249 data_time: 0.0606 memory: 6243 grad_norm: 1.0472 loss: 0.7831 detection_loss_cls: 0.7831 2024/07/07 13:38:55 - mmengine - INFO - Iter(train) [ 54650/120000] base_lr: 1.1482e-04 lr: 1.2257e-05 eta: 22:03:49 time: 1.2248 data_time: 0.0606 memory: 6243 grad_norm: 1.0472 loss: 0.7827 detection_loss_cls: 0.7827 2024/07/07 13:39:56 - mmengine - INFO - Iter(train) [ 54700/120000] base_lr: 1.1470e-04 lr: 1.2245e-05 eta: 22:02:48 time: 1.2250 data_time: 0.0606 memory: 6243 grad_norm: 1.0469 loss: 0.7815 detection_loss_cls: 0.7815 2024/07/07 13:40:56 - mmengine - INFO - Iter(train) [ 54750/120000] base_lr: 1.1457e-04 lr: 1.2233e-05 eta: 22:01:47 time: 1.2252 data_time: 0.0606 memory: 6243 grad_norm: 1.0465 loss: 0.7816 detection_loss_cls: 0.7816 2024/07/07 13:41:57 - mmengine - INFO - Iter(train) [ 54800/120000] base_lr: 1.1444e-04 lr: 1.2222e-05 eta: 22:00:46 time: 1.2250 data_time: 0.0606 memory: 6243 grad_norm: 1.0466 loss: 0.7816 detection_loss_cls: 0.7816 2024/07/07 13:42:58 - mmengine - INFO - Iter(train) [ 54850/120000] base_lr: 1.1431e-04 lr: 1.2210e-05 eta: 21:59:45 time: 1.2251 data_time: 0.0606 memory: 6243 grad_norm: 1.0484 loss: 0.7817 detection_loss_cls: 0.7817 2024/07/07 13:43:58 - mmengine - INFO - Iter(train) [ 54900/120000] base_lr: 1.1418e-04 lr: 1.2198e-05 eta: 21:58:44 time: 1.2250 data_time: 0.0606 memory: 6243 grad_norm: 1.0489 loss: 0.7812 detection_loss_cls: 0.7812 2024/07/07 13:44:58 - mmengine - INFO - Iter(train) [ 54950/120000] base_lr: 1.1405e-04 lr: 1.2187e-05 eta: 21:57:41 time: 1.2248 data_time: 0.0605 memory: 6243 grad_norm: 1.0497 loss: 0.7807 detection_loss_cls: 0.7807 2024/07/07 13:45:59 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 13:45:59 - mmengine - INFO - Iter(train) [ 55000/120000] base_lr: 1.1392e-04 lr: 1.2175e-05 eta: 21:56:41 time: 1.2250 data_time: 0.0606 memory: 6243 grad_norm: 1.0509 loss: 0.7818 detection_loss_cls: 0.7818 2024/07/07 13:45:59 - mmengine - INFO - Saving checkpoint at 55000 iterations 2024/07/07 13:46:47 - mmengine - INFO - Iter(val) [ 50/105] eta: 0:00:43 time: 0.8020 data_time: 0.0294 memory: 6808 2024/07/07 13:47:27 - mmengine - INFO - Iter(val) [100/105] eta: 0:00:03 time: 0.8019 data_time: 0.0293 memory: 6807 2024/07/07 13:47:32 - mmengine - INFO - Evaluating bbox... 2024/07/07 13:48:00 - mmengine - INFO - bbox_mAP_copypaste: 0.405 0.570 0.433 0.189 0.445 0.584 2024/07/07 13:48:00 - mmengine - INFO - Iter(val) [105/105] coco/bbox_mAP: 0.4050 coco/bbox_mAP_50: 0.5700 coco/bbox_mAP_75: 0.4330 coco/bbox_mAP_s: 0.1890 coco/bbox_mAP_m: 0.4450 coco/bbox_mAP_l: 0.5840 data_time: 0.0279 time: 0.7902 2024/07/07 13:49:01 - mmengine - INFO - Iter(train) [ 55050/120000] base_lr: 1.1380e-04 lr: 1.2163e-05 eta: 21:56:29 time: 1.2304 data_time: 0.0661 memory: 6808 grad_norm: 1.0509 loss: 0.7820 detection_loss_cls: 0.7820 2024/07/07 13:50:01 - mmengine - INFO - Iter(train) [ 55100/120000] base_lr: 1.1367e-04 lr: 1.2152e-05 eta: 21:55:28 time: 1.2302 data_time: 0.0661 memory: 6242 grad_norm: 1.0517 loss: 0.7822 detection_loss_cls: 0.7822 2024/07/07 13:51:02 - 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mmengine - INFO - Saving checkpoint at 58000 iterations 2024/07/07 14:50:13 - mmengine - INFO - Iter(train) [ 58050/120000] base_lr: 1.0605e-04 lr: 1.1459e-05 eta: 20:56:15 time: 1.2284 data_time: 0.0659 memory: 6242 grad_norm: 1.0457 loss: 0.7772 detection_loss_cls: 0.7772 2024/07/07 14:51:14 - mmengine - INFO - Iter(train) [ 58100/120000] base_lr: 1.0593e-04 lr: 1.1448e-05 eta: 20:55:15 time: 1.2284 data_time: 0.0658 memory: 6242 grad_norm: 1.0458 loss: 0.7763 detection_loss_cls: 0.7763 2024/07/07 14:52:16 - mmengine - INFO - Iter(train) [ 58150/120000] base_lr: 1.0580e-04 lr: 1.1436e-05 eta: 20:54:15 time: 1.2288 data_time: 0.0658 memory: 6242 grad_norm: 1.0458 loss: 0.7761 detection_loss_cls: 0.7761 2024/07/07 14:53:16 - mmengine - INFO - Iter(train) [ 58200/120000] base_lr: 1.0567e-04 lr: 1.1424e-05 eta: 20:53:13 time: 1.2287 data_time: 0.0658 memory: 6242 grad_norm: 1.0458 loss: 0.7748 detection_loss_cls: 0.7748 2024/07/07 14:54:18 - mmengine - INFO - Iter(train) [ 58250/120000] base_lr: 1.0554e-04 lr: 1.1412e-05 eta: 20:52:14 time: 1.2289 data_time: 0.0658 memory: 6242 grad_norm: 1.0461 loss: 0.7760 detection_loss_cls: 0.7760 2024/07/07 14:55:20 - 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mmengine - INFO - Saving checkpoint at 59000 iterations 2024/07/07 15:10:47 - mmengine - INFO - Iter(train) [ 59050/120000] base_lr: 1.0346e-04 lr: 1.1224e-05 eta: 20:36:22 time: 1.2262 data_time: 0.0603 memory: 6242 grad_norm: 1.0457 loss: 0.7724 detection_loss_cls: 0.7724 2024/07/07 15:11:48 - mmengine - INFO - Iter(train) [ 59100/120000] base_lr: 1.0334e-04 lr: 1.1212e-05 eta: 20:35:22 time: 1.2264 data_time: 0.0603 memory: 6242 grad_norm: 1.0446 loss: 0.7722 detection_loss_cls: 0.7722 2024/07/07 15:12:50 - mmengine - INFO - Iter(train) [ 59150/120000] base_lr: 1.0321e-04 lr: 1.1200e-05 eta: 20:34:22 time: 1.2266 data_time: 0.0603 memory: 6242 grad_norm: 1.0446 loss: 0.7727 detection_loss_cls: 0.7727 2024/07/07 15:13:52 - mmengine - INFO - Iter(train) [ 59200/120000] base_lr: 1.0308e-04 lr: 1.1189e-05 eta: 20:33:23 time: 1.2269 data_time: 0.0603 memory: 6242 grad_norm: 1.0451 loss: 0.7725 detection_loss_cls: 0.7725 2024/07/07 15:14:53 - mmengine - INFO - Iter(train) [ 59250/120000] base_lr: 1.0295e-04 lr: 1.1177e-05 eta: 20:32:23 time: 1.2270 data_time: 0.0604 memory: 6242 grad_norm: 1.0453 loss: 0.7740 detection_loss_cls: 0.7740 2024/07/07 15:15:54 - mmengine - INFO - Iter(train) [ 59300/120000] base_lr: 1.0282e-04 lr: 1.1165e-05 eta: 20:31:22 time: 1.2268 data_time: 0.0604 memory: 6242 grad_norm: 1.0450 loss: 0.7735 detection_loss_cls: 0.7735 2024/07/07 15:16:55 - mmengine - INFO - Iter(train) [ 59350/120000] base_lr: 1.0269e-04 lr: 1.1153e-05 eta: 20:30:22 time: 1.2271 data_time: 0.0605 memory: 6242 grad_norm: 1.0450 loss: 0.7747 detection_loss_cls: 0.7747 2024/07/07 15:17:57 - mmengine - INFO - Iter(train) [ 59400/120000] base_lr: 1.0256e-04 lr: 1.1142e-05 eta: 20:29:21 time: 1.2272 data_time: 0.0605 memory: 6242 grad_norm: 1.0450 loss: 0.7748 detection_loss_cls: 0.7748 2024/07/07 15:18:59 - mmengine - INFO - Iter(train) [ 59450/120000] base_lr: 1.0243e-04 lr: 1.1130e-05 eta: 20:28:22 time: 1.2273 data_time: 0.0605 memory: 6242 grad_norm: 1.0443 loss: 0.7752 detection_loss_cls: 0.7752 2024/07/07 15:20:01 - mmengine - INFO - Iter(train) [ 59500/120000] base_lr: 1.0230e-04 lr: 1.1118e-05 eta: 20:27:24 time: 1.2278 data_time: 0.0605 memory: 6242 grad_norm: 1.0446 loss: 0.7746 detection_loss_cls: 0.7746 2024/07/07 15:21:03 - mmengine - INFO - Iter(train) [ 59550/120000] base_lr: 1.0217e-04 lr: 1.1106e-05 eta: 20:26:23 time: 1.2277 data_time: 0.0605 memory: 6242 grad_norm: 1.0460 loss: 0.7742 detection_loss_cls: 0.7742 2024/07/07 15:22:04 - mmengine - INFO - Iter(train) [ 59600/120000] base_lr: 1.0204e-04 lr: 1.1094e-05 eta: 20:25:23 time: 1.2276 data_time: 0.0605 memory: 6242 grad_norm: 1.0462 loss: 0.7750 detection_loss_cls: 0.7750 2024/07/07 15:23:06 - mmengine - INFO - Iter(train) [ 59650/120000] base_lr: 1.0191e-04 lr: 1.1083e-05 eta: 20:24:23 time: 1.2278 data_time: 0.0606 memory: 6242 grad_norm: 1.0462 loss: 0.7749 detection_loss_cls: 0.7749 2024/07/07 15:24:07 - mmengine - INFO - Iter(train) [ 59700/120000] base_lr: 1.0178e-04 lr: 1.1071e-05 eta: 20:23:23 time: 1.2279 data_time: 0.0606 memory: 6242 grad_norm: 1.0461 loss: 0.7749 detection_loss_cls: 0.7749 2024/07/07 15:25:08 - mmengine - INFO - Iter(train) [ 59750/120000] base_lr: 1.0165e-04 lr: 1.1059e-05 eta: 20:22:23 time: 1.2278 data_time: 0.0606 memory: 6242 grad_norm: 1.0460 loss: 0.7751 detection_loss_cls: 0.7751 2024/07/07 15:26:10 - mmengine - INFO - Iter(train) [ 59800/120000] base_lr: 1.0152e-04 lr: 1.1047e-05 eta: 20:21:23 time: 1.2280 data_time: 0.0607 memory: 6242 grad_norm: 1.0465 loss: 0.7764 detection_loss_cls: 0.7764 2024/07/07 15:27:11 - mmengine - INFO - Iter(train) [ 59850/120000] base_lr: 1.0139e-04 lr: 1.1036e-05 eta: 20:20:23 time: 1.2278 data_time: 0.0607 memory: 6242 grad_norm: 1.0466 loss: 0.7761 detection_loss_cls: 0.7761 2024/07/07 15:28:12 - mmengine - INFO - Iter(train) [ 59900/120000] base_lr: 1.0126e-04 lr: 1.1024e-05 eta: 20:19:21 time: 1.2276 data_time: 0.0606 memory: 6242 grad_norm: 1.0461 loss: 0.7759 detection_loss_cls: 0.7759 2024/07/07 15:29:14 - mmengine - INFO - Iter(train) [ 59950/120000] base_lr: 1.0113e-04 lr: 1.1012e-05 eta: 20:18:22 time: 1.2279 data_time: 0.0605 memory: 6242 grad_norm: 1.0485 loss: 0.7747 detection_loss_cls: 0.7747 2024/07/07 15:30:15 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 15:30:15 - mmengine - INFO - Iter(train) [ 60000/120000] base_lr: 1.0100e-04 lr: 1.1000e-05 eta: 20:17:22 time: 1.2280 data_time: 0.0605 memory: 6242 grad_norm: 1.0484 loss: 0.7744 detection_loss_cls: 0.7744 2024/07/07 15:30:15 - mmengine - INFO - Saving checkpoint at 60000 iterations 2024/07/07 15:31:03 - mmengine - INFO - Iter(val) [ 50/105] eta: 0:00:44 time: 0.8016 data_time: 0.0293 memory: 6807 2024/07/07 15:31:43 - mmengine - INFO - Iter(val) [100/105] eta: 0:00:04 time: 0.8015 data_time: 0.0293 memory: 6808 2024/07/07 15:31:49 - mmengine - INFO - Evaluating bbox... 2024/07/07 15:32:16 - mmengine - INFO - bbox_mAP_copypaste: 0.410 0.576 0.439 0.195 0.451 0.589 2024/07/07 15:32:16 - mmengine - INFO - Iter(val) [105/105] coco/bbox_mAP: 0.4100 coco/bbox_mAP_50: 0.5760 coco/bbox_mAP_75: 0.4390 coco/bbox_mAP_s: 0.1950 coco/bbox_mAP_m: 0.4510 coco/bbox_mAP_l: 0.5890 data_time: 0.0285 time: 0.7944 2024/07/07 15:33:17 - mmengine - INFO - Iter(train) [ 60050/120000] base_lr: 1.0087e-04 lr: 1.0988e-05 eta: 20:17:01 time: 1.2335 data_time: 0.0661 memory: 6805 grad_norm: 1.0482 loss: 0.7747 detection_loss_cls: 0.7747 2024/07/07 15:34:18 - mmengine - INFO - Iter(train) [ 60100/120000] base_lr: 1.0074e-04 lr: 1.0977e-05 eta: 20:16:00 time: 1.2337 data_time: 0.0660 memory: 6237 grad_norm: 1.0506 loss: 0.7737 detection_loss_cls: 0.7737 2024/07/07 15:35:18 - mmengine - INFO - Iter(train) [ 60150/120000] base_lr: 1.0061e-04 lr: 1.0965e-05 eta: 20:14:58 time: 1.2335 data_time: 0.0661 memory: 6237 grad_norm: 1.0506 loss: 0.7739 detection_loss_cls: 0.7739 2024/07/07 15:36:18 - mmengine - INFO - Iter(train) [ 60200/120000] base_lr: 1.0048e-04 lr: 1.0953e-05 eta: 20:13:57 time: 1.2334 data_time: 0.0661 memory: 6237 grad_norm: 1.0512 loss: 0.7730 detection_loss_cls: 0.7730 2024/07/07 15:37:20 - mmengine - INFO - Iter(train) [ 60250/120000] base_lr: 1.0035e-04 lr: 1.0941e-05 eta: 20:12:56 time: 1.2338 data_time: 0.0661 memory: 6237 grad_norm: 1.0512 loss: 0.7724 detection_loss_cls: 0.7724 2024/07/07 15:38:20 - mmengine - INFO - Iter(train) [ 60300/120000] base_lr: 1.0023e-04 lr: 1.0930e-05 eta: 20:11:55 time: 1.2338 data_time: 0.0661 memory: 6237 grad_norm: 1.0512 loss: 0.7728 detection_loss_cls: 0.7728 2024/07/07 15:39:21 - mmengine - INFO - Iter(train) [ 60350/120000] base_lr: 1.0010e-04 lr: 1.0918e-05 eta: 20:10:53 time: 1.2335 data_time: 0.0661 memory: 6237 grad_norm: 1.0518 loss: 0.7719 detection_loss_cls: 0.7719 2024/07/07 15:40:22 - mmengine - INFO - Iter(train) [ 60400/120000] base_lr: 9.9966e-05 lr: 1.0906e-05 eta: 20:09:53 time: 1.2336 data_time: 0.0661 memory: 6237 grad_norm: 1.0524 loss: 0.7714 detection_loss_cls: 0.7714 2024/07/07 15:41:22 - mmengine - INFO - Iter(train) [ 60450/120000] base_lr: 9.9836e-05 lr: 1.0894e-05 eta: 20:08:51 time: 1.2334 data_time: 0.0661 memory: 6237 grad_norm: 1.0529 loss: 0.7711 detection_loss_cls: 0.7711 2024/07/07 15:42:23 - mmengine - INFO - Iter(train) [ 60500/120000] base_lr: 9.9707e-05 lr: 1.0882e-05 eta: 20:07:50 time: 1.2334 data_time: 0.0660 memory: 6237 grad_norm: 1.0533 loss: 0.7706 detection_loss_cls: 0.7706 2024/07/07 15:43:24 - mmengine - INFO - Iter(train) [ 60550/120000] base_lr: 9.9577e-05 lr: 1.0871e-05 eta: 20:06:50 time: 1.2338 data_time: 0.0661 memory: 6237 grad_norm: 1.0530 loss: 0.7703 detection_loss_cls: 0.7703 2024/07/07 15:44:25 - mmengine - INFO - Iter(train) [ 60600/120000] base_lr: 9.9448e-05 lr: 1.0859e-05 eta: 20:05:48 time: 1.2337 data_time: 0.0660 memory: 6237 grad_norm: 1.0526 loss: 0.7688 detection_loss_cls: 0.7688 2024/07/07 15:45:25 - mmengine - INFO - Iter(train) [ 60650/120000] base_lr: 9.9318e-05 lr: 1.0847e-05 eta: 20:04:47 time: 1.2336 data_time: 0.0660 memory: 6237 grad_norm: 1.0526 loss: 0.7698 detection_loss_cls: 0.7698 2024/07/07 15:46:27 - mmengine - INFO - Iter(train) [ 60700/120000] base_lr: 9.9188e-05 lr: 1.0835e-05 eta: 20:03:46 time: 1.2337 data_time: 0.0660 memory: 6237 grad_norm: 1.0516 loss: 0.7695 detection_loss_cls: 0.7695 2024/07/07 15:47:27 - mmengine - INFO - Iter(train) [ 60750/120000] base_lr: 9.9059e-05 lr: 1.0824e-05 eta: 20:02:45 time: 1.2335 data_time: 0.0661 memory: 6237 grad_norm: 1.0518 loss: 0.7701 detection_loss_cls: 0.7701 2024/07/07 15:48:28 - mmengine - INFO - Iter(train) [ 60800/120000] base_lr: 9.8929e-05 lr: 1.0812e-05 eta: 20:01:43 time: 1.2334 data_time: 0.0661 memory: 6237 grad_norm: 1.0517 loss: 0.7692 detection_loss_cls: 0.7692 2024/07/07 15:49:29 - mmengine - INFO - Iter(train) [ 60850/120000] base_lr: 9.8800e-05 lr: 1.0800e-05 eta: 20:00:43 time: 1.2336 data_time: 0.0661 memory: 6237 grad_norm: 1.0518 loss: 0.7696 detection_loss_cls: 0.7696 2024/07/07 15:50:29 - mmengine - INFO - Iter(train) [ 60900/120000] base_lr: 9.8670e-05 lr: 1.0788e-05 eta: 19:59:41 time: 1.2337 data_time: 0.0660 memory: 6237 grad_norm: 1.0509 loss: 0.7695 detection_loss_cls: 0.7695 2024/07/07 15:51:30 - mmengine - INFO - Iter(train) [ 60950/120000] base_lr: 9.8541e-05 lr: 1.0776e-05 eta: 19:58:41 time: 1.2336 data_time: 0.0660 memory: 6237 grad_norm: 1.0499 loss: 0.7687 detection_loss_cls: 0.7687 2024/07/07 15:52:31 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 15:52:31 - mmengine - INFO - Iter(train) [ 61000/120000] base_lr: 9.8411e-05 lr: 1.0765e-05 eta: 19:57:40 time: 1.2337 data_time: 0.0660 memory: 6237 grad_norm: 1.0500 loss: 0.7675 detection_loss_cls: 0.7675 2024/07/07 15:52:31 - mmengine - INFO - Saving checkpoint at 61000 iterations 2024/07/07 15:53:40 - mmengine - INFO - Iter(train) [ 61050/120000] base_lr: 9.8282e-05 lr: 1.0753e-05 eta: 19:56:49 time: 1.2338 data_time: 0.0659 memory: 6237 grad_norm: 1.0498 loss: 0.7672 detection_loss_cls: 0.7672 2024/07/07 15:54:41 - mmengine - INFO - Iter(train) [ 61100/120000] base_lr: 9.8152e-05 lr: 1.0741e-05 eta: 19:55:48 time: 1.2339 data_time: 0.0660 memory: 6237 grad_norm: 1.0502 loss: 0.7673 detection_loss_cls: 0.7673 2024/07/07 15:55:42 - mmengine - INFO - Iter(train) [ 61150/120000] base_lr: 9.8022e-05 lr: 1.0729e-05 eta: 19:54:48 time: 1.2342 data_time: 0.0660 memory: 6237 grad_norm: 1.0493 loss: 0.7674 detection_loss_cls: 0.7674 2024/07/07 15:56:43 - mmengine - INFO - Iter(train) [ 61200/120000] base_lr: 9.7893e-05 lr: 1.0718e-05 eta: 19:53:47 time: 1.2342 data_time: 0.0659 memory: 6237 grad_norm: 1.0511 loss: 0.7663 detection_loss_cls: 0.7663 2024/07/07 15:57:44 - mmengine - INFO - Iter(train) [ 61250/120000] base_lr: 9.7763e-05 lr: 1.0706e-05 eta: 19:52:46 time: 1.2343 data_time: 0.0660 memory: 6237 grad_norm: 1.0516 loss: 0.7669 detection_loss_cls: 0.7669 2024/07/07 15:58:45 - mmengine - INFO - Iter(train) [ 61300/120000] base_lr: 9.7634e-05 lr: 1.0694e-05 eta: 19:51:45 time: 1.2345 data_time: 0.0659 memory: 6237 grad_norm: 1.0516 loss: 0.7669 detection_loss_cls: 0.7669 2024/07/07 15:59:46 - 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mmengine - INFO - Iter(train) [ 61850/120000] base_lr: 9.6210e-05 lr: 1.0565e-05 eta: 19:40:39 time: 1.2357 data_time: 0.0659 memory: 6237 grad_norm: 1.0502 loss: 0.7641 detection_loss_cls: 0.7641 2024/07/07 16:11:00 - mmengine - INFO - Iter(train) [ 61900/120000] base_lr: 9.6080e-05 lr: 1.0553e-05 eta: 19:39:39 time: 1.2361 data_time: 0.0659 memory: 6237 grad_norm: 1.0531 loss: 0.7640 detection_loss_cls: 0.7640 2024/07/07 16:12:00 - mmengine - INFO - Iter(train) [ 61950/120000] base_lr: 9.5951e-05 lr: 1.0541e-05 eta: 19:38:37 time: 1.2359 data_time: 0.0658 memory: 6237 grad_norm: 1.0530 loss: 0.7639 detection_loss_cls: 0.7639 2024/07/07 16:13:01 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 16:13:01 - mmengine - INFO - Iter(train) [ 62000/120000] base_lr: 9.5821e-05 lr: 1.0529e-05 eta: 19:37:37 time: 1.2360 data_time: 0.0659 memory: 6237 grad_norm: 1.0527 loss: 0.7642 detection_loss_cls: 0.7642 2024/07/07 16:13:01 - mmengine - INFO - Saving checkpoint at 62000 iterations 2024/07/07 16:14:10 - 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mmengine - INFO - Saving checkpoint at 63000 iterations 2024/07/07 16:34:40 - mmengine - INFO - Iter(train) [ 63050/120000] base_lr: 9.3106e-05 lr: 1.0282e-05 eta: 19:16:41 time: 1.2352 data_time: 0.0661 memory: 6237 grad_norm: 1.0519 loss: 0.7621 detection_loss_cls: 0.7621 2024/07/07 16:35:42 - mmengine - INFO - Iter(train) [ 63100/120000] base_lr: 9.2977e-05 lr: 1.0271e-05 eta: 19:15:41 time: 1.2352 data_time: 0.0661 memory: 6237 grad_norm: 1.0522 loss: 0.7625 detection_loss_cls: 0.7625 2024/07/07 16:36:43 - mmengine - INFO - Iter(train) [ 63150/120000] base_lr: 9.2848e-05 lr: 1.0259e-05 eta: 19:14:40 time: 1.2350 data_time: 0.0661 memory: 6237 grad_norm: 1.0517 loss: 0.7622 detection_loss_cls: 0.7622 2024/07/07 16:37:44 - mmengine - INFO - Iter(train) [ 63200/120000] base_lr: 9.2718e-05 lr: 1.0247e-05 eta: 19:13:39 time: 1.2347 data_time: 0.0661 memory: 6237 grad_norm: 1.0529 loss: 0.7630 detection_loss_cls: 0.7630 2024/07/07 16:38:45 - mmengine - INFO - Iter(train) [ 63250/120000] base_lr: 9.2589e-05 lr: 1.0235e-05 eta: 19:12:38 time: 1.2347 data_time: 0.0661 memory: 6237 grad_norm: 1.0528 loss: 0.7626 detection_loss_cls: 0.7626 2024/07/07 16:39:46 - 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mmengine - INFO - Iter(train) [ 63800/120000] base_lr: 9.1170e-05 lr: 1.0106e-05 eta: 19:01:29 time: 1.2334 data_time: 0.0659 memory: 6237 grad_norm: 1.0540 loss: 0.7578 detection_loss_cls: 0.7578 2024/07/07 16:50:58 - mmengine - INFO - Iter(train) [ 63850/120000] base_lr: 9.1041e-05 lr: 1.0095e-05 eta: 19:00:29 time: 1.2335 data_time: 0.0659 memory: 6237 grad_norm: 1.0542 loss: 0.7576 detection_loss_cls: 0.7576 2024/07/07 16:51:59 - mmengine - INFO - Iter(train) [ 63900/120000] base_lr: 9.0912e-05 lr: 1.0083e-05 eta: 18:59:28 time: 1.2336 data_time: 0.0658 memory: 6237 grad_norm: 1.0541 loss: 0.7565 detection_loss_cls: 0.7565 2024/07/07 16:53:00 - mmengine - INFO - Iter(train) [ 63950/120000] base_lr: 9.0783e-05 lr: 1.0071e-05 eta: 18:58:28 time: 1.2334 data_time: 0.0658 memory: 6237 grad_norm: 1.0517 loss: 0.7561 detection_loss_cls: 0.7561 2024/07/07 16:54:02 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 16:54:02 - mmengine - INFO - Iter(train) [ 64000/120000] base_lr: 9.0654e-05 lr: 1.0059e-05 eta: 18:57:28 time: 1.2336 data_time: 0.0658 memory: 6237 grad_norm: 1.0517 loss: 0.7561 detection_loss_cls: 0.7561 2024/07/07 16:54:02 - mmengine - INFO - Saving checkpoint at 64000 iterations 2024/07/07 16:55:11 - mmengine - INFO - Iter(train) [ 64050/120000] base_lr: 9.0525e-05 lr: 1.0048e-05 eta: 18:56:36 time: 1.2282 data_time: 0.0604 memory: 6237 grad_norm: 1.0519 loss: 0.7572 detection_loss_cls: 0.7572 2024/07/07 16:56:12 - mmengine - INFO - Iter(train) [ 64100/120000] base_lr: 9.0397e-05 lr: 1.0036e-05 eta: 18:55:35 time: 1.2282 data_time: 0.0605 memory: 6237 grad_norm: 1.0492 loss: 0.7577 detection_loss_cls: 0.7577 2024/07/07 16:57:13 - mmengine - INFO - Iter(train) [ 64150/120000] base_lr: 9.0268e-05 lr: 1.0024e-05 eta: 18:54:34 time: 1.2285 data_time: 0.0605 memory: 6237 grad_norm: 1.0492 loss: 0.7578 detection_loss_cls: 0.7578 2024/07/07 16:58:14 - mmengine - INFO - Iter(train) [ 64200/120000] base_lr: 9.0139e-05 lr: 1.0013e-05 eta: 18:53:34 time: 1.2286 data_time: 0.0605 memory: 6237 grad_norm: 1.0494 loss: 0.7587 detection_loss_cls: 0.7587 2024/07/07 16:59:15 - mmengine - INFO - Iter(train) [ 64250/120000] base_lr: 9.0010e-05 lr: 1.0001e-05 eta: 18:52:33 time: 1.2285 data_time: 0.0605 memory: 6237 grad_norm: 1.0493 loss: 0.7591 detection_loss_cls: 0.7591 2024/07/07 17:00:16 - mmengine - INFO - Iter(train) [ 64300/120000] base_lr: 8.9881e-05 lr: 9.9892e-06 eta: 18:51:32 time: 1.2287 data_time: 0.0605 memory: 6237 grad_norm: 1.0494 loss: 0.7591 detection_loss_cls: 0.7591 2024/07/07 17:01:18 - mmengine - INFO - Iter(train) [ 64350/120000] base_lr: 8.9753e-05 lr: 9.9775e-06 eta: 18:50:31 time: 1.2289 data_time: 0.0605 memory: 6237 grad_norm: 1.0496 loss: 0.7600 detection_loss_cls: 0.7600 2024/07/07 17:02:18 - mmengine - INFO - Iter(train) [ 64400/120000] base_lr: 8.9624e-05 lr: 9.9658e-06 eta: 18:49:30 time: 1.2289 data_time: 0.0606 memory: 6237 grad_norm: 1.0497 loss: 0.7605 detection_loss_cls: 0.7605 2024/07/07 17:03:20 - mmengine - INFO - Iter(train) [ 64450/120000] base_lr: 8.9495e-05 lr: 9.9541e-06 eta: 18:48:30 time: 1.2290 data_time: 0.0606 memory: 6237 grad_norm: 1.0504 loss: 0.7603 detection_loss_cls: 0.7603 2024/07/07 17:04:21 - mmengine - INFO - Iter(train) [ 64500/120000] base_lr: 8.9366e-05 lr: 9.9424e-06 eta: 18:47:29 time: 1.2292 data_time: 0.0606 memory: 6237 grad_norm: 1.0504 loss: 0.7611 detection_loss_cls: 0.7611 2024/07/07 17:05:22 - mmengine - INFO - Iter(train) [ 64550/120000] base_lr: 8.9238e-05 lr: 9.9307e-06 eta: 18:46:29 time: 1.2291 data_time: 0.0606 memory: 6237 grad_norm: 1.0506 loss: 0.7614 detection_loss_cls: 0.7614 2024/07/07 17:06:23 - mmengine - INFO - Iter(train) [ 64600/120000] base_lr: 8.9109e-05 lr: 9.9190e-06 eta: 18:45:28 time: 1.2294 data_time: 0.0607 memory: 6237 grad_norm: 1.0507 loss: 0.7621 detection_loss_cls: 0.7621 2024/07/07 17:07:24 - mmengine - INFO - Iter(train) [ 64650/120000] base_lr: 8.8980e-05 lr: 9.9073e-06 eta: 18:44:27 time: 1.2294 data_time: 0.0607 memory: 6237 grad_norm: 1.0509 loss: 0.7621 detection_loss_cls: 0.7621 2024/07/07 17:08:26 - mmengine - INFO - Iter(train) [ 64700/120000] base_lr: 8.8852e-05 lr: 9.8956e-06 eta: 18:43:27 time: 1.2295 data_time: 0.0608 memory: 6237 grad_norm: 1.0528 loss: 0.7624 detection_loss_cls: 0.7624 2024/07/07 17:09:27 - mmengine - INFO - Iter(train) [ 64750/120000] base_lr: 8.8723e-05 lr: 9.8839e-06 eta: 18:42:26 time: 1.2297 data_time: 0.0607 memory: 6237 grad_norm: 1.0536 loss: 0.7610 detection_loss_cls: 0.7610 2024/07/07 17:10:28 - mmengine - INFO - Iter(train) [ 64800/120000] base_lr: 8.8595e-05 lr: 9.8722e-06 eta: 18:41:25 time: 1.2299 data_time: 0.0608 memory: 6237 grad_norm: 1.0537 loss: 0.7615 detection_loss_cls: 0.7615 2024/07/07 17:11:30 - mmengine - INFO - Iter(train) [ 64850/120000] base_lr: 8.8466e-05 lr: 9.8605e-06 eta: 18:40:25 time: 1.2300 data_time: 0.0608 memory: 6237 grad_norm: 1.0541 loss: 0.7614 detection_loss_cls: 0.7614 2024/07/07 17:12:31 - mmengine - INFO - Iter(train) [ 64900/120000] base_lr: 8.8337e-05 lr: 9.8489e-06 eta: 18:39:24 time: 1.2301 data_time: 0.0608 memory: 6237 grad_norm: 1.0545 loss: 0.7607 detection_loss_cls: 0.7607 2024/07/07 17:13:33 - mmengine - INFO - Iter(train) [ 64950/120000] base_lr: 8.8209e-05 lr: 9.8372e-06 eta: 18:38:24 time: 1.2303 data_time: 0.0608 memory: 6237 grad_norm: 1.0552 loss: 0.7607 detection_loss_cls: 0.7607 2024/07/07 17:14:34 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 17:14:34 - mmengine - INFO - Iter(train) [ 65000/120000] base_lr: 8.8080e-05 lr: 9.8255e-06 eta: 18:37:23 time: 1.2304 data_time: 0.0608 memory: 6237 grad_norm: 1.0554 loss: 0.7620 detection_loss_cls: 0.7620 2024/07/07 17:14:34 - mmengine - INFO - Saving checkpoint at 65000 iterations 2024/07/07 17:15:22 - mmengine - INFO - Iter(val) [ 50/105] eta: 0:00:44 time: 0.8013 data_time: 0.0293 memory: 6807 2024/07/07 17:16:02 - mmengine - INFO - Iter(val) [100/105] eta: 0:00:04 time: 0.8012 data_time: 0.0292 memory: 6811 2024/07/07 17:16:08 - mmengine - INFO - Evaluating bbox... 2024/07/07 17:16:34 - mmengine - INFO - bbox_mAP_copypaste: 0.413 0.581 0.445 0.198 0.459 0.592 2024/07/07 17:16:35 - mmengine - INFO - Iter(val) [105/105] coco/bbox_mAP: 0.4130 coco/bbox_mAP_50: 0.5810 coco/bbox_mAP_75: 0.4450 coco/bbox_mAP_s: 0.1980 coco/bbox_mAP_m: 0.4590 coco/bbox_mAP_l: 0.5920 data_time: 0.0287 time: 0.7925 2024/07/07 17:17:36 - mmengine - INFO - Iter(train) [ 65050/120000] base_lr: 8.7952e-05 lr: 9.8138e-06 eta: 18:36:56 time: 1.2359 data_time: 0.0663 memory: 6803 grad_norm: 1.0561 loss: 0.7628 detection_loss_cls: 0.7628 2024/07/07 17:18:37 - mmengine - INFO - Iter(train) [ 65100/120000] base_lr: 8.7824e-05 lr: 9.8021e-06 eta: 18:35:55 time: 1.2359 data_time: 0.0663 memory: 6241 grad_norm: 1.0562 loss: 0.7625 detection_loss_cls: 0.7625 2024/07/07 17:19:39 - 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mmengine - INFO - Saving checkpoint at 68000 iterations 2024/07/07 18:19:11 - mmengine - INFO - Iter(train) [ 68050/120000] base_lr: 8.0293e-05 lr: 9.1175e-06 eta: 17:36:32 time: 1.2368 data_time: 0.0668 memory: 6241 grad_norm: 1.0605 loss: 0.7628 detection_loss_cls: 0.7628 2024/07/07 18:20:11 - mmengine - INFO - Iter(train) [ 68100/120000] base_lr: 8.0166e-05 lr: 9.1060e-06 eta: 17:35:30 time: 1.2366 data_time: 0.0668 memory: 6241 grad_norm: 1.0618 loss: 0.7632 detection_loss_cls: 0.7632 2024/07/07 18:21:12 - mmengine - INFO - Iter(train) [ 68150/120000] base_lr: 8.0039e-05 lr: 9.0945e-06 eta: 17:34:29 time: 1.2365 data_time: 0.0669 memory: 6241 grad_norm: 1.0619 loss: 0.7635 detection_loss_cls: 0.7635 2024/07/07 18:22:13 - mmengine - INFO - Iter(train) [ 68200/120000] base_lr: 7.9913e-05 lr: 9.0830e-06 eta: 17:33:28 time: 1.2364 data_time: 0.0668 memory: 6241 grad_norm: 1.0622 loss: 0.7627 detection_loss_cls: 0.7627 2024/07/07 18:23:13 - mmengine - INFO - Iter(train) [ 68250/120000] base_lr: 7.9786e-05 lr: 9.0714e-06 eta: 17:32:26 time: 1.2362 data_time: 0.0669 memory: 6241 grad_norm: 1.0625 loss: 0.7634 detection_loss_cls: 0.7634 2024/07/07 18:24:14 - 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mmengine - INFO - Saving checkpoint at 69000 iterations 2024/07/07 18:39:30 - mmengine - INFO - Iter(train) [ 69050/120000] base_lr: 7.7765e-05 lr: 8.8878e-06 eta: 17:16:10 time: 1.2281 data_time: 0.0614 memory: 6241 grad_norm: 1.0636 loss: 0.7627 detection_loss_cls: 0.7627 2024/07/07 18:40:31 - mmengine - INFO - Iter(train) [ 69100/120000] base_lr: 7.7639e-05 lr: 8.8763e-06 eta: 17:15:09 time: 1.2281 data_time: 0.0614 memory: 6241 grad_norm: 1.0636 loss: 0.7617 detection_loss_cls: 0.7617 2024/07/07 18:41:31 - mmengine - INFO - Iter(train) [ 69150/120000] base_lr: 7.7514e-05 lr: 8.8649e-06 eta: 17:14:07 time: 1.2277 data_time: 0.0614 memory: 6241 grad_norm: 1.0634 loss: 0.7622 detection_loss_cls: 0.7622 2024/07/07 18:42:33 - mmengine - INFO - Iter(train) [ 69200/120000] base_lr: 7.7388e-05 lr: 8.8534e-06 eta: 17:13:07 time: 1.2278 data_time: 0.0614 memory: 6241 grad_norm: 1.0631 loss: 0.7620 detection_loss_cls: 0.7620 2024/07/07 18:43:34 - mmengine - INFO - Iter(train) [ 69250/120000] base_lr: 7.7262e-05 lr: 8.8420e-06 eta: 17:12:06 time: 1.2277 data_time: 0.0614 memory: 6241 grad_norm: 1.0630 loss: 0.7617 detection_loss_cls: 0.7617 2024/07/07 18:44:35 - mmengine - INFO - Iter(train) [ 69300/120000] base_lr: 7.7136e-05 lr: 8.8306e-06 eta: 17:11:05 time: 1.2278 data_time: 0.0614 memory: 6241 grad_norm: 1.0636 loss: 0.7614 detection_loss_cls: 0.7614 2024/07/07 18:45:37 - mmengine - INFO - Iter(train) [ 69350/120000] base_lr: 7.7010e-05 lr: 8.8191e-06 eta: 17:10:05 time: 1.2281 data_time: 0.0614 memory: 6241 grad_norm: 1.0635 loss: 0.7614 detection_loss_cls: 0.7614 2024/07/07 18:46:38 - mmengine - INFO - Iter(train) [ 69400/120000] base_lr: 7.6885e-05 lr: 8.8077e-06 eta: 17:09:05 time: 1.2282 data_time: 0.0614 memory: 6241 grad_norm: 1.0637 loss: 0.7622 detection_loss_cls: 0.7622 2024/07/07 18:47:39 - mmengine - INFO - Iter(train) [ 69450/120000] base_lr: 7.6759e-05 lr: 8.7963e-06 eta: 17:08:03 time: 1.2281 data_time: 0.0614 memory: 6241 grad_norm: 1.0643 loss: 0.7624 detection_loss_cls: 0.7624 2024/07/07 18:48:41 - mmengine - INFO - Iter(train) [ 69500/120000] base_lr: 7.6633e-05 lr: 8.7848e-06 eta: 17:07:02 time: 1.2280 data_time: 0.0614 memory: 6241 grad_norm: 1.0637 loss: 0.7623 detection_loss_cls: 0.7623 2024/07/07 18:49:42 - mmengine - INFO - Iter(train) [ 69550/120000] base_lr: 7.6508e-05 lr: 8.7734e-06 eta: 17:06:02 time: 1.2281 data_time: 0.0614 memory: 6241 grad_norm: 1.0634 loss: 0.7613 detection_loss_cls: 0.7613 2024/07/07 18:50:43 - mmengine - INFO - Iter(train) [ 69600/120000] base_lr: 7.6382e-05 lr: 8.7620e-06 eta: 17:05:00 time: 1.2280 data_time: 0.0613 memory: 6241 grad_norm: 1.0638 loss: 0.7599 detection_loss_cls: 0.7599 2024/07/07 18:51:44 - mmengine - INFO - Iter(train) [ 69650/120000] base_lr: 7.6257e-05 lr: 8.7506e-06 eta: 17:04:00 time: 1.2280 data_time: 0.0614 memory: 6241 grad_norm: 1.0636 loss: 0.7598 detection_loss_cls: 0.7598 2024/07/07 18:52:45 - mmengine - INFO - Iter(train) [ 69700/120000] base_lr: 7.6131e-05 lr: 8.7392e-06 eta: 17:02:59 time: 1.2281 data_time: 0.0614 memory: 6241 grad_norm: 1.0636 loss: 0.7592 detection_loss_cls: 0.7592 2024/07/07 18:53:46 - mmengine - INFO - Iter(train) [ 69750/120000] base_lr: 7.6006e-05 lr: 8.7278e-06 eta: 17:01:57 time: 1.2279 data_time: 0.0613 memory: 6241 grad_norm: 1.0634 loss: 0.7585 detection_loss_cls: 0.7585 2024/07/07 18:54:47 - mmengine - INFO - Iter(train) [ 69800/120000] base_lr: 7.5880e-05 lr: 8.7164e-06 eta: 17:00:57 time: 1.2279 data_time: 0.0613 memory: 6241 grad_norm: 1.0639 loss: 0.7579 detection_loss_cls: 0.7579 2024/07/07 18:55:48 - mmengine - INFO - Iter(train) [ 69850/120000] base_lr: 7.5755e-05 lr: 8.7050e-06 eta: 16:59:56 time: 1.2280 data_time: 0.0613 memory: 6241 grad_norm: 1.0620 loss: 0.7583 detection_loss_cls: 0.7583 2024/07/07 18:56:49 - mmengine - INFO - Iter(train) [ 69900/120000] base_lr: 7.5630e-05 lr: 8.6936e-06 eta: 16:58:55 time: 1.2281 data_time: 0.0613 memory: 6241 grad_norm: 1.0622 loss: 0.7577 detection_loss_cls: 0.7577 2024/07/07 18:57:51 - mmengine - INFO - Iter(train) [ 69950/120000] base_lr: 7.5505e-05 lr: 8.6822e-06 eta: 16:57:54 time: 1.2281 data_time: 0.0613 memory: 6241 grad_norm: 1.0624 loss: 0.7578 detection_loss_cls: 0.7578 2024/07/07 18:58:52 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 18:58:52 - mmengine - INFO - Iter(train) [ 70000/120000] base_lr: 7.5379e-05 lr: 8.6709e-06 eta: 16:56:54 time: 1.2282 data_time: 0.0613 memory: 6241 grad_norm: 1.0630 loss: 0.7584 detection_loss_cls: 0.7584 2024/07/07 18:58:52 - mmengine - INFO - Saving checkpoint at 70000 iterations 2024/07/07 18:59:41 - mmengine - INFO - Iter(val) [ 50/105] eta: 0:00:44 time: 0.8014 data_time: 0.0292 memory: 6807 2024/07/07 19:00:21 - mmengine - INFO - Iter(val) [100/105] eta: 0:00:04 time: 0.8014 data_time: 0.0292 memory: 6806 2024/07/07 19:00:27 - mmengine - INFO - Evaluating bbox... 2024/07/07 19:00:53 - mmengine - INFO - bbox_mAP_copypaste: 0.415 0.585 0.444 0.200 0.461 0.594 2024/07/07 19:00:53 - mmengine - INFO - Iter(val) [105/105] coco/bbox_mAP: 0.4150 coco/bbox_mAP_50: 0.5850 coco/bbox_mAP_75: 0.4440 coco/bbox_mAP_s: 0.2000 coco/bbox_mAP_m: 0.4610 coco/bbox_mAP_l: 0.5940 data_time: 0.0279 time: 0.7989 2024/07/07 19:01:53 - mmengine - INFO - Iter(train) [ 70050/120000] base_lr: 7.5254e-05 lr: 8.6595e-06 eta: 16:56:18 time: 1.2332 data_time: 0.0666 memory: 6805 grad_norm: 1.0628 loss: 0.7582 detection_loss_cls: 0.7582 2024/07/07 19:02:54 - mmengine - INFO - Iter(train) [ 70100/120000] base_lr: 7.5129e-05 lr: 8.6481e-06 eta: 16:55:17 time: 1.2329 data_time: 0.0666 memory: 6243 grad_norm: 1.0622 loss: 0.7576 detection_loss_cls: 0.7576 2024/07/07 19:03:55 - mmengine - INFO - Iter(train) [ 70150/120000] base_lr: 7.5004e-05 lr: 8.6367e-06 eta: 16:54:16 time: 1.2330 data_time: 0.0666 memory: 6243 grad_norm: 1.0619 loss: 0.7571 detection_loss_cls: 0.7571 2024/07/07 19:04:55 - mmengine - INFO - Iter(train) [ 70200/120000] base_lr: 7.4879e-05 lr: 8.6254e-06 eta: 16:53:14 time: 1.2329 data_time: 0.0666 memory: 6243 grad_norm: 1.0620 loss: 0.7562 detection_loss_cls: 0.7562 2024/07/07 19:05:57 - mmengine - INFO - Iter(train) [ 70250/120000] base_lr: 7.4754e-05 lr: 8.6140e-06 eta: 16:52:14 time: 1.2331 data_time: 0.0667 memory: 6243 grad_norm: 1.0619 loss: 0.7566 detection_loss_cls: 0.7566 2024/07/07 19:06:59 - mmengine - INFO - Iter(train) [ 70300/120000] base_lr: 7.4629e-05 lr: 8.6027e-06 eta: 16:51:13 time: 1.2332 data_time: 0.0667 memory: 6243 grad_norm: 1.0611 loss: 0.7571 detection_loss_cls: 0.7571 2024/07/07 19:08:00 - mmengine - INFO - Iter(train) [ 70350/120000] base_lr: 7.4504e-05 lr: 8.5913e-06 eta: 16:50:12 time: 1.2333 data_time: 0.0667 memory: 6243 grad_norm: 1.0612 loss: 0.7574 detection_loss_cls: 0.7574 2024/07/07 19:09:01 - mmengine - INFO - Iter(train) [ 70400/120000] base_lr: 7.4379e-05 lr: 8.5799e-06 eta: 16:49:12 time: 1.2331 data_time: 0.0668 memory: 6243 grad_norm: 1.0610 loss: 0.7580 detection_loss_cls: 0.7580 2024/07/07 19:10:02 - mmengine - INFO - Iter(train) [ 70450/120000] base_lr: 7.4255e-05 lr: 8.5686e-06 eta: 16:48:10 time: 1.2331 data_time: 0.0667 memory: 6243 grad_norm: 1.0613 loss: 0.7570 detection_loss_cls: 0.7570 2024/07/07 19:11:03 - mmengine - INFO - Iter(train) [ 70500/120000] base_lr: 7.4130e-05 lr: 8.5573e-06 eta: 16:47:09 time: 1.2332 data_time: 0.0667 memory: 6243 grad_norm: 1.0614 loss: 0.7568 detection_loss_cls: 0.7568 2024/07/07 19:12:05 - mmengine - INFO - Iter(train) [ 70550/120000] base_lr: 7.4005e-05 lr: 8.5459e-06 eta: 16:46:09 time: 1.2333 data_time: 0.0667 memory: 6243 grad_norm: 1.0611 loss: 0.7572 detection_loss_cls: 0.7572 2024/07/07 19:13:06 - mmengine - INFO - Iter(train) [ 70600/120000] base_lr: 7.3881e-05 lr: 8.5346e-06 eta: 16:45:08 time: 1.2333 data_time: 0.0667 memory: 6243 grad_norm: 1.0610 loss: 0.7572 detection_loss_cls: 0.7572 2024/07/07 19:14:07 - mmengine - INFO - Iter(train) [ 70650/120000] base_lr: 7.3756e-05 lr: 8.5233e-06 eta: 16:44:06 time: 1.2332 data_time: 0.0668 memory: 6243 grad_norm: 1.0608 loss: 0.7578 detection_loss_cls: 0.7578 2024/07/07 19:15:08 - mmengine - INFO - Iter(train) [ 70700/120000] base_lr: 7.3631e-05 lr: 8.5119e-06 eta: 16:43:05 time: 1.2329 data_time: 0.0668 memory: 6243 grad_norm: 1.0612 loss: 0.7568 detection_loss_cls: 0.7568 2024/07/07 19:16:09 - mmengine - INFO - Iter(train) [ 70750/120000] base_lr: 7.3507e-05 lr: 8.5006e-06 eta: 16:42:04 time: 1.2330 data_time: 0.0668 memory: 6243 grad_norm: 1.0610 loss: 0.7580 detection_loss_cls: 0.7580 2024/07/07 19:17:10 - mmengine - INFO - Iter(train) [ 70800/120000] base_lr: 7.3382e-05 lr: 8.4893e-06 eta: 16:41:03 time: 1.2329 data_time: 0.0667 memory: 6243 grad_norm: 1.0615 loss: 0.7573 detection_loss_cls: 0.7573 2024/07/07 19:18:11 - mmengine - INFO - Iter(train) [ 70850/120000] base_lr: 7.3258e-05 lr: 8.4780e-06 eta: 16:40:02 time: 1.2329 data_time: 0.0667 memory: 6243 grad_norm: 1.0616 loss: 0.7567 detection_loss_cls: 0.7567 2024/07/07 19:19:12 - mmengine - INFO - Iter(train) [ 70900/120000] base_lr: 7.3134e-05 lr: 8.4667e-06 eta: 16:39:01 time: 1.2329 data_time: 0.0667 memory: 6243 grad_norm: 1.0615 loss: 0.7564 detection_loss_cls: 0.7564 2024/07/07 19:20:13 - mmengine - INFO - Iter(train) [ 70950/120000] base_lr: 7.3009e-05 lr: 8.4554e-06 eta: 16:38:01 time: 1.2330 data_time: 0.0667 memory: 6243 grad_norm: 1.0613 loss: 0.7562 detection_loss_cls: 0.7562 2024/07/07 19:21:14 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 19:21:14 - mmengine - INFO - Iter(train) [ 71000/120000] base_lr: 7.2885e-05 lr: 8.4441e-06 eta: 16:36:59 time: 1.2329 data_time: 0.0667 memory: 6243 grad_norm: 1.0609 loss: 0.7559 detection_loss_cls: 0.7559 2024/07/07 19:21:14 - mmengine - INFO - Saving checkpoint at 71000 iterations 2024/07/07 19:22:22 - mmengine - INFO - Iter(train) [ 71050/120000] base_lr: 7.2761e-05 lr: 8.4328e-06 eta: 16:36:05 time: 1.2327 data_time: 0.0666 memory: 6243 grad_norm: 1.0611 loss: 0.7561 detection_loss_cls: 0.7561 2024/07/07 19:23:22 - mmengine - INFO - Iter(train) [ 71100/120000] base_lr: 7.2637e-05 lr: 8.4215e-06 eta: 16:35:03 time: 1.2324 data_time: 0.0666 memory: 6243 grad_norm: 1.0613 loss: 0.7561 detection_loss_cls: 0.7561 2024/07/07 19:24:23 - mmengine - INFO - Iter(train) [ 71150/120000] base_lr: 7.2512e-05 lr: 8.4102e-06 eta: 16:34:02 time: 1.2322 data_time: 0.0666 memory: 6243 grad_norm: 1.0613 loss: 0.7557 detection_loss_cls: 0.7557 2024/07/07 19:25:24 - mmengine - INFO - Iter(train) [ 71200/120000] base_lr: 7.2388e-05 lr: 8.3989e-06 eta: 16:33:00 time: 1.2321 data_time: 0.0666 memory: 6243 grad_norm: 1.0611 loss: 0.7556 detection_loss_cls: 0.7556 2024/07/07 19:26:24 - mmengine - INFO - Iter(train) [ 71250/120000] base_lr: 7.2264e-05 lr: 8.3877e-06 eta: 16:31:58 time: 1.2318 data_time: 0.0666 memory: 6243 grad_norm: 1.0607 loss: 0.7567 detection_loss_cls: 0.7567 2024/07/07 19:27:25 - mmengine - INFO - Iter(train) [ 71300/120000] base_lr: 7.2140e-05 lr: 8.3764e-06 eta: 16:30:57 time: 1.2316 data_time: 0.0666 memory: 6243 grad_norm: 1.0614 loss: 0.7548 detection_loss_cls: 0.7548 2024/07/07 19:28:25 - mmengine - INFO - Iter(train) [ 71350/120000] base_lr: 7.2016e-05 lr: 8.3651e-06 eta: 16:29:55 time: 1.2314 data_time: 0.0665 memory: 6243 grad_norm: 1.0613 loss: 0.7537 detection_loss_cls: 0.7537 2024/07/07 19:29:26 - mmengine - INFO - Iter(train) [ 71400/120000] base_lr: 7.1892e-05 lr: 8.3539e-06 eta: 16:28:54 time: 1.2313 data_time: 0.0664 memory: 6243 grad_norm: 1.0610 loss: 0.7529 detection_loss_cls: 0.7529 2024/07/07 19:30:26 - mmengine - INFO - Iter(train) [ 71450/120000] base_lr: 7.1769e-05 lr: 8.3426e-06 eta: 16:27:52 time: 1.2310 data_time: 0.0664 memory: 6243 grad_norm: 1.0609 loss: 0.7530 detection_loss_cls: 0.7530 2024/07/07 19:31:27 - mmengine - INFO - Iter(train) [ 71500/120000] base_lr: 7.1645e-05 lr: 8.3314e-06 eta: 16:26:51 time: 1.2308 data_time: 0.0664 memory: 6243 grad_norm: 1.0607 loss: 0.7524 detection_loss_cls: 0.7524 2024/07/07 19:32:27 - mmengine - INFO - Iter(train) [ 71550/120000] base_lr: 7.1521e-05 lr: 8.3201e-06 eta: 16:25:49 time: 1.2307 data_time: 0.0664 memory: 6243 grad_norm: 1.0611 loss: 0.7520 detection_loss_cls: 0.7520 2024/07/07 19:33:28 - 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mmengine - INFO - Iter(train) [ 71850/120000] base_lr: 7.0780e-05 lr: 8.2527e-06 eta: 16:19:40 time: 1.2296 data_time: 0.0663 memory: 6243 grad_norm: 1.0617 loss: 0.7505 detection_loss_cls: 0.7505 2024/07/07 19:39:31 - mmengine - INFO - Iter(train) [ 71900/120000] base_lr: 7.0656e-05 lr: 8.2415e-06 eta: 16:18:39 time: 1.2293 data_time: 0.0662 memory: 6243 grad_norm: 1.0624 loss: 0.7500 detection_loss_cls: 0.7500 2024/07/07 19:40:32 - mmengine - INFO - Iter(train) [ 71950/120000] base_lr: 7.0533e-05 lr: 8.2303e-06 eta: 16:17:38 time: 1.2294 data_time: 0.0662 memory: 6243 grad_norm: 1.0622 loss: 0.7499 detection_loss_cls: 0.7499 2024/07/07 19:41:33 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 19:41:33 - mmengine - INFO - Iter(train) [ 72000/120000] base_lr: 7.0410e-05 lr: 8.2191e-06 eta: 16:16:36 time: 1.2293 data_time: 0.0662 memory: 6243 grad_norm: 1.0624 loss: 0.7491 detection_loss_cls: 0.7491 2024/07/07 19:41:33 - mmengine - INFO - Saving checkpoint at 72000 iterations 2024/07/07 19:42:42 - 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mmengine - INFO - Saving checkpoint at 73000 iterations 2024/07/07 20:03:11 - mmengine - INFO - Iter(train) [ 73050/120000] base_lr: 6.7833e-05 lr: 7.9849e-06 eta: 15:55:28 time: 1.2320 data_time: 0.0659 memory: 6243 grad_norm: 1.0579 loss: 0.7437 detection_loss_cls: 0.7437 2024/07/07 20:04:12 - mmengine - INFO - Iter(train) [ 73100/120000] base_lr: 6.7711e-05 lr: 7.9738e-06 eta: 15:54:27 time: 1.2319 data_time: 0.0659 memory: 6243 grad_norm: 1.0579 loss: 0.7437 detection_loss_cls: 0.7437 2024/07/07 20:05:14 - mmengine - INFO - Iter(train) [ 73150/120000] base_lr: 6.7589e-05 lr: 7.9627e-06 eta: 15:53:26 time: 1.2322 data_time: 0.0659 memory: 6243 grad_norm: 1.0576 loss: 0.7440 detection_loss_cls: 0.7440 2024/07/07 20:06:14 - mmengine - INFO - Iter(train) [ 73200/120000] base_lr: 6.7467e-05 lr: 7.9516e-06 eta: 15:52:25 time: 1.2320 data_time: 0.0659 memory: 6243 grad_norm: 1.0581 loss: 0.7446 detection_loss_cls: 0.7446 2024/07/07 20:07:15 - mmengine - INFO - Iter(train) [ 73250/120000] base_lr: 6.7345e-05 lr: 7.9405e-06 eta: 15:51:24 time: 1.2320 data_time: 0.0659 memory: 6243 grad_norm: 1.0586 loss: 0.7445 detection_loss_cls: 0.7445 2024/07/07 20:08:17 - 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mmengine - INFO - Iter(train) [ 73800/120000] base_lr: 6.6008e-05 lr: 7.8189e-06 eta: 15:40:15 time: 1.2325 data_time: 0.0660 memory: 6243 grad_norm: 1.0561 loss: 0.7447 detection_loss_cls: 0.7447 2024/07/07 20:19:32 - mmengine - INFO - Iter(train) [ 73850/120000] base_lr: 6.5887e-05 lr: 7.8079e-06 eta: 15:39:14 time: 1.2327 data_time: 0.0661 memory: 6243 grad_norm: 1.0563 loss: 0.7449 detection_loss_cls: 0.7449 2024/07/07 20:20:34 - mmengine - INFO - Iter(train) [ 73900/120000] base_lr: 6.5766e-05 lr: 7.7969e-06 eta: 15:38:13 time: 1.2327 data_time: 0.0660 memory: 6243 grad_norm: 1.0570 loss: 0.7448 detection_loss_cls: 0.7448 2024/07/07 20:21:34 - mmengine - INFO - Iter(train) [ 73950/120000] base_lr: 6.5645e-05 lr: 7.7859e-06 eta: 15:37:12 time: 1.2325 data_time: 0.0660 memory: 6243 grad_norm: 1.0571 loss: 0.7447 detection_loss_cls: 0.7447 2024/07/07 20:22:36 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 20:22:36 - mmengine - INFO - Iter(train) [ 74000/120000] base_lr: 6.5524e-05 lr: 7.7749e-06 eta: 15:36:11 time: 1.2325 data_time: 0.0660 memory: 6243 grad_norm: 1.0567 loss: 0.7450 detection_loss_cls: 0.7450 2024/07/07 20:22:36 - mmengine - INFO - Saving checkpoint at 74000 iterations 2024/07/07 20:23:44 - mmengine - INFO - Iter(train) [ 74050/120000] base_lr: 6.5403e-05 lr: 7.7639e-06 eta: 15:35:16 time: 1.2274 data_time: 0.0607 memory: 6243 grad_norm: 1.0564 loss: 0.7452 detection_loss_cls: 0.7452 2024/07/07 20:24:44 - mmengine - INFO - Iter(train) [ 74100/120000] base_lr: 6.5282e-05 lr: 7.7529e-06 eta: 15:34:14 time: 1.2273 data_time: 0.0606 memory: 6243 grad_norm: 1.0563 loss: 0.7447 detection_loss_cls: 0.7447 2024/07/07 20:25:45 - mmengine - INFO - Iter(train) [ 74150/120000] base_lr: 6.5161e-05 lr: 7.7419e-06 eta: 15:33:13 time: 1.2272 data_time: 0.0606 memory: 6243 grad_norm: 1.0565 loss: 0.7446 detection_loss_cls: 0.7446 2024/07/07 20:26:46 - mmengine - INFO - Iter(train) [ 74200/120000] base_lr: 6.5041e-05 lr: 7.7310e-06 eta: 15:32:12 time: 1.2274 data_time: 0.0606 memory: 6243 grad_norm: 1.0572 loss: 0.7444 detection_loss_cls: 0.7444 2024/07/07 20:27:46 - mmengine - INFO - Iter(train) [ 74250/120000] base_lr: 6.4920e-05 lr: 7.7200e-06 eta: 15:31:10 time: 1.2270 data_time: 0.0606 memory: 6243 grad_norm: 1.0575 loss: 0.7438 detection_loss_cls: 0.7438 2024/07/07 20:28:47 - mmengine - INFO - Iter(train) [ 74300/120000] base_lr: 6.4799e-05 lr: 7.7090e-06 eta: 15:30:08 time: 1.2267 data_time: 0.0605 memory: 6243 grad_norm: 1.0581 loss: 0.7431 detection_loss_cls: 0.7431 2024/07/07 20:29:47 - mmengine - INFO - Iter(train) [ 74350/120000] base_lr: 6.4679e-05 lr: 7.6981e-06 eta: 15:29:07 time: 1.2264 data_time: 0.0605 memory: 6243 grad_norm: 1.0585 loss: 0.7422 detection_loss_cls: 0.7422 2024/07/07 20:30:47 - mmengine - INFO - Iter(train) [ 74400/120000] base_lr: 6.4558e-05 lr: 7.6871e-06 eta: 15:28:05 time: 1.2261 data_time: 0.0604 memory: 6243 grad_norm: 1.0591 loss: 0.7414 detection_loss_cls: 0.7414 2024/07/07 20:31:48 - mmengine - INFO - Iter(train) [ 74450/120000] base_lr: 6.4438e-05 lr: 7.6761e-06 eta: 15:27:04 time: 1.2261 data_time: 0.0604 memory: 6243 grad_norm: 1.0591 loss: 0.7408 detection_loss_cls: 0.7408 2024/07/07 20:32:48 - mmengine - INFO - Iter(train) [ 74500/120000] base_lr: 6.4317e-05 lr: 7.6652e-06 eta: 15:26:02 time: 1.2259 data_time: 0.0604 memory: 6243 grad_norm: 1.0596 loss: 0.7405 detection_loss_cls: 0.7405 2024/07/07 20:33:49 - mmengine - INFO - Iter(train) [ 74550/120000] base_lr: 6.4197e-05 lr: 7.6543e-06 eta: 15:25:01 time: 1.2257 data_time: 0.0604 memory: 6243 grad_norm: 1.0601 loss: 0.7401 detection_loss_cls: 0.7401 2024/07/07 20:34:50 - mmengine - INFO - Iter(train) [ 74600/120000] base_lr: 6.4077e-05 lr: 7.6433e-06 eta: 15:24:00 time: 1.2257 data_time: 0.0604 memory: 6243 grad_norm: 1.0597 loss: 0.7404 detection_loss_cls: 0.7404 2024/07/07 20:35:51 - mmengine - INFO - Iter(train) [ 74650/120000] base_lr: 6.3956e-05 lr: 7.6324e-06 eta: 15:22:58 time: 1.2257 data_time: 0.0604 memory: 6243 grad_norm: 1.0600 loss: 0.7396 detection_loss_cls: 0.7396 2024/07/07 20:36:50 - mmengine - INFO - Iter(train) [ 74700/120000] base_lr: 6.3836e-05 lr: 7.6215e-06 eta: 15:21:56 time: 1.2254 data_time: 0.0603 memory: 6243 grad_norm: 1.0600 loss: 0.7388 detection_loss_cls: 0.7388 2024/07/07 20:37:51 - mmengine - INFO - Iter(train) [ 74750/120000] base_lr: 6.3716e-05 lr: 7.6106e-06 eta: 15:20:55 time: 1.2253 data_time: 0.0604 memory: 6243 grad_norm: 1.0601 loss: 0.7391 detection_loss_cls: 0.7391 2024/07/07 20:38:52 - mmengine - INFO - Iter(train) [ 74800/120000] base_lr: 6.3596e-05 lr: 7.5997e-06 eta: 15:19:54 time: 1.2253 data_time: 0.0604 memory: 6243 grad_norm: 1.0598 loss: 0.7391 detection_loss_cls: 0.7391 2024/07/07 20:39:52 - mmengine - INFO - Iter(train) [ 74850/120000] base_lr: 6.3476e-05 lr: 7.5887e-06 eta: 15:18:52 time: 1.2249 data_time: 0.0604 memory: 6243 grad_norm: 1.0603 loss: 0.7393 detection_loss_cls: 0.7393 2024/07/07 20:40:52 - mmengine - INFO - Iter(train) [ 74900/120000] base_lr: 6.3356e-05 lr: 7.5778e-06 eta: 15:17:50 time: 1.2248 data_time: 0.0604 memory: 6243 grad_norm: 1.0603 loss: 0.7400 detection_loss_cls: 0.7400 2024/07/07 20:41:53 - mmengine - INFO - Iter(train) [ 74950/120000] base_lr: 6.3236e-05 lr: 7.5670e-06 eta: 15:16:49 time: 1.2245 data_time: 0.0603 memory: 6243 grad_norm: 1.0603 loss: 0.7384 detection_loss_cls: 0.7384 2024/07/07 20:42:53 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 20:42:53 - mmengine - INFO - Iter(train) [ 75000/120000] base_lr: 6.3117e-05 lr: 7.5561e-06 eta: 15:15:47 time: 1.2243 data_time: 0.0604 memory: 6243 grad_norm: 1.0606 loss: 0.7391 detection_loss_cls: 0.7391 2024/07/07 20:42:53 - mmengine - INFO - Saving checkpoint at 75000 iterations 2024/07/07 20:43:41 - mmengine - INFO - Iter(val) [ 50/105] eta: 0:00:44 time: 0.8011 data_time: 0.0291 memory: 6807 2024/07/07 20:44:22 - mmengine - INFO - Iter(val) [100/105] eta: 0:00:04 time: 0.8014 data_time: 0.0291 memory: 6809 2024/07/07 20:44:27 - mmengine - INFO - Evaluating bbox... 2024/07/07 20:44:54 - mmengine - INFO - bbox_mAP_copypaste: 0.416 0.584 0.442 0.201 0.462 0.596 2024/07/07 20:44:54 - mmengine - INFO - Iter(val) [105/105] coco/bbox_mAP: 0.4160 coco/bbox_mAP_50: 0.5840 coco/bbox_mAP_75: 0.4420 coco/bbox_mAP_s: 0.2010 coco/bbox_mAP_m: 0.4620 coco/bbox_mAP_l: 0.5960 data_time: 0.0287 time: 0.7981 2024/07/07 20:45:55 - mmengine - INFO - Iter(train) [ 75050/120000] base_lr: 6.2997e-05 lr: 7.5452e-06 eta: 15:15:08 time: 1.2298 data_time: 0.0658 memory: 6802 grad_norm: 1.0616 loss: 0.7404 detection_loss_cls: 0.7404 2024/07/07 20:46:56 - mmengine - INFO - Iter(train) [ 75100/120000] base_lr: 6.2877e-05 lr: 7.5343e-06 eta: 15:14:07 time: 1.2301 data_time: 0.0658 memory: 6240 grad_norm: 1.0618 loss: 0.7410 detection_loss_cls: 0.7410 2024/07/07 20:47:57 - 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mmengine - INFO - Iter(train) [ 76850/120000] base_lr: 5.8733e-05 lr: 7.1575e-06 eta: 14:38:31 time: 1.2314 data_time: 0.0659 memory: 6240 grad_norm: 1.0697 loss: 0.7381 detection_loss_cls: 0.7381 2024/07/07 21:23:38 - mmengine - INFO - Iter(train) [ 76900/120000] base_lr: 5.8616e-05 lr: 7.1469e-06 eta: 14:37:30 time: 1.2312 data_time: 0.0658 memory: 6240 grad_norm: 1.0695 loss: 0.7370 detection_loss_cls: 0.7370 2024/07/07 21:24:39 - mmengine - INFO - Iter(train) [ 76950/120000] base_lr: 5.8499e-05 lr: 7.1362e-06 eta: 14:36:28 time: 1.2311 data_time: 0.0658 memory: 6240 grad_norm: 1.0693 loss: 0.7373 detection_loss_cls: 0.7373 2024/07/07 21:25:39 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 21:25:39 - mmengine - INFO - Iter(train) [ 77000/120000] base_lr: 5.8382e-05 lr: 7.1256e-06 eta: 14:35:27 time: 1.2308 data_time: 0.0658 memory: 6240 grad_norm: 1.0692 loss: 0.7366 detection_loss_cls: 0.7366 2024/07/07 21:25:39 - mmengine - INFO - Saving checkpoint at 77000 iterations 2024/07/07 21:26:48 - 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mmengine - INFO - Saving checkpoint at 78000 iterations 2024/07/07 21:47:08 - mmengine - INFO - Iter(train) [ 78050/120000] base_lr: 5.5942e-05 lr: 6.9038e-06 eta: 14:14:08 time: 1.2276 data_time: 0.0660 memory: 6240 grad_norm: 1.0738 loss: 0.7396 detection_loss_cls: 0.7396 2024/07/07 21:48:09 - mmengine - INFO - Iter(train) [ 78100/120000] base_lr: 5.5826e-05 lr: 6.8933e-06 eta: 14:13:07 time: 1.2277 data_time: 0.0660 memory: 6240 grad_norm: 1.0745 loss: 0.7399 detection_loss_cls: 0.7399 2024/07/07 21:49:09 - mmengine - INFO - Iter(train) [ 78150/120000] base_lr: 5.5711e-05 lr: 6.8828e-06 eta: 14:12:05 time: 1.2277 data_time: 0.0660 memory: 6240 grad_norm: 1.0747 loss: 0.7396 detection_loss_cls: 0.7396 2024/07/07 21:50:11 - mmengine - INFO - Iter(train) [ 78200/120000] base_lr: 5.5596e-05 lr: 6.8724e-06 eta: 14:11:05 time: 1.2279 data_time: 0.0661 memory: 6240 grad_norm: 1.0739 loss: 0.7403 detection_loss_cls: 0.7403 2024/07/07 21:51:11 - mmengine - INFO - Iter(train) [ 78250/120000] base_lr: 5.5481e-05 lr: 6.8619e-06 eta: 14:10:03 time: 1.2279 data_time: 0.0661 memory: 6240 grad_norm: 1.0738 loss: 0.7406 detection_loss_cls: 0.7406 2024/07/07 21:52:12 - 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mmengine - INFO - Saving checkpoint at 79000 iterations 2024/07/07 22:07:35 - mmengine - INFO - Iter(train) [ 79050/120000] base_lr: 5.3650e-05 lr: 6.6954e-06 eta: 13:53:49 time: 1.2245 data_time: 0.0608 memory: 6240 grad_norm: 1.0756 loss: 0.7387 detection_loss_cls: 0.7387 2024/07/07 22:08:36 - mmengine - INFO - Iter(train) [ 79100/120000] base_lr: 5.3536e-05 lr: 6.6851e-06 eta: 13:52:48 time: 1.2245 data_time: 0.0608 memory: 6240 grad_norm: 1.0757 loss: 0.7381 detection_loss_cls: 0.7381 2024/07/07 22:09:36 - mmengine - INFO - Iter(train) [ 79150/120000] base_lr: 5.3422e-05 lr: 6.6748e-06 eta: 13:51:47 time: 1.2245 data_time: 0.0608 memory: 6240 grad_norm: 1.0763 loss: 0.7379 detection_loss_cls: 0.7379 2024/07/07 22:10:37 - mmengine - INFO - Iter(train) [ 79200/120000] base_lr: 5.3309e-05 lr: 6.6644e-06 eta: 13:50:45 time: 1.2242 data_time: 0.0608 memory: 6240 grad_norm: 1.0768 loss: 0.7371 detection_loss_cls: 0.7371 2024/07/07 22:11:38 - mmengine - INFO - Iter(train) [ 79250/120000] base_lr: 5.3195e-05 lr: 6.6541e-06 eta: 13:49:44 time: 1.2242 data_time: 0.0607 memory: 6240 grad_norm: 1.0767 loss: 0.7359 detection_loss_cls: 0.7359 2024/07/07 22:12:38 - mmengine - INFO - Iter(train) [ 79300/120000] base_lr: 5.3082e-05 lr: 6.6438e-06 eta: 13:48:43 time: 1.2242 data_time: 0.0607 memory: 6240 grad_norm: 1.0770 loss: 0.7360 detection_loss_cls: 0.7360 2024/07/07 22:13:39 - mmengine - INFO - Iter(train) [ 79350/120000] base_lr: 5.2968e-05 lr: 6.6335e-06 eta: 13:47:41 time: 1.2240 data_time: 0.0607 memory: 6240 grad_norm: 1.0778 loss: 0.7363 detection_loss_cls: 0.7363 2024/07/07 22:14:40 - mmengine - INFO - Iter(train) [ 79400/120000] base_lr: 5.2855e-05 lr: 6.6232e-06 eta: 13:46:40 time: 1.2241 data_time: 0.0608 memory: 6240 grad_norm: 1.0783 loss: 0.7362 detection_loss_cls: 0.7362 2024/07/07 22:15:41 - mmengine - INFO - Iter(train) [ 79450/120000] base_lr: 5.2742e-05 lr: 6.6129e-06 eta: 13:45:39 time: 1.2242 data_time: 0.0608 memory: 6240 grad_norm: 1.0790 loss: 0.7368 detection_loss_cls: 0.7368 2024/07/07 22:16:41 - mmengine - INFO - Iter(train) [ 79500/120000] base_lr: 5.2629e-05 lr: 6.6026e-06 eta: 13:44:38 time: 1.2241 data_time: 0.0608 memory: 6240 grad_norm: 1.0793 loss: 0.7373 detection_loss_cls: 0.7373 2024/07/07 22:17:43 - mmengine - INFO - Iter(train) [ 79550/120000] base_lr: 5.2516e-05 lr: 6.5923e-06 eta: 13:43:37 time: 1.2242 data_time: 0.0608 memory: 6240 grad_norm: 1.0793 loss: 0.7360 detection_loss_cls: 0.7360 2024/07/07 22:18:44 - mmengine - INFO - Iter(train) [ 79600/120000] base_lr: 5.2403e-05 lr: 6.5821e-06 eta: 13:42:36 time: 1.2242 data_time: 0.0608 memory: 6240 grad_norm: 1.0795 loss: 0.7366 detection_loss_cls: 0.7366 2024/07/07 22:19:44 - mmengine - INFO - Iter(train) [ 79650/120000] base_lr: 5.2290e-05 lr: 6.5718e-06 eta: 13:41:34 time: 1.2241 data_time: 0.0608 memory: 6240 grad_norm: 1.0817 loss: 0.7364 detection_loss_cls: 0.7364 2024/07/07 22:20:45 - mmengine - INFO - Iter(train) [ 79700/120000] base_lr: 5.2177e-05 lr: 6.5616e-06 eta: 13:40:33 time: 1.2241 data_time: 0.0608 memory: 6240 grad_norm: 1.0826 loss: 0.7364 detection_loss_cls: 0.7364 2024/07/07 22:21:46 - mmengine - INFO - Iter(train) [ 79750/120000] base_lr: 5.2064e-05 lr: 6.5513e-06 eta: 13:39:32 time: 1.2241 data_time: 0.0608 memory: 6240 grad_norm: 1.0826 loss: 0.7365 detection_loss_cls: 0.7365 2024/07/07 22:22:47 - mmengine - INFO - Iter(train) [ 79800/120000] base_lr: 5.1952e-05 lr: 6.5411e-06 eta: 13:38:30 time: 1.2239 data_time: 0.0608 memory: 6240 grad_norm: 1.0822 loss: 0.7361 detection_loss_cls: 0.7361 2024/07/07 22:23:49 - mmengine - INFO - Iter(train) [ 79850/120000] base_lr: 5.1839e-05 lr: 6.5308e-06 eta: 13:37:30 time: 1.2242 data_time: 0.0608 memory: 6240 grad_norm: 1.0821 loss: 0.7359 detection_loss_cls: 0.7359 2024/07/07 22:24:50 - mmengine - INFO - Iter(train) [ 79900/120000] base_lr: 5.1727e-05 lr: 6.5206e-06 eta: 13:36:29 time: 1.2243 data_time: 0.0608 memory: 6240 grad_norm: 1.0826 loss: 0.7365 detection_loss_cls: 0.7365 2024/07/07 22:25:50 - mmengine - INFO - Iter(train) [ 79950/120000] base_lr: 5.1615e-05 lr: 6.5104e-06 eta: 13:35:27 time: 1.2242 data_time: 0.0607 memory: 6240 grad_norm: 1.0827 loss: 0.7356 detection_loss_cls: 0.7356 2024/07/07 22:26:51 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 22:26:51 - mmengine - INFO - Iter(train) [ 80000/120000] base_lr: 5.1502e-05 lr: 6.5002e-06 eta: 13:34:26 time: 1.2243 data_time: 0.0607 memory: 6240 grad_norm: 1.0825 loss: 0.7345 detection_loss_cls: 0.7345 2024/07/07 22:26:51 - mmengine - INFO - Saving checkpoint at 80000 iterations 2024/07/07 22:27:39 - mmengine - INFO - Iter(val) [ 50/105] eta: 0:00:44 time: 0.8011 data_time: 0.0292 memory: 6808 2024/07/07 22:28:19 - mmengine - INFO - Iter(val) [100/105] eta: 0:00:03 time: 0.8010 data_time: 0.0291 memory: 6806 2024/07/07 22:28:25 - mmengine - INFO - Evaluating bbox... 2024/07/07 22:28:51 - mmengine - INFO - bbox_mAP_copypaste: 0.416 0.585 0.445 0.202 0.462 0.595 2024/07/07 22:28:51 - mmengine - INFO - Iter(val) [105/105] coco/bbox_mAP: 0.4160 coco/bbox_mAP_50: 0.5850 coco/bbox_mAP_75: 0.4450 coco/bbox_mAP_s: 0.2020 coco/bbox_mAP_m: 0.4620 coco/bbox_mAP_l: 0.5950 data_time: 0.0284 time: 0.7907 2024/07/07 22:29:51 - mmengine - INFO - Iter(train) [ 80050/120000] base_lr: 5.1390e-05 lr: 6.4900e-06 eta: 13:33:42 time: 1.2292 data_time: 0.0660 memory: 6805 grad_norm: 1.0813 loss: 0.7352 detection_loss_cls: 0.7352 2024/07/07 22:30:51 - mmengine - INFO - Iter(train) [ 80100/120000] base_lr: 5.1278e-05 lr: 6.4798e-06 eta: 13:32:40 time: 1.2289 data_time: 0.0660 memory: 6241 grad_norm: 1.0812 loss: 0.7347 detection_loss_cls: 0.7347 2024/07/07 22:31:51 - mmengine - INFO - Iter(train) [ 80150/120000] base_lr: 5.1166e-05 lr: 6.4696e-06 eta: 13:31:38 time: 1.2288 data_time: 0.0660 memory: 6241 grad_norm: 1.0810 loss: 0.7339 detection_loss_cls: 0.7339 2024/07/07 22:32:50 - mmengine - INFO - Iter(train) [ 80200/120000] base_lr: 5.1054e-05 lr: 6.4595e-06 eta: 13:30:36 time: 1.2286 data_time: 0.0660 memory: 6241 grad_norm: 1.0812 loss: 0.7344 detection_loss_cls: 0.7344 2024/07/07 22:33:50 - mmengine - INFO - Iter(train) [ 80250/120000] base_lr: 5.0942e-05 lr: 6.4493e-06 eta: 13:29:34 time: 1.2283 data_time: 0.0660 memory: 6241 grad_norm: 1.0806 loss: 0.7348 detection_loss_cls: 0.7348 2024/07/07 22:34:50 - mmengine - INFO - Iter(train) [ 80300/120000] base_lr: 5.0830e-05 lr: 6.4391e-06 eta: 13:28:33 time: 1.2280 data_time: 0.0661 memory: 6241 grad_norm: 1.0809 loss: 0.7358 detection_loss_cls: 0.7358 2024/07/07 22:35:50 - mmengine - INFO - Iter(train) [ 80350/120000] base_lr: 5.0719e-05 lr: 6.4290e-06 eta: 13:27:31 time: 1.2278 data_time: 0.0661 memory: 6241 grad_norm: 1.0811 loss: 0.7363 detection_loss_cls: 0.7363 2024/07/07 22:36:50 - mmengine - INFO - Iter(train) [ 80400/120000] base_lr: 5.0607e-05 lr: 6.4188e-06 eta: 13:26:29 time: 1.2274 data_time: 0.0661 memory: 6241 grad_norm: 1.0826 loss: 0.7359 detection_loss_cls: 0.7359 2024/07/07 22:37:50 - mmengine - INFO - Iter(train) [ 80450/120000] base_lr: 5.0496e-05 lr: 6.4087e-06 eta: 13:25:27 time: 1.2273 data_time: 0.0661 memory: 6241 grad_norm: 1.0811 loss: 0.7355 detection_loss_cls: 0.7355 2024/07/07 22:38:50 - mmengine - INFO - Iter(train) [ 80500/120000] base_lr: 5.0384e-05 lr: 6.3986e-06 eta: 13:24:25 time: 1.2270 data_time: 0.0661 memory: 6241 grad_norm: 1.0816 loss: 0.7355 detection_loss_cls: 0.7355 2024/07/07 22:39:50 - mmengine - INFO - Iter(train) [ 80550/120000] base_lr: 5.0273e-05 lr: 6.3884e-06 eta: 13:23:23 time: 1.2266 data_time: 0.0661 memory: 6241 grad_norm: 1.0812 loss: 0.7352 detection_loss_cls: 0.7352 2024/07/07 22:40:50 - mmengine - INFO - Iter(train) [ 80600/120000] base_lr: 5.0162e-05 lr: 6.3783e-06 eta: 13:22:22 time: 1.2262 data_time: 0.0660 memory: 6241 grad_norm: 1.0819 loss: 0.7346 detection_loss_cls: 0.7346 2024/07/07 22:41:49 - mmengine - INFO - Iter(train) [ 80650/120000] base_lr: 5.0050e-05 lr: 6.3682e-06 eta: 13:21:20 time: 1.2260 data_time: 0.0660 memory: 6241 grad_norm: 1.0824 loss: 0.7350 detection_loss_cls: 0.7350 2024/07/07 22:42:49 - mmengine - INFO - Iter(train) [ 80700/120000] base_lr: 4.9939e-05 lr: 6.3581e-06 eta: 13:20:18 time: 1.2257 data_time: 0.0661 memory: 6241 grad_norm: 1.0854 loss: 0.7357 detection_loss_cls: 0.7357 2024/07/07 22:43:50 - mmengine - INFO - Iter(train) [ 80750/120000] base_lr: 4.9828e-05 lr: 6.3480e-06 eta: 13:19:17 time: 1.2257 data_time: 0.0661 memory: 6241 grad_norm: 1.0856 loss: 0.7362 detection_loss_cls: 0.7362 2024/07/07 22:44:50 - mmengine - INFO - Iter(train) [ 80800/120000] base_lr: 4.9718e-05 lr: 6.3380e-06 eta: 13:18:15 time: 1.2253 data_time: 0.0662 memory: 6241 grad_norm: 1.0859 loss: 0.7372 detection_loss_cls: 0.7372 2024/07/07 22:45:49 - mmengine - INFO - Iter(train) [ 80850/120000] base_lr: 4.9607e-05 lr: 6.3279e-06 eta: 13:17:13 time: 1.2248 data_time: 0.0661 memory: 6241 grad_norm: 1.0856 loss: 0.7364 detection_loss_cls: 0.7364 2024/07/07 22:46:50 - mmengine - INFO - Iter(train) [ 80900/120000] base_lr: 4.9496e-05 lr: 6.3178e-06 eta: 13:16:11 time: 1.2248 data_time: 0.0661 memory: 6241 grad_norm: 1.0859 loss: 0.7366 detection_loss_cls: 0.7366 2024/07/07 22:47:50 - mmengine - INFO - Iter(train) [ 80950/120000] base_lr: 4.9385e-05 lr: 6.3078e-06 eta: 13:15:09 time: 1.2246 data_time: 0.0661 memory: 6241 grad_norm: 1.0867 loss: 0.7360 detection_loss_cls: 0.7360 2024/07/07 22:48:49 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 22:48:49 - mmengine - INFO - Iter(train) [ 81000/120000] base_lr: 4.9275e-05 lr: 6.2977e-06 eta: 13:14:07 time: 1.2243 data_time: 0.0661 memory: 6241 grad_norm: 1.0868 loss: 0.7356 detection_loss_cls: 0.7356 2024/07/07 22:48:49 - mmengine - INFO - Saving checkpoint at 81000 iterations 2024/07/07 22:49:57 - mmengine - INFO - Iter(train) [ 81050/120000] base_lr: 4.9164e-05 lr: 6.2877e-06 eta: 13:13:10 time: 1.2242 data_time: 0.0662 memory: 6241 grad_norm: 1.0873 loss: 0.7349 detection_loss_cls: 0.7349 2024/07/07 22:50:57 - mmengine - INFO - Iter(train) [ 81100/120000] base_lr: 4.9054e-05 lr: 6.2776e-06 eta: 13:12:08 time: 1.2238 data_time: 0.0662 memory: 6241 grad_norm: 1.0875 loss: 0.7347 detection_loss_cls: 0.7347 2024/07/07 22:51:56 - mmengine - INFO - Iter(train) [ 81150/120000] base_lr: 4.8944e-05 lr: 6.2676e-06 eta: 13:11:06 time: 1.2234 data_time: 0.0662 memory: 6241 grad_norm: 1.0886 loss: 0.7346 detection_loss_cls: 0.7346 2024/07/07 22:52:57 - mmengine - INFO - Iter(train) [ 81200/120000] base_lr: 4.8834e-05 lr: 6.2576e-06 eta: 13:10:05 time: 1.2235 data_time: 0.0662 memory: 6241 grad_norm: 1.0890 loss: 0.7345 detection_loss_cls: 0.7345 2024/07/07 22:53:56 - mmengine - INFO - Iter(train) [ 81250/120000] base_lr: 4.8723e-05 lr: 6.2476e-06 eta: 13:09:03 time: 1.2235 data_time: 0.0662 memory: 6241 grad_norm: 1.0881 loss: 0.7342 detection_loss_cls: 0.7342 2024/07/07 22:54:55 - mmengine - INFO - Iter(train) [ 81300/120000] base_lr: 4.8613e-05 lr: 6.2376e-06 eta: 13:08:00 time: 1.2230 data_time: 0.0662 memory: 6241 grad_norm: 1.0894 loss: 0.7335 detection_loss_cls: 0.7335 2024/07/07 22:55:56 - mmengine - INFO - Iter(train) [ 81350/120000] base_lr: 4.8504e-05 lr: 6.2276e-06 eta: 13:06:59 time: 1.2230 data_time: 0.0662 memory: 6241 grad_norm: 1.0892 loss: 0.7335 detection_loss_cls: 0.7335 2024/07/07 22:56:56 - mmengine - INFO - Iter(train) [ 81400/120000] base_lr: 4.8394e-05 lr: 6.2176e-06 eta: 13:05:57 time: 1.2229 data_time: 0.0662 memory: 6241 grad_norm: 1.0888 loss: 0.7328 detection_loss_cls: 0.7328 2024/07/07 22:57:55 - mmengine - INFO - Iter(train) [ 81450/120000] base_lr: 4.8284e-05 lr: 6.2076e-06 eta: 13:04:55 time: 1.2225 data_time: 0.0662 memory: 6241 grad_norm: 1.0894 loss: 0.7332 detection_loss_cls: 0.7332 2024/07/07 22:58:55 - mmengine - INFO - Iter(train) [ 81500/120000] base_lr: 4.8174e-05 lr: 6.1977e-06 eta: 13:03:53 time: 1.2225 data_time: 0.0661 memory: 6241 grad_norm: 1.0892 loss: 0.7336 detection_loss_cls: 0.7336 2024/07/07 22:59:55 - mmengine - INFO - Iter(train) [ 81550/120000] base_lr: 4.8065e-05 lr: 6.1877e-06 eta: 13:02:52 time: 1.2226 data_time: 0.0661 memory: 6241 grad_norm: 1.0895 loss: 0.7342 detection_loss_cls: 0.7342 2024/07/07 23:00:55 - 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mmengine - INFO - Iter(train) [ 81850/120000] base_lr: 4.7409e-05 lr: 6.1281e-06 eta: 12:56:40 time: 1.2209 data_time: 0.0660 memory: 6241 grad_norm: 1.0919 loss: 0.7313 detection_loss_cls: 0.7313 2024/07/07 23:06:52 - mmengine - INFO - Iter(train) [ 81900/120000] base_lr: 4.7300e-05 lr: 6.1182e-06 eta: 12:55:38 time: 1.2204 data_time: 0.0659 memory: 6241 grad_norm: 1.0938 loss: 0.7309 detection_loss_cls: 0.7309 2024/07/07 23:07:53 - mmengine - INFO - Iter(train) [ 81950/120000] base_lr: 4.7192e-05 lr: 6.1083e-06 eta: 12:54:37 time: 1.2203 data_time: 0.0659 memory: 6241 grad_norm: 1.0948 loss: 0.7298 detection_loss_cls: 0.7298 2024/07/07 23:08:52 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 23:08:52 - mmengine - INFO - Iter(train) [ 82000/120000] base_lr: 4.7083e-05 lr: 6.0984e-06 eta: 12:53:35 time: 1.2202 data_time: 0.0659 memory: 6241 grad_norm: 1.0952 loss: 0.7288 detection_loss_cls: 0.7288 2024/07/07 23:08:52 - mmengine - INFO - Saving checkpoint at 82000 iterations 2024/07/07 23:10:00 - 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mmengine - INFO - Saving checkpoint at 83000 iterations 2024/07/07 23:30:17 - mmengine - INFO - Iter(train) [ 83050/120000] base_lr: 4.4821e-05 lr: 5.8928e-06 eta: 12:32:13 time: 1.2175 data_time: 0.0662 memory: 6241 grad_norm: 1.0992 loss: 0.7289 detection_loss_cls: 0.7289 2024/07/07 23:31:17 - mmengine - INFO - Iter(train) [ 83100/120000] base_lr: 4.4715e-05 lr: 5.8831e-06 eta: 12:31:12 time: 1.2172 data_time: 0.0661 memory: 6241 grad_norm: 1.0990 loss: 0.7290 detection_loss_cls: 0.7290 2024/07/07 23:32:18 - mmengine - INFO - Iter(train) [ 83150/120000] base_lr: 4.4608e-05 lr: 5.8734e-06 eta: 12:30:10 time: 1.2173 data_time: 0.0662 memory: 6241 grad_norm: 1.0985 loss: 0.7300 detection_loss_cls: 0.7300 2024/07/07 23:33:19 - mmengine - INFO - Iter(train) [ 83200/120000] base_lr: 4.4501e-05 lr: 5.8638e-06 eta: 12:29:09 time: 1.2175 data_time: 0.0663 memory: 6241 grad_norm: 1.0983 loss: 0.7310 detection_loss_cls: 0.7310 2024/07/07 23:34:19 - mmengine - INFO - Iter(train) [ 83250/120000] base_lr: 4.4395e-05 lr: 5.8541e-06 eta: 12:28:08 time: 1.2173 data_time: 0.0663 memory: 6241 grad_norm: 1.0987 loss: 0.7319 detection_loss_cls: 0.7319 2024/07/07 23:35:20 - 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mmengine - INFO - Iter(train) [ 83800/120000] base_lr: 4.3232e-05 lr: 5.7483e-06 eta: 12:16:53 time: 1.2166 data_time: 0.0661 memory: 6241 grad_norm: 1.0976 loss: 0.7283 detection_loss_cls: 0.7283 2024/07/07 23:46:26 - mmengine - INFO - Iter(train) [ 83850/120000] base_lr: 4.3126e-05 lr: 5.7388e-06 eta: 12:15:51 time: 1.2162 data_time: 0.0661 memory: 6241 grad_norm: 1.0979 loss: 0.7281 detection_loss_cls: 0.7281 2024/07/07 23:47:27 - mmengine - INFO - Iter(train) [ 83900/120000] base_lr: 4.3021e-05 lr: 5.7292e-06 eta: 12:14:50 time: 1.2161 data_time: 0.0662 memory: 6241 grad_norm: 1.0981 loss: 0.7285 detection_loss_cls: 0.7285 2024/07/07 23:48:27 - mmengine - INFO - Iter(train) [ 83950/120000] base_lr: 4.2916e-05 lr: 5.7197e-06 eta: 12:13:49 time: 1.2160 data_time: 0.0662 memory: 6241 grad_norm: 1.0984 loss: 0.7290 detection_loss_cls: 0.7290 2024/07/07 23:49:27 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/07 23:49:27 - mmengine - INFO - Iter(train) [ 84000/120000] base_lr: 4.2811e-05 lr: 5.7101e-06 eta: 12:12:47 time: 1.2159 data_time: 0.0662 memory: 6241 grad_norm: 1.0991 loss: 0.7291 detection_loss_cls: 0.7291 2024/07/07 23:49:27 - mmengine - INFO - Saving checkpoint at 84000 iterations 2024/07/07 23:50:36 - mmengine - INFO - Iter(train) [ 84050/120000] base_lr: 4.2707e-05 lr: 5.7006e-06 eta: 12:11:50 time: 1.2109 data_time: 0.0610 memory: 6241 grad_norm: 1.0990 loss: 0.7283 detection_loss_cls: 0.7283 2024/07/07 23:51:36 - mmengine - INFO - Iter(train) [ 84100/120000] base_lr: 4.2602e-05 lr: 5.6911e-06 eta: 12:10:49 time: 1.2111 data_time: 0.0610 memory: 6241 grad_norm: 1.0989 loss: 0.7285 detection_loss_cls: 0.7285 2024/07/07 23:52:37 - mmengine - INFO - Iter(train) [ 84150/120000] base_lr: 4.2497e-05 lr: 5.6816e-06 eta: 12:09:47 time: 1.2111 data_time: 0.0610 memory: 6241 grad_norm: 1.0993 loss: 0.7296 detection_loss_cls: 0.7296 2024/07/07 23:53:37 - mmengine - INFO - Iter(train) [ 84200/120000] base_lr: 4.2393e-05 lr: 5.6721e-06 eta: 12:08:46 time: 1.2113 data_time: 0.0610 memory: 6241 grad_norm: 1.1000 loss: 0.7300 detection_loss_cls: 0.7300 2024/07/07 23:54:37 - mmengine - INFO - Iter(train) [ 84250/120000] base_lr: 4.2288e-05 lr: 5.6626e-06 eta: 12:07:44 time: 1.2113 data_time: 0.0610 memory: 6241 grad_norm: 1.1015 loss: 0.7296 detection_loss_cls: 0.7296 2024/07/07 23:55:37 - mmengine - INFO - Iter(train) [ 84300/120000] base_lr: 4.2184e-05 lr: 5.6531e-06 eta: 12:06:42 time: 1.2112 data_time: 0.0609 memory: 6241 grad_norm: 1.1013 loss: 0.7289 detection_loss_cls: 0.7289 2024/07/07 23:56:37 - mmengine - INFO - Iter(train) [ 84350/120000] base_lr: 4.2080e-05 lr: 5.6436e-06 eta: 12:05:41 time: 1.2114 data_time: 0.0610 memory: 6241 grad_norm: 1.1010 loss: 0.7292 detection_loss_cls: 0.7292 2024/07/07 23:57:38 - mmengine - INFO - Iter(train) [ 84400/120000] base_lr: 4.1976e-05 lr: 5.6342e-06 eta: 12:04:40 time: 1.2117 data_time: 0.0610 memory: 6241 grad_norm: 1.0994 loss: 0.7290 detection_loss_cls: 0.7290 2024/07/07 23:58:38 - mmengine - INFO - Iter(train) [ 84450/120000] base_lr: 4.1872e-05 lr: 5.6247e-06 eta: 12:03:38 time: 1.2115 data_time: 0.0609 memory: 6241 grad_norm: 1.0996 loss: 0.7287 detection_loss_cls: 0.7287 2024/07/07 23:59:39 - mmengine - INFO - Iter(train) [ 84500/120000] base_lr: 4.1768e-05 lr: 5.6153e-06 eta: 12:02:37 time: 1.2118 data_time: 0.0609 memory: 6241 grad_norm: 1.0996 loss: 0.7282 detection_loss_cls: 0.7282 2024/07/08 00:00:39 - mmengine - INFO - Iter(train) [ 84550/120000] base_lr: 4.1664e-05 lr: 5.6058e-06 eta: 12:01:35 time: 1.2119 data_time: 0.0609 memory: 6241 grad_norm: 1.1001 loss: 0.7282 detection_loss_cls: 0.7282 2024/07/08 00:01:39 - mmengine - INFO - Iter(train) [ 84600/120000] base_lr: 4.1560e-05 lr: 5.5964e-06 eta: 12:00:34 time: 1.2120 data_time: 0.0610 memory: 6241 grad_norm: 1.0990 loss: 0.7281 detection_loss_cls: 0.7281 2024/07/08 00:02:40 - mmengine - INFO - Iter(train) [ 84650/120000] base_lr: 4.1457e-05 lr: 5.5870e-06 eta: 11:59:32 time: 1.2122 data_time: 0.0610 memory: 6241 grad_norm: 1.0990 loss: 0.7282 detection_loss_cls: 0.7282 2024/07/08 00:03:41 - mmengine - INFO - Iter(train) [ 84700/120000] base_lr: 4.1353e-05 lr: 5.5776e-06 eta: 11:58:31 time: 1.2125 data_time: 0.0610 memory: 6241 grad_norm: 1.0952 loss: 0.7280 detection_loss_cls: 0.7280 2024/07/08 00:04:41 - mmengine - INFO - Iter(train) [ 84750/120000] base_lr: 4.1250e-05 lr: 5.5682e-06 eta: 11:57:30 time: 1.2124 data_time: 0.0610 memory: 6241 grad_norm: 1.0949 loss: 0.7272 detection_loss_cls: 0.7272 2024/07/08 00:05:42 - mmengine - INFO - Iter(train) [ 84800/120000] base_lr: 4.1147e-05 lr: 5.5588e-06 eta: 11:56:29 time: 1.2126 data_time: 0.0609 memory: 6241 grad_norm: 1.0955 loss: 0.7267 detection_loss_cls: 0.7267 2024/07/08 00:06:42 - mmengine - INFO - Iter(train) [ 84850/120000] base_lr: 4.1044e-05 lr: 5.5494e-06 eta: 11:55:27 time: 1.2127 data_time: 0.0610 memory: 6241 grad_norm: 1.0959 loss: 0.7272 detection_loss_cls: 0.7272 2024/07/08 00:07:41 - mmengine - INFO - Iter(train) [ 84900/120000] base_lr: 4.0941e-05 lr: 5.5400e-06 eta: 11:54:25 time: 1.2125 data_time: 0.0610 memory: 6241 grad_norm: 1.0961 loss: 0.7279 detection_loss_cls: 0.7279 2024/07/08 00:08:42 - mmengine - INFO - Iter(train) [ 84950/120000] base_lr: 4.0838e-05 lr: 5.5307e-06 eta: 11:53:24 time: 1.2128 data_time: 0.0610 memory: 6241 grad_norm: 1.0958 loss: 0.7273 detection_loss_cls: 0.7273 2024/07/08 00:09:42 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/08 00:09:42 - mmengine - INFO - Iter(train) [ 85000/120000] base_lr: 4.0735e-05 lr: 5.5213e-06 eta: 11:52:23 time: 1.2130 data_time: 0.0611 memory: 6241 grad_norm: 1.0964 loss: 0.7289 detection_loss_cls: 0.7289 2024/07/08 00:09:42 - mmengine - INFO - Saving checkpoint at 85000 iterations 2024/07/08 00:10:30 - mmengine - INFO - Iter(val) [ 50/105] eta: 0:00:44 time: 0.8008 data_time: 0.0291 memory: 6808 2024/07/08 00:11:10 - mmengine - INFO - Iter(val) [100/105] eta: 0:00:04 time: 0.8008 data_time: 0.0291 memory: 6808 2024/07/08 00:11:16 - mmengine - INFO - Evaluating bbox... 2024/07/08 00:11:43 - mmengine - INFO - bbox_mAP_copypaste: 0.419 0.590 0.448 0.204 0.467 0.598 2024/07/08 00:11:43 - mmengine - INFO - Iter(val) [105/105] coco/bbox_mAP: 0.4190 coco/bbox_mAP_50: 0.5900 coco/bbox_mAP_75: 0.4480 coco/bbox_mAP_s: 0.2040 coco/bbox_mAP_m: 0.4670 coco/bbox_mAP_l: 0.5980 data_time: 0.0283 time: 0.7925 2024/07/08 00:12:43 - mmengine - INFO - Iter(train) [ 85050/120000] base_lr: 4.0632e-05 lr: 5.5120e-06 eta: 11:51:35 time: 1.2181 data_time: 0.0663 memory: 6804 grad_norm: 1.0964 loss: 0.7299 detection_loss_cls: 0.7299 2024/07/08 00:13:43 - mmengine - INFO - Iter(train) [ 85100/120000] base_lr: 4.0529e-05 lr: 5.5027e-06 eta: 11:50:34 time: 1.2184 data_time: 0.0663 memory: 6239 grad_norm: 1.0964 loss: 0.7292 detection_loss_cls: 0.7292 2024/07/08 00:14:43 - 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mmengine - INFO - Iter(train) [ 86850/120000] base_lr: 3.7003e-05 lr: 5.1821e-06 eta: 11:14:45 time: 1.2192 data_time: 0.0666 memory: 6239 grad_norm: 1.0962 loss: 0.7278 detection_loss_cls: 0.7278 2024/07/08 00:49:58 - mmengine - INFO - Iter(train) [ 86900/120000] base_lr: 3.6904e-05 lr: 5.1731e-06 eta: 11:13:43 time: 1.2193 data_time: 0.0665 memory: 6239 grad_norm: 1.0976 loss: 0.7276 detection_loss_cls: 0.7276 2024/07/08 00:50:58 - mmengine - INFO - Iter(train) [ 86950/120000] base_lr: 3.6805e-05 lr: 5.1641e-06 eta: 11:12:42 time: 1.2193 data_time: 0.0665 memory: 6239 grad_norm: 1.0980 loss: 0.7272 detection_loss_cls: 0.7272 2024/07/08 00:51:58 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/08 00:51:58 - mmengine - INFO - Iter(train) [ 87000/120000] base_lr: 3.6707e-05 lr: 5.1551e-06 eta: 11:11:40 time: 1.2189 data_time: 0.0664 memory: 6239 grad_norm: 1.0996 loss: 0.7262 detection_loss_cls: 0.7262 2024/07/08 00:51:58 - mmengine - INFO - Saving checkpoint at 87000 iterations 2024/07/08 00:53:06 - 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mmengine - INFO - Saving checkpoint at 88000 iterations 2024/07/08 01:13:15 - mmengine - INFO - Iter(train) [ 88050/120000] base_lr: 3.4662e-05 lr: 4.9692e-06 eta: 10:50:16 time: 1.2166 data_time: 0.0664 memory: 6239 grad_norm: 1.1042 loss: 0.7234 detection_loss_cls: 0.7234 2024/07/08 01:14:16 - mmengine - INFO - Iter(train) [ 88100/120000] base_lr: 3.4566e-05 lr: 4.9605e-06 eta: 10:49:14 time: 1.2166 data_time: 0.0664 memory: 6239 grad_norm: 1.1042 loss: 0.7232 detection_loss_cls: 0.7232 2024/07/08 01:15:16 - mmengine - INFO - Iter(train) [ 88150/120000] base_lr: 3.4470e-05 lr: 4.9518e-06 eta: 10:48:13 time: 1.2167 data_time: 0.0664 memory: 6239 grad_norm: 1.1040 loss: 0.7226 detection_loss_cls: 0.7226 2024/07/08 01:16:16 - mmengine - INFO - Iter(train) [ 88200/120000] base_lr: 3.4374e-05 lr: 4.9431e-06 eta: 10:47:12 time: 1.2166 data_time: 0.0664 memory: 6239 grad_norm: 1.1036 loss: 0.7223 detection_loss_cls: 0.7223 2024/07/08 01:17:17 - mmengine - INFO - Iter(train) [ 88250/120000] base_lr: 3.4278e-05 lr: 4.9344e-06 eta: 10:46:10 time: 1.2167 data_time: 0.0664 memory: 6239 grad_norm: 1.1026 loss: 0.7225 detection_loss_cls: 0.7225 2024/07/08 01:18:16 - 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mmengine - INFO - Saving checkpoint at 89000 iterations 2024/07/08 01:33:28 - mmengine - INFO - Iter(train) [ 89050/120000] base_lr: 3.2761e-05 lr: 4.7964e-06 eta: 10:29:51 time: 1.2111 data_time: 0.0613 memory: 6239 grad_norm: 1.1049 loss: 0.7200 detection_loss_cls: 0.7200 2024/07/08 01:34:28 - mmengine - INFO - Iter(train) [ 89100/120000] base_lr: 3.2667e-05 lr: 4.7879e-06 eta: 10:28:49 time: 1.2109 data_time: 0.0613 memory: 6239 grad_norm: 1.1057 loss: 0.7206 detection_loss_cls: 0.7206 2024/07/08 01:35:28 - mmengine - INFO - Iter(train) [ 89150/120000] base_lr: 3.2573e-05 lr: 4.7794e-06 eta: 10:27:48 time: 1.2109 data_time: 0.0613 memory: 6239 grad_norm: 1.1062 loss: 0.7203 detection_loss_cls: 0.7203 2024/07/08 01:36:29 - mmengine - INFO - Iter(train) [ 89200/120000] base_lr: 3.2480e-05 lr: 4.7709e-06 eta: 10:26:47 time: 1.2112 data_time: 0.0613 memory: 6239 grad_norm: 1.1065 loss: 0.7197 detection_loss_cls: 0.7197 2024/07/08 01:37:29 - mmengine - INFO - Iter(train) [ 89250/120000] base_lr: 3.2386e-05 lr: 4.7624e-06 eta: 10:25:45 time: 1.2109 data_time: 0.0613 memory: 6239 grad_norm: 1.1067 loss: 0.7204 detection_loss_cls: 0.7204 2024/07/08 01:38:30 - mmengine - INFO - Iter(train) [ 89300/120000] base_lr: 3.2293e-05 lr: 4.7539e-06 eta: 10:24:44 time: 1.2110 data_time: 0.0614 memory: 6239 grad_norm: 1.1061 loss: 0.7207 detection_loss_cls: 0.7207 2024/07/08 01:39:30 - mmengine - INFO - Iter(train) [ 89350/120000] base_lr: 3.2200e-05 lr: 4.7454e-06 eta: 10:23:43 time: 1.2110 data_time: 0.0614 memory: 6239 grad_norm: 1.1065 loss: 0.7206 detection_loss_cls: 0.7206 2024/07/08 01:40:29 - mmengine - INFO - Iter(train) [ 89400/120000] base_lr: 3.2106e-05 lr: 4.7370e-06 eta: 10:22:41 time: 1.2107 data_time: 0.0613 memory: 6239 grad_norm: 1.1066 loss: 0.7194 detection_loss_cls: 0.7194 2024/07/08 01:41:30 - mmengine - INFO - Iter(train) [ 89450/120000] base_lr: 3.2013e-05 lr: 4.7285e-06 eta: 10:21:40 time: 1.2108 data_time: 0.0613 memory: 6239 grad_norm: 1.1070 loss: 0.7185 detection_loss_cls: 0.7185 2024/07/08 01:42:30 - mmengine - INFO - Iter(train) [ 89500/120000] base_lr: 3.1921e-05 lr: 4.7201e-06 eta: 10:20:38 time: 1.2108 data_time: 0.0613 memory: 6239 grad_norm: 1.1073 loss: 0.7181 detection_loss_cls: 0.7181 2024/07/08 01:43:30 - mmengine - INFO - Iter(train) [ 89550/120000] base_lr: 3.1828e-05 lr: 4.7116e-06 eta: 10:19:37 time: 1.2107 data_time: 0.0613 memory: 6239 grad_norm: 1.1071 loss: 0.7179 detection_loss_cls: 0.7179 2024/07/08 01:44:31 - mmengine - INFO - Iter(train) [ 89600/120000] base_lr: 3.1735e-05 lr: 4.7032e-06 eta: 10:18:36 time: 1.2107 data_time: 0.0612 memory: 6239 grad_norm: 1.1072 loss: 0.7172 detection_loss_cls: 0.7172 2024/07/08 01:45:31 - mmengine - INFO - Iter(train) [ 89650/120000] base_lr: 3.1643e-05 lr: 4.6948e-06 eta: 10:17:34 time: 1.2108 data_time: 0.0613 memory: 6239 grad_norm: 1.1075 loss: 0.7178 detection_loss_cls: 0.7178 2024/07/08 01:46:31 - mmengine - INFO - Iter(train) [ 89700/120000] base_lr: 3.1550e-05 lr: 4.6864e-06 eta: 10:16:33 time: 1.2106 data_time: 0.0613 memory: 6239 grad_norm: 1.1075 loss: 0.7182 detection_loss_cls: 0.7182 2024/07/08 01:47:32 - mmengine - INFO - Iter(train) [ 89750/120000] base_lr: 3.1458e-05 lr: 4.6780e-06 eta: 10:15:32 time: 1.2105 data_time: 0.0613 memory: 6239 grad_norm: 1.1077 loss: 0.7178 detection_loss_cls: 0.7178 2024/07/08 01:48:32 - mmengine - INFO - Iter(train) [ 89800/120000] base_lr: 3.1366e-05 lr: 4.6696e-06 eta: 10:14:30 time: 1.2105 data_time: 0.0613 memory: 6239 grad_norm: 1.1072 loss: 0.7174 detection_loss_cls: 0.7174 2024/07/08 01:49:33 - mmengine - INFO - Iter(train) [ 89850/120000] base_lr: 3.1274e-05 lr: 4.6612e-06 eta: 10:13:29 time: 1.2106 data_time: 0.0614 memory: 6239 grad_norm: 1.1063 loss: 0.7188 detection_loss_cls: 0.7188 2024/07/08 01:50:33 - mmengine - INFO - Iter(train) [ 89900/120000] base_lr: 3.1182e-05 lr: 4.6529e-06 eta: 10:12:28 time: 1.2107 data_time: 0.0614 memory: 6239 grad_norm: 1.1061 loss: 0.7185 detection_loss_cls: 0.7185 2024/07/08 01:51:34 - mmengine - INFO - Iter(train) [ 89950/120000] base_lr: 3.1090e-05 lr: 4.6445e-06 eta: 10:11:27 time: 1.2109 data_time: 0.0614 memory: 6239 grad_norm: 1.1062 loss: 0.7185 detection_loss_cls: 0.7185 2024/07/08 01:52:33 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/08 01:52:33 - mmengine - INFO - Iter(train) [ 90000/120000] base_lr: 3.0998e-05 lr: 4.6362e-06 eta: 10:10:25 time: 1.2106 data_time: 0.0614 memory: 6239 grad_norm: 1.1062 loss: 0.7194 detection_loss_cls: 0.7194 2024/07/08 01:52:33 - mmengine - INFO - Saving checkpoint at 90000 iterations 2024/07/08 01:53:21 - mmengine - INFO - Iter(val) [ 50/105] eta: 0:00:43 time: 0.8005 data_time: 0.0291 memory: 6807 2024/07/08 01:54:01 - mmengine - INFO - Iter(val) [100/105] eta: 0:00:03 time: 0.8005 data_time: 0.0290 memory: 6806 2024/07/08 01:54:07 - mmengine - INFO - Evaluating bbox... 2024/07/08 01:54:33 - mmengine - INFO - bbox_mAP_copypaste: 0.420 0.590 0.449 0.205 0.467 0.601 2024/07/08 01:54:33 - mmengine - INFO - Iter(val) [105/105] coco/bbox_mAP: 0.4200 coco/bbox_mAP_50: 0.5900 coco/bbox_mAP_75: 0.4490 coco/bbox_mAP_s: 0.2050 coco/bbox_mAP_m: 0.4670 coco/bbox_mAP_l: 0.6010 data_time: 0.0283 time: 0.7904 2024/07/08 01:55:33 - mmengine - INFO - Iter(train) [ 90050/120000] base_lr: 3.0907e-05 lr: 4.6279e-06 eta: 10:09:35 time: 1.2158 data_time: 0.0666 memory: 6804 grad_norm: 1.1059 loss: 0.7199 detection_loss_cls: 0.7199 2024/07/08 01:56:33 - mmengine - INFO - Iter(train) [ 90100/120000] base_lr: 3.0815e-05 lr: 4.6196e-06 eta: 10:08:33 time: 1.2161 data_time: 0.0665 memory: 6240 grad_norm: 1.1061 loss: 0.7201 detection_loss_cls: 0.7201 2024/07/08 01:57:34 - mmengine - INFO - Iter(train) [ 90150/120000] base_lr: 3.0724e-05 lr: 4.6113e-06 eta: 10:07:32 time: 1.2163 data_time: 0.0666 memory: 6240 grad_norm: 1.1058 loss: 0.7212 detection_loss_cls: 0.7212 2024/07/08 01:58:35 - mmengine - INFO - Iter(train) [ 90200/120000] base_lr: 3.0633e-05 lr: 4.6030e-06 eta: 10:06:31 time: 1.2167 data_time: 0.0666 memory: 6240 grad_norm: 1.1055 loss: 0.7216 detection_loss_cls: 0.7216 2024/07/08 01:59:34 - mmengine - INFO - Iter(train) [ 90250/120000] base_lr: 3.0542e-05 lr: 4.5947e-06 eta: 10:05:29 time: 1.2167 data_time: 0.0667 memory: 6240 grad_norm: 1.1058 loss: 0.7218 detection_loss_cls: 0.7218 2024/07/08 02:00:34 - mmengine - INFO - Iter(train) [ 90300/120000] base_lr: 3.0451e-05 lr: 4.5864e-06 eta: 10:04:28 time: 1.2165 data_time: 0.0667 memory: 6240 grad_norm: 1.1056 loss: 0.7224 detection_loss_cls: 0.7224 2024/07/08 02:01:35 - mmengine - INFO - Iter(train) [ 90350/120000] base_lr: 3.0360e-05 lr: 4.5782e-06 eta: 10:03:27 time: 1.2167 data_time: 0.0667 memory: 6240 grad_norm: 1.1055 loss: 0.7218 detection_loss_cls: 0.7218 2024/07/08 02:02:36 - mmengine - INFO - Iter(train) [ 90400/120000] base_lr: 3.0269e-05 lr: 4.5699e-06 eta: 10:02:25 time: 1.2169 data_time: 0.0667 memory: 6240 grad_norm: 1.1058 loss: 0.7215 detection_loss_cls: 0.7215 2024/07/08 02:03:35 - mmengine - INFO - Iter(train) [ 90450/120000] base_lr: 3.0178e-05 lr: 4.5617e-06 eta: 10:01:24 time: 1.2167 data_time: 0.0667 memory: 6240 grad_norm: 1.1058 loss: 0.7218 detection_loss_cls: 0.7218 2024/07/08 02:04:36 - mmengine - INFO - Iter(train) [ 90500/120000] base_lr: 3.0088e-05 lr: 4.5534e-06 eta: 10:00:23 time: 1.2170 data_time: 0.0667 memory: 6240 grad_norm: 1.1058 loss: 0.7218 detection_loss_cls: 0.7218 2024/07/08 02:05:37 - mmengine - INFO - Iter(train) [ 90550/120000] base_lr: 2.9998e-05 lr: 4.5452e-06 eta: 9:59:21 time: 1.2171 data_time: 0.0667 memory: 6240 grad_norm: 1.1057 loss: 0.7220 detection_loss_cls: 0.7220 2024/07/08 02:06:36 - mmengine - INFO - Iter(train) [ 90600/120000] base_lr: 2.9907e-05 lr: 4.5370e-06 eta: 9:58:20 time: 1.2168 data_time: 0.0668 memory: 6240 grad_norm: 1.1058 loss: 0.7229 detection_loss_cls: 0.7229 2024/07/08 02:07:37 - mmengine - INFO - Iter(train) [ 90650/120000] base_lr: 2.9817e-05 lr: 4.5288e-06 eta: 9:57:19 time: 1.2170 data_time: 0.0668 memory: 6240 grad_norm: 1.1063 loss: 0.7225 detection_loss_cls: 0.7225 2024/07/08 02:08:37 - mmengine - INFO - Iter(train) [ 90700/120000] base_lr: 2.9727e-05 lr: 4.5207e-06 eta: 9:56:17 time: 1.2171 data_time: 0.0668 memory: 6240 grad_norm: 1.1050 loss: 0.7219 detection_loss_cls: 0.7219 2024/07/08 02:09:37 - mmengine - INFO - Iter(train) [ 90750/120000] base_lr: 2.9637e-05 lr: 4.5125e-06 eta: 9:55:16 time: 1.2167 data_time: 0.0667 memory: 6240 grad_norm: 1.1052 loss: 0.7214 detection_loss_cls: 0.7214 2024/07/08 02:10:38 - mmengine - INFO - Iter(train) [ 90800/120000] base_lr: 2.9548e-05 lr: 4.5043e-06 eta: 9:54:15 time: 1.2170 data_time: 0.0668 memory: 6240 grad_norm: 1.1046 loss: 0.7223 detection_loss_cls: 0.7223 2024/07/08 02:11:39 - mmengine - INFO - Iter(train) [ 90850/120000] base_lr: 2.9458e-05 lr: 4.4962e-06 eta: 9:53:13 time: 1.2172 data_time: 0.0667 memory: 6240 grad_norm: 1.1054 loss: 0.7222 detection_loss_cls: 0.7222 2024/07/08 02:12:39 - mmengine - INFO - Iter(train) [ 90900/120000] base_lr: 2.9368e-05 lr: 4.4880e-06 eta: 9:52:12 time: 1.2171 data_time: 0.0667 memory: 6240 grad_norm: 1.1045 loss: 0.7212 detection_loss_cls: 0.7212 2024/07/08 02:13:40 - mmengine - INFO - Iter(train) [ 90950/120000] base_lr: 2.9279e-05 lr: 4.4799e-06 eta: 9:51:11 time: 1.2172 data_time: 0.0667 memory: 6240 grad_norm: 1.1043 loss: 0.7212 detection_loss_cls: 0.7212 2024/07/08 02:14:40 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/08 02:14:40 - mmengine - INFO - Iter(train) [ 91000/120000] base_lr: 2.9190e-05 lr: 4.4718e-06 eta: 9:50:09 time: 1.2173 data_time: 0.0667 memory: 6240 grad_norm: 1.1023 loss: 0.7212 detection_loss_cls: 0.7212 2024/07/08 02:14:40 - mmengine - INFO - Saving checkpoint at 91000 iterations 2024/07/08 02:15:49 - mmengine - INFO - Iter(train) [ 91050/120000] base_lr: 2.9101e-05 lr: 4.4637e-06 eta: 9:49:11 time: 1.2174 data_time: 0.0667 memory: 6240 grad_norm: 1.1025 loss: 0.7208 detection_loss_cls: 0.7208 2024/07/08 02:16:50 - mmengine - INFO - Iter(train) [ 91100/120000] base_lr: 2.9012e-05 lr: 4.4556e-06 eta: 9:48:10 time: 1.2179 data_time: 0.0667 memory: 6240 grad_norm: 1.1028 loss: 0.7208 detection_loss_cls: 0.7208 2024/07/08 02:17:51 - mmengine - INFO - Iter(train) [ 91150/120000] base_lr: 2.8923e-05 lr: 4.4475e-06 eta: 9:47:09 time: 1.2182 data_time: 0.0667 memory: 6240 grad_norm: 1.1034 loss: 0.7201 detection_loss_cls: 0.7201 2024/07/08 02:18:52 - mmengine - INFO - Iter(train) [ 91200/120000] base_lr: 2.8834e-05 lr: 4.4394e-06 eta: 9:46:08 time: 1.2183 data_time: 0.0666 memory: 6240 grad_norm: 1.1019 loss: 0.7196 detection_loss_cls: 0.7196 2024/07/08 02:19:54 - mmengine - INFO - Iter(train) [ 91250/120000] base_lr: 2.8745e-05 lr: 4.4314e-06 eta: 9:45:07 time: 1.2187 data_time: 0.0667 memory: 6240 grad_norm: 1.1017 loss: 0.7191 detection_loss_cls: 0.7191 2024/07/08 02:20:54 - mmengine - INFO - Iter(train) [ 91300/120000] base_lr: 2.8657e-05 lr: 4.4233e-06 eta: 9:44:06 time: 1.2191 data_time: 0.0667 memory: 6240 grad_norm: 1.1005 loss: 0.7203 detection_loss_cls: 0.7203 2024/07/08 02:21:55 - 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mmengine - INFO - Iter(train) [ 91850/120000] base_lr: 2.7691e-05 lr: 4.3356e-06 eta: 9:32:55 time: 1.2223 data_time: 0.0667 memory: 6240 grad_norm: 1.1006 loss: 0.7210 detection_loss_cls: 0.7210 2024/07/08 02:33:10 - mmengine - INFO - Iter(train) [ 91900/120000] base_lr: 2.7604e-05 lr: 4.3276e-06 eta: 9:31:54 time: 1.2227 data_time: 0.0668 memory: 6240 grad_norm: 1.1006 loss: 0.7213 detection_loss_cls: 0.7213 2024/07/08 02:34:11 - mmengine - INFO - Iter(train) [ 91950/120000] base_lr: 2.7517e-05 lr: 4.3197e-06 eta: 9:30:53 time: 1.2229 data_time: 0.0667 memory: 6240 grad_norm: 1.1013 loss: 0.7207 detection_loss_cls: 0.7207 2024/07/08 02:35:13 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/08 02:35:13 - mmengine - INFO - Iter(train) [ 92000/120000] base_lr: 2.7430e-05 lr: 4.3119e-06 eta: 9:29:53 time: 1.2233 data_time: 0.0668 memory: 6240 grad_norm: 1.1011 loss: 0.7209 detection_loss_cls: 0.7209 2024/07/08 02:35:13 - mmengine - INFO - Saving checkpoint at 92000 iterations 2024/07/08 02:36:22 - 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mmengine - INFO - Saving checkpoint at 93000 iterations 2024/07/08 02:56:53 - mmengine - INFO - Iter(train) [ 93050/120000] base_lr: 2.5637e-05 lr: 4.1489e-06 eta: 9:08:36 time: 1.2279 data_time: 0.0667 memory: 6240 grad_norm: 1.1058 loss: 0.7214 detection_loss_cls: 0.7214 2024/07/08 02:57:54 - mmengine - INFO - Iter(train) [ 93100/120000] base_lr: 2.5553e-05 lr: 4.1412e-06 eta: 9:07:36 time: 1.2284 data_time: 0.0667 memory: 6240 grad_norm: 1.1056 loss: 0.7216 detection_loss_cls: 0.7216 2024/07/08 02:58:55 - mmengine - INFO - Iter(train) [ 93150/120000] base_lr: 2.5470e-05 lr: 4.1336e-06 eta: 9:06:34 time: 1.2286 data_time: 0.0668 memory: 6240 grad_norm: 1.1055 loss: 0.7220 detection_loss_cls: 0.7220 2024/07/08 02:59:58 - mmengine - INFO - Iter(train) [ 93200/120000] base_lr: 2.5386e-05 lr: 4.1260e-06 eta: 9:05:34 time: 1.2289 data_time: 0.0668 memory: 6240 grad_norm: 1.1052 loss: 0.7231 detection_loss_cls: 0.7231 2024/07/08 03:00:59 - mmengine - INFO - Iter(train) [ 93250/120000] base_lr: 2.5302e-05 lr: 4.1184e-06 eta: 9:04:33 time: 1.2292 data_time: 0.0667 memory: 6240 grad_norm: 1.1050 loss: 0.7227 detection_loss_cls: 0.7227 2024/07/08 03:01:59 - 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mmengine - INFO - Iter(train) [ 93550/120000] base_lr: 2.4804e-05 lr: 4.0730e-06 eta: 8:58:27 time: 1.2308 data_time: 0.0669 memory: 6240 grad_norm: 1.1056 loss: 0.7255 detection_loss_cls: 0.7255 2024/07/08 03:08:07 - mmengine - INFO - Iter(train) [ 93600/120000] base_lr: 2.4721e-05 lr: 4.0655e-06 eta: 8:57:26 time: 1.2310 data_time: 0.0669 memory: 6240 grad_norm: 1.1055 loss: 0.7260 detection_loss_cls: 0.7260 2024/07/08 03:09:10 - mmengine - INFO - Iter(train) [ 93650/120000] base_lr: 2.4638e-05 lr: 4.0580e-06 eta: 8:56:25 time: 1.2314 data_time: 0.0669 memory: 6240 grad_norm: 1.1050 loss: 0.7262 detection_loss_cls: 0.7262 2024/07/08 03:10:11 - mmengine - INFO - Iter(train) [ 93700/120000] base_lr: 2.4556e-05 lr: 4.0505e-06 eta: 8:55:24 time: 1.2317 data_time: 0.0669 memory: 6240 grad_norm: 1.1048 loss: 0.7263 detection_loss_cls: 0.7263 2024/07/08 03:11:12 - mmengine - INFO - Iter(train) [ 93750/120000] base_lr: 2.4474e-05 lr: 4.0431e-06 eta: 8:54:23 time: 1.2317 data_time: 0.0669 memory: 6240 grad_norm: 1.1046 loss: 0.7258 detection_loss_cls: 0.7258 2024/07/08 03:12:13 - mmengine - INFO - Iter(train) [ 93800/120000] base_lr: 2.4391e-05 lr: 4.0356e-06 eta: 8:53:22 time: 1.2320 data_time: 0.0669 memory: 6240 grad_norm: 1.1049 loss: 0.7260 detection_loss_cls: 0.7260 2024/07/08 03:13:14 - mmengine - INFO - Iter(train) [ 93850/120000] base_lr: 2.4309e-05 lr: 4.0281e-06 eta: 8:52:21 time: 1.2322 data_time: 0.0668 memory: 6240 grad_norm: 1.1043 loss: 0.7252 detection_loss_cls: 0.7252 2024/07/08 03:14:15 - mmengine - INFO - Iter(train) [ 93900/120000] base_lr: 2.4228e-05 lr: 4.0207e-06 eta: 8:51:19 time: 1.2323 data_time: 0.0669 memory: 6240 grad_norm: 1.1043 loss: 0.7257 detection_loss_cls: 0.7257 2024/07/08 03:15:17 - mmengine - INFO - Iter(train) [ 93950/120000] base_lr: 2.4146e-05 lr: 4.0133e-06 eta: 8:50:19 time: 1.2325 data_time: 0.0668 memory: 6240 grad_norm: 1.1051 loss: 0.7253 detection_loss_cls: 0.7253 2024/07/08 03:16:18 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/08 03:16:18 - mmengine - INFO - Iter(train) [ 94000/120000] base_lr: 2.4064e-05 lr: 4.0058e-06 eta: 8:49:18 time: 1.2330 data_time: 0.0668 memory: 6240 grad_norm: 1.1054 loss: 0.7244 detection_loss_cls: 0.7244 2024/07/08 03:16:18 - mmengine - INFO - Saving checkpoint at 94000 iterations 2024/07/08 03:17:26 - mmengine - INFO - Iter(train) [ 94050/120000] base_lr: 2.3983e-05 lr: 3.9984e-06 eta: 8:48:19 time: 1.2279 data_time: 0.0616 memory: 6240 grad_norm: 1.1056 loss: 0.7234 detection_loss_cls: 0.7234 2024/07/08 03:18:28 - mmengine - INFO - Iter(train) [ 94100/120000] base_lr: 2.3901e-05 lr: 3.9910e-06 eta: 8:47:18 time: 1.2282 data_time: 0.0616 memory: 6240 grad_norm: 1.1054 loss: 0.7230 detection_loss_cls: 0.7230 2024/07/08 03:19:28 - mmengine - INFO - Iter(train) [ 94150/120000] base_lr: 2.3820e-05 lr: 3.9836e-06 eta: 8:46:17 time: 1.2281 data_time: 0.0616 memory: 6240 grad_norm: 1.1060 loss: 0.7226 detection_loss_cls: 0.7226 2024/07/08 03:20:28 - mmengine - INFO - Iter(train) [ 94200/120000] base_lr: 2.3739e-05 lr: 3.9763e-06 eta: 8:45:15 time: 1.2280 data_time: 0.0616 memory: 6240 grad_norm: 1.1063 loss: 0.7226 detection_loss_cls: 0.7226 2024/07/08 03:21:29 - mmengine - INFO - Iter(train) [ 94250/120000] base_lr: 2.3658e-05 lr: 3.9689e-06 eta: 8:44:14 time: 1.2282 data_time: 0.0615 memory: 6240 grad_norm: 1.1066 loss: 0.7224 detection_loss_cls: 0.7224 2024/07/08 03:22:29 - mmengine - INFO - Iter(train) [ 94300/120000] base_lr: 2.3577e-05 lr: 3.9616e-06 eta: 8:43:13 time: 1.2283 data_time: 0.0615 memory: 6240 grad_norm: 1.1066 loss: 0.7214 detection_loss_cls: 0.7214 2024/07/08 03:23:30 - mmengine - INFO - Iter(train) [ 94350/120000] base_lr: 2.3497e-05 lr: 3.9542e-06 eta: 8:42:12 time: 1.2283 data_time: 0.0615 memory: 6240 grad_norm: 1.1062 loss: 0.7213 detection_loss_cls: 0.7213 2024/07/08 03:24:31 - mmengine - INFO - Iter(train) [ 94400/120000] base_lr: 2.3416e-05 lr: 3.9469e-06 eta: 8:41:10 time: 1.2285 data_time: 0.0615 memory: 6240 grad_norm: 1.1062 loss: 0.7212 detection_loss_cls: 0.7212 2024/07/08 03:25:31 - mmengine - INFO - Iter(train) [ 94450/120000] base_lr: 2.3336e-05 lr: 3.9396e-06 eta: 8:40:09 time: 1.2285 data_time: 0.0615 memory: 6240 grad_norm: 1.1060 loss: 0.7208 detection_loss_cls: 0.7208 2024/07/08 03:26:31 - mmengine - INFO - Iter(train) [ 94500/120000] base_lr: 2.3255e-05 lr: 3.9323e-06 eta: 8:39:08 time: 1.2283 data_time: 0.0614 memory: 6240 grad_norm: 1.1061 loss: 0.7199 detection_loss_cls: 0.7199 2024/07/08 03:27:32 - mmengine - INFO - Iter(train) [ 94550/120000] base_lr: 2.3175e-05 lr: 3.9250e-06 eta: 8:38:06 time: 1.2284 data_time: 0.0614 memory: 6240 grad_norm: 1.1061 loss: 0.7198 detection_loss_cls: 0.7198 2024/07/08 03:28:32 - mmengine - INFO - Iter(train) [ 94600/120000] base_lr: 2.3095e-05 lr: 3.9177e-06 eta: 8:37:05 time: 1.2286 data_time: 0.0614 memory: 6240 grad_norm: 1.1064 loss: 0.7186 detection_loss_cls: 0.7186 2024/07/08 03:29:32 - mmengine - INFO - Iter(train) [ 94650/120000] base_lr: 2.3015e-05 lr: 3.9105e-06 eta: 8:36:04 time: 1.2284 data_time: 0.0614 memory: 6240 grad_norm: 1.1078 loss: 0.7191 detection_loss_cls: 0.7191 2024/07/08 03:30:34 - mmengine - INFO - Iter(train) [ 94700/120000] base_lr: 2.2935e-05 lr: 3.9032e-06 eta: 8:35:03 time: 1.2287 data_time: 0.0614 memory: 6240 grad_norm: 1.1077 loss: 0.7185 detection_loss_cls: 0.7185 2024/07/08 03:31:34 - mmengine - INFO - Iter(train) [ 94750/120000] base_lr: 2.2856e-05 lr: 3.8960e-06 eta: 8:34:01 time: 1.2288 data_time: 0.0614 memory: 6240 grad_norm: 1.1076 loss: 0.7197 detection_loss_cls: 0.7197 2024/07/08 03:32:34 - mmengine - INFO - Iter(train) [ 94800/120000] base_lr: 2.2776e-05 lr: 3.8887e-06 eta: 8:33:00 time: 1.2287 data_time: 0.0614 memory: 6240 grad_norm: 1.1084 loss: 0.7186 detection_loss_cls: 0.7186 2024/07/08 03:33:35 - mmengine - INFO - Iter(train) [ 94850/120000] base_lr: 2.2697e-05 lr: 3.8815e-06 eta: 8:31:59 time: 1.2287 data_time: 0.0614 memory: 6240 grad_norm: 1.1067 loss: 0.7180 detection_loss_cls: 0.7180 2024/07/08 03:34:35 - mmengine - INFO - Iter(train) [ 94900/120000] base_lr: 2.2618e-05 lr: 3.8743e-06 eta: 8:30:58 time: 1.2287 data_time: 0.0614 memory: 6240 grad_norm: 1.1062 loss: 0.7189 detection_loss_cls: 0.7189 2024/07/08 03:35:35 - mmengine - INFO - Iter(train) [ 94950/120000] base_lr: 2.2539e-05 lr: 3.8671e-06 eta: 8:29:56 time: 1.2286 data_time: 0.0615 memory: 6240 grad_norm: 1.1060 loss: 0.7197 detection_loss_cls: 0.7197 2024/07/08 03:36:37 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/08 03:36:37 - mmengine - INFO - Iter(train) [ 95000/120000] base_lr: 2.2460e-05 lr: 3.8600e-06 eta: 8:28:55 time: 1.2289 data_time: 0.0615 memory: 6240 grad_norm: 1.1059 loss: 0.7192 detection_loss_cls: 0.7192 2024/07/08 03:36:37 - mmengine - INFO - Saving checkpoint at 95000 iterations 2024/07/08 03:37:25 - mmengine - INFO - Iter(val) [ 50/105] eta: 0:00:44 time: 0.8004 data_time: 0.0291 memory: 6807 2024/07/08 03:38:05 - mmengine - INFO - Iter(val) [100/105] eta: 0:00:04 time: 0.8003 data_time: 0.0291 memory: 6807 2024/07/08 03:38:11 - mmengine - INFO - Evaluating bbox... 2024/07/08 03:38:37 - mmengine - INFO - bbox_mAP_copypaste: 0.420 0.589 0.450 0.205 0.467 0.603 2024/07/08 03:38:37 - mmengine - INFO - Iter(val) [105/105] coco/bbox_mAP: 0.4200 coco/bbox_mAP_50: 0.5890 coco/bbox_mAP_75: 0.4500 coco/bbox_mAP_s: 0.2050 coco/bbox_mAP_m: 0.4670 coco/bbox_mAP_l: 0.6030 data_time: 0.0287 time: 0.7925 2024/07/08 03:39:38 - mmengine - INFO - Iter(train) [ 95050/120000] base_lr: 2.2381e-05 lr: 3.8528e-06 eta: 8:28:03 time: 1.2339 data_time: 0.0667 memory: 6808 grad_norm: 1.1055 loss: 0.7194 detection_loss_cls: 0.7194 2024/07/08 03:40:38 - mmengine - INFO - Iter(train) [ 95100/120000] base_lr: 2.2302e-05 lr: 3.8456e-06 eta: 8:27:02 time: 1.2337 data_time: 0.0667 memory: 6240 grad_norm: 1.1058 loss: 0.7196 detection_loss_cls: 0.7196 2024/07/08 03:41:39 - 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mmengine - INFO - Iter(train) [ 96600/120000] base_lr: 2.0005e-05 lr: 3.6368e-06 eta: 7:56:24 time: 1.2268 data_time: 0.0668 memory: 6240 grad_norm: 1.1111 loss: 0.7217 detection_loss_cls: 0.7217 2024/07/08 04:11:57 - mmengine - INFO - Iter(train) [ 96650/120000] base_lr: 1.9930e-05 lr: 3.6300e-06 eta: 7:55:23 time: 1.2270 data_time: 0.0668 memory: 6240 grad_norm: 1.1117 loss: 0.7216 detection_loss_cls: 0.7216 2024/07/08 04:12:56 - mmengine - INFO - Iter(train) [ 96700/120000] base_lr: 1.9856e-05 lr: 3.6233e-06 eta: 7:54:22 time: 1.2267 data_time: 0.0667 memory: 6240 grad_norm: 1.1122 loss: 0.7208 detection_loss_cls: 0.7208 2024/07/08 04:13:57 - mmengine - INFO - Iter(train) [ 96750/120000] base_lr: 1.9782e-05 lr: 3.6165e-06 eta: 7:53:21 time: 1.2264 data_time: 0.0667 memory: 6240 grad_norm: 1.1129 loss: 0.7205 detection_loss_cls: 0.7205 2024/07/08 04:14:57 - mmengine - INFO - Iter(train) [ 96800/120000] base_lr: 1.9708e-05 lr: 3.6098e-06 eta: 7:52:19 time: 1.2263 data_time: 0.0667 memory: 6240 grad_norm: 1.1141 loss: 0.7207 detection_loss_cls: 0.7207 2024/07/08 04:15:57 - 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mmengine - INFO - Saving checkpoint at 98000 iterations 2024/07/08 04:40:36 - mmengine - INFO - Iter(train) [ 98050/120000] base_lr: 1.7903e-05 lr: 3.4457e-06 eta: 7:26:55 time: 1.2241 data_time: 0.0666 memory: 6240 grad_norm: 1.1153 loss: 0.7178 detection_loss_cls: 0.7178 2024/07/08 04:41:37 - mmengine - INFO - Iter(train) [ 98100/120000] base_lr: 1.7832e-05 lr: 3.4393e-06 eta: 7:25:54 time: 1.2239 data_time: 0.0667 memory: 6240 grad_norm: 1.1156 loss: 0.7185 detection_loss_cls: 0.7185 2024/07/08 04:42:39 - mmengine - INFO - Iter(train) [ 98150/120000] base_lr: 1.7762e-05 lr: 3.4329e-06 eta: 7:24:53 time: 1.2243 data_time: 0.0667 memory: 6240 grad_norm: 1.1164 loss: 0.7179 detection_loss_cls: 0.7179 2024/07/08 04:43:39 - mmengine - INFO - Iter(train) [ 98200/120000] base_lr: 1.7692e-05 lr: 3.4265e-06 eta: 7:23:52 time: 1.2244 data_time: 0.0667 memory: 6240 grad_norm: 1.1163 loss: 0.7174 detection_loss_cls: 0.7174 2024/07/08 04:44:40 - mmengine - INFO - Iter(train) [ 98250/120000] base_lr: 1.7622e-05 lr: 3.4202e-06 eta: 7:22:51 time: 1.2244 data_time: 0.0667 memory: 6240 grad_norm: 1.1160 loss: 0.7170 detection_loss_cls: 0.7170 2024/07/08 04:45:41 - 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mmengine - INFO - Saving checkpoint at 99000 iterations 2024/07/08 05:01:04 - mmengine - INFO - Iter(train) [ 99050/120000] base_lr: 1.6522e-05 lr: 3.3202e-06 eta: 7:06:35 time: 1.2213 data_time: 0.0617 memory: 6240 grad_norm: 1.1232 loss: 0.7186 detection_loss_cls: 0.7186 2024/07/08 05:02:05 - mmengine - INFO - Iter(train) [ 99100/120000] base_lr: 1.6455e-05 lr: 3.3141e-06 eta: 7:05:34 time: 1.2213 data_time: 0.0617 memory: 6240 grad_norm: 1.1232 loss: 0.7183 detection_loss_cls: 0.7183 2024/07/08 05:03:06 - mmengine - INFO - Iter(train) [ 99150/120000] base_lr: 1.6387e-05 lr: 3.3080e-06 eta: 7:04:33 time: 1.2213 data_time: 0.0617 memory: 6240 grad_norm: 1.1233 loss: 0.7182 detection_loss_cls: 0.7182 2024/07/08 05:04:07 - mmengine - INFO - Iter(train) [ 99200/120000] base_lr: 1.6320e-05 lr: 3.3018e-06 eta: 7:03:32 time: 1.2215 data_time: 0.0617 memory: 6240 grad_norm: 1.1237 loss: 0.7178 detection_loss_cls: 0.7178 2024/07/08 05:05:07 - mmengine - INFO - Iter(train) [ 99250/120000] base_lr: 1.6253e-05 lr: 3.2957e-06 eta: 7:02:31 time: 1.2214 data_time: 0.0617 memory: 6240 grad_norm: 1.1253 loss: 0.7173 detection_loss_cls: 0.7173 2024/07/08 05:06:08 - mmengine - INFO - Iter(train) [ 99300/120000] base_lr: 1.6186e-05 lr: 3.2897e-06 eta: 7:01:29 time: 1.2213 data_time: 0.0617 memory: 6240 grad_norm: 1.1228 loss: 0.7174 detection_loss_cls: 0.7174 2024/07/08 05:07:09 - mmengine - INFO - Iter(train) [ 99350/120000] base_lr: 1.6120e-05 lr: 3.2836e-06 eta: 7:00:28 time: 1.2217 data_time: 0.0618 memory: 6240 grad_norm: 1.1236 loss: 0.7180 detection_loss_cls: 0.7180 2024/07/08 05:08:09 - mmengine - INFO - Iter(train) [ 99400/120000] base_lr: 1.6053e-05 lr: 3.2775e-06 eta: 6:59:27 time: 1.2215 data_time: 0.0618 memory: 6240 grad_norm: 1.1235 loss: 0.7180 detection_loss_cls: 0.7180 2024/07/08 05:09:10 - mmengine - INFO - Iter(train) [ 99450/120000] base_lr: 1.5986e-05 lr: 3.2715e-06 eta: 6:58:26 time: 1.2216 data_time: 0.0618 memory: 6240 grad_norm: 1.1234 loss: 0.7184 detection_loss_cls: 0.7184 2024/07/08 05:10:11 - mmengine - INFO - Iter(train) [ 99500/120000] base_lr: 1.5920e-05 lr: 3.2655e-06 eta: 6:57:25 time: 1.2219 data_time: 0.0618 memory: 6240 grad_norm: 1.1232 loss: 0.7185 detection_loss_cls: 0.7185 2024/07/08 05:11:12 - mmengine - INFO - Iter(train) [ 99550/120000] base_lr: 1.5854e-05 lr: 3.2594e-06 eta: 6:56:24 time: 1.2219 data_time: 0.0619 memory: 6240 grad_norm: 1.1228 loss: 0.7188 detection_loss_cls: 0.7188 2024/07/08 05:12:12 - mmengine - INFO - Iter(train) [ 99600/120000] base_lr: 1.5788e-05 lr: 3.2534e-06 eta: 6:55:22 time: 1.2217 data_time: 0.0619 memory: 6240 grad_norm: 1.1231 loss: 0.7189 detection_loss_cls: 0.7189 2024/07/08 05:13:13 - mmengine - INFO - Iter(train) [ 99650/120000] base_lr: 1.5722e-05 lr: 3.2475e-06 eta: 6:54:21 time: 1.2220 data_time: 0.0618 memory: 6240 grad_norm: 1.1224 loss: 0.7180 detection_loss_cls: 0.7180 2024/07/08 05:14:14 - mmengine - INFO - Iter(train) [ 99700/120000] base_lr: 1.5656e-05 lr: 3.2415e-06 eta: 6:53:20 time: 1.2223 data_time: 0.0619 memory: 6240 grad_norm: 1.1225 loss: 0.7177 detection_loss_cls: 0.7177 2024/07/08 05:15:15 - mmengine - INFO - Iter(train) [ 99750/120000] base_lr: 1.5591e-05 lr: 3.2355e-06 eta: 6:52:19 time: 1.2223 data_time: 0.0619 memory: 6240 grad_norm: 1.1224 loss: 0.7178 detection_loss_cls: 0.7178 2024/07/08 05:16:16 - mmengine - INFO - Iter(train) [ 99800/120000] base_lr: 1.5525e-05 lr: 3.2296e-06 eta: 6:51:18 time: 1.2226 data_time: 0.0618 memory: 6240 grad_norm: 1.1223 loss: 0.7172 detection_loss_cls: 0.7172 2024/07/08 05:17:17 - mmengine - INFO - Iter(train) [ 99850/120000] base_lr: 1.5460e-05 lr: 3.2236e-06 eta: 6:50:17 time: 1.2226 data_time: 0.0618 memory: 6240 grad_norm: 1.1225 loss: 0.7174 detection_loss_cls: 0.7174 2024/07/08 05:18:17 - mmengine - INFO - Iter(train) [ 99900/120000] base_lr: 1.5395e-05 lr: 3.2177e-06 eta: 6:49:15 time: 1.2226 data_time: 0.0618 memory: 6240 grad_norm: 1.1226 loss: 0.7169 detection_loss_cls: 0.7169 2024/07/08 05:19:18 - mmengine - INFO - Iter(train) [ 99950/120000] base_lr: 1.5330e-05 lr: 3.2118e-06 eta: 6:48:14 time: 1.2229 data_time: 0.0619 memory: 6240 grad_norm: 1.1225 loss: 0.7175 detection_loss_cls: 0.7175 2024/07/08 05:20:19 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/08 05:20:19 - mmengine - INFO - Iter(train) [100000/120000] base_lr: 1.5265e-05 lr: 3.2059e-06 eta: 6:47:13 time: 1.2230 data_time: 0.0619 memory: 6240 grad_norm: 1.1224 loss: 0.7174 detection_loss_cls: 0.7174 2024/07/08 05:20:19 - mmengine - INFO - Saving checkpoint at 100000 iterations 2024/07/08 05:21:06 - mmengine - INFO - Iter(val) [ 50/105] eta: 0:00:43 time: 0.8000 data_time: 0.0290 memory: 6809 2024/07/08 05:21:46 - mmengine - INFO - Iter(val) [100/105] eta: 0:00:03 time: 0.7999 data_time: 0.0290 memory: 6807 2024/07/08 05:21:52 - mmengine - INFO - Evaluating bbox... 2024/07/08 05:22:18 - mmengine - INFO - bbox_mAP_copypaste: 0.420 0.589 0.449 0.204 0.466 0.602 2024/07/08 05:22:18 - mmengine - INFO - Iter(val) [105/105] coco/bbox_mAP: 0.4200 coco/bbox_mAP_50: 0.5890 coco/bbox_mAP_75: 0.4490 coco/bbox_mAP_s: 0.2040 coco/bbox_mAP_m: 0.4660 coco/bbox_mAP_l: 0.6020 data_time: 0.0278 time: 0.7892 2024/07/08 05:23:19 - mmengine - INFO - Iter(train) [100050/120000] base_lr: 1.5200e-05 lr: 3.2000e-06 eta: 6:46:19 time: 1.2281 data_time: 0.0669 memory: 6804 grad_norm: 1.1227 loss: 0.7167 detection_loss_cls: 0.7167 2024/07/08 05:24:20 - mmengine - INFO - Iter(train) [100100/120000] base_lr: 1.5135e-05 lr: 3.1941e-06 eta: 6:45:18 time: 1.2285 data_time: 0.0669 memory: 6240 grad_norm: 1.1232 loss: 0.7161 detection_loss_cls: 0.7161 2024/07/08 05:25:21 - mmengine - INFO - Iter(train) [100150/120000] base_lr: 1.5071e-05 lr: 3.1883e-06 eta: 6:44:16 time: 1.2286 data_time: 0.0669 memory: 6240 grad_norm: 1.1230 loss: 0.7157 detection_loss_cls: 0.7157 2024/07/08 05:26:21 - mmengine - INFO - Iter(train) [100200/120000] base_lr: 1.5007e-05 lr: 3.1824e-06 eta: 6:43:15 time: 1.2286 data_time: 0.0668 memory: 6240 grad_norm: 1.1242 loss: 0.7145 detection_loss_cls: 0.7145 2024/07/08 05:27:22 - mmengine - INFO - Iter(train) [100250/120000] base_lr: 1.4943e-05 lr: 3.1766e-06 eta: 6:42:14 time: 1.2287 data_time: 0.0667 memory: 6240 grad_norm: 1.1246 loss: 0.7139 detection_loss_cls: 0.7139 2024/07/08 05:28:22 - mmengine - INFO - Iter(train) [100300/120000] base_lr: 1.4879e-05 lr: 3.1708e-06 eta: 6:41:12 time: 1.2288 data_time: 0.0667 memory: 6240 grad_norm: 1.1255 loss: 0.7140 detection_loss_cls: 0.7140 2024/07/08 05:29:22 - mmengine - INFO - Iter(train) [100350/120000] base_lr: 1.4815e-05 lr: 3.1650e-06 eta: 6:40:11 time: 1.2288 data_time: 0.0667 memory: 6240 grad_norm: 1.1260 loss: 0.7139 detection_loss_cls: 0.7139 2024/07/08 05:30:23 - mmengine - INFO - Iter(train) [100400/120000] base_lr: 1.4751e-05 lr: 3.1592e-06 eta: 6:39:10 time: 1.2289 data_time: 0.0667 memory: 6240 grad_norm: 1.1266 loss: 0.7133 detection_loss_cls: 0.7133 2024/07/08 05:31:23 - mmengine - INFO - Iter(train) [100450/120000] base_lr: 1.4688e-05 lr: 3.1534e-06 eta: 6:38:09 time: 1.2288 data_time: 0.0667 memory: 6240 grad_norm: 1.1262 loss: 0.7128 detection_loss_cls: 0.7128 2024/07/08 05:32:23 - mmengine - INFO - Iter(train) [100500/120000] base_lr: 1.4624e-05 lr: 3.1477e-06 eta: 6:37:07 time: 1.2287 data_time: 0.0666 memory: 6240 grad_norm: 1.1261 loss: 0.7125 detection_loss_cls: 0.7125 2024/07/08 05:33:24 - mmengine - INFO - Iter(train) [100550/120000] base_lr: 1.4561e-05 lr: 3.1419e-06 eta: 6:36:06 time: 1.2288 data_time: 0.0666 memory: 6240 grad_norm: 1.1253 loss: 0.7122 detection_loss_cls: 0.7122 2024/07/08 05:34:24 - mmengine - INFO - Iter(train) [100600/120000] base_lr: 1.4498e-05 lr: 3.1362e-06 eta: 6:35:05 time: 1.2289 data_time: 0.0666 memory: 6240 grad_norm: 1.1267 loss: 0.7117 detection_loss_cls: 0.7117 2024/07/08 05:35:25 - mmengine - INFO - Iter(train) [100650/120000] base_lr: 1.4435e-05 lr: 3.1304e-06 eta: 6:34:04 time: 1.2288 data_time: 0.0666 memory: 6240 grad_norm: 1.1264 loss: 0.7118 detection_loss_cls: 0.7118 2024/07/08 05:36:25 - mmengine - INFO - Iter(train) [100700/120000] base_lr: 1.4372e-05 lr: 3.1247e-06 eta: 6:33:02 time: 1.2290 data_time: 0.0666 memory: 6240 grad_norm: 1.1265 loss: 0.7127 detection_loss_cls: 0.7127 2024/07/08 05:37:25 - mmengine - INFO - Iter(train) [100750/120000] base_lr: 1.4309e-05 lr: 3.1190e-06 eta: 6:32:01 time: 1.2289 data_time: 0.0667 memory: 6240 grad_norm: 1.1262 loss: 0.7128 detection_loss_cls: 0.7128 2024/07/08 05:38:25 - mmengine - INFO - Iter(train) [100800/120000] base_lr: 1.4247e-05 lr: 3.1134e-06 eta: 6:31:00 time: 1.2289 data_time: 0.0667 memory: 6240 grad_norm: 1.1255 loss: 0.7135 detection_loss_cls: 0.7135 2024/07/08 05:39:26 - mmengine - INFO - Iter(train) [100850/120000] base_lr: 1.4185e-05 lr: 3.1077e-06 eta: 6:29:59 time: 1.2292 data_time: 0.0667 memory: 6240 grad_norm: 1.1254 loss: 0.7139 detection_loss_cls: 0.7139 2024/07/08 05:40:26 - mmengine - INFO - Iter(train) [100900/120000] base_lr: 1.4122e-05 lr: 3.1020e-06 eta: 6:28:57 time: 1.2292 data_time: 0.0667 memory: 6240 grad_norm: 1.1263 loss: 0.7129 detection_loss_cls: 0.7129 2024/07/08 05:41:27 - mmengine - INFO - Iter(train) [100950/120000] base_lr: 1.4060e-05 lr: 3.0964e-06 eta: 6:27:56 time: 1.2292 data_time: 0.0667 memory: 6240 grad_norm: 1.1266 loss: 0.7133 detection_loss_cls: 0.7133 2024/07/08 05:42:28 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/08 05:42:28 - mmengine - INFO - Iter(train) [101000/120000] base_lr: 1.3998e-05 lr: 3.0908e-06 eta: 6:26:55 time: 1.2293 data_time: 0.0667 memory: 6240 grad_norm: 1.1270 loss: 0.7133 detection_loss_cls: 0.7133 2024/07/08 05:42:28 - mmengine - INFO - Saving checkpoint at 101000 iterations 2024/07/08 05:43:36 - mmengine - INFO - Iter(train) [101050/120000] base_lr: 1.3937e-05 lr: 3.0851e-06 eta: 6:25:55 time: 1.2291 data_time: 0.0666 memory: 6240 grad_norm: 1.1272 loss: 0.7121 detection_loss_cls: 0.7121 2024/07/08 05:44:36 - mmengine - INFO - Iter(train) [101100/120000] base_lr: 1.3875e-05 lr: 3.0795e-06 eta: 6:24:54 time: 1.2288 data_time: 0.0666 memory: 6240 grad_norm: 1.1269 loss: 0.7123 detection_loss_cls: 0.7123 2024/07/08 05:45:38 - mmengine - INFO - Iter(train) [101150/120000] base_lr: 1.3813e-05 lr: 3.0740e-06 eta: 6:23:53 time: 1.2291 data_time: 0.0666 memory: 6240 grad_norm: 1.1271 loss: 0.7118 detection_loss_cls: 0.7118 2024/07/08 05:46:38 - mmengine - INFO - Iter(train) [101200/120000] base_lr: 1.3752e-05 lr: 3.0684e-06 eta: 6:22:52 time: 1.2291 data_time: 0.0665 memory: 6240 grad_norm: 1.1266 loss: 0.7111 detection_loss_cls: 0.7111 2024/07/08 05:47:40 - mmengine - INFO - Iter(train) [101250/120000] base_lr: 1.3691e-05 lr: 3.0628e-06 eta: 6:21:51 time: 1.2292 data_time: 0.0665 memory: 6240 grad_norm: 1.1267 loss: 0.7105 detection_loss_cls: 0.7105 2024/07/08 05:48:41 - mmengine - INFO - Iter(train) [101300/120000] base_lr: 1.3630e-05 lr: 3.0573e-06 eta: 6:20:50 time: 1.2295 data_time: 0.0665 memory: 6240 grad_norm: 1.1276 loss: 0.7104 detection_loss_cls: 0.7104 2024/07/08 05:49:42 - mmengine - INFO - Iter(train) [101350/120000] base_lr: 1.3569e-05 lr: 3.0517e-06 eta: 6:19:49 time: 1.2292 data_time: 0.0665 memory: 6240 grad_norm: 1.1283 loss: 0.7107 detection_loss_cls: 0.7107 2024/07/08 05:50:42 - mmengine - INFO - Iter(train) [101400/120000] base_lr: 1.3508e-05 lr: 3.0462e-06 eta: 6:18:47 time: 1.2291 data_time: 0.0666 memory: 6240 grad_norm: 1.1284 loss: 0.7111 detection_loss_cls: 0.7111 2024/07/08 05:51:43 - mmengine - INFO - Iter(train) [101450/120000] base_lr: 1.3448e-05 lr: 3.0407e-06 eta: 6:17:46 time: 1.2291 data_time: 0.0666 memory: 6240 grad_norm: 1.1283 loss: 0.7110 detection_loss_cls: 0.7110 2024/07/08 05:52:44 - mmengine - INFO - Iter(train) [101500/120000] base_lr: 1.3387e-05 lr: 3.0352e-06 eta: 6:16:45 time: 1.2290 data_time: 0.0666 memory: 6240 grad_norm: 1.1290 loss: 0.7119 detection_loss_cls: 0.7119 2024/07/08 05:53:45 - mmengine - INFO - Iter(train) [101550/120000] base_lr: 1.3327e-05 lr: 3.0297e-06 eta: 6:15:44 time: 1.2290 data_time: 0.0666 memory: 6240 grad_norm: 1.1290 loss: 0.7118 detection_loss_cls: 0.7118 2024/07/08 05:54:46 - mmengine - INFO - Iter(train) [101600/120000] base_lr: 1.3267e-05 lr: 3.0243e-06 eta: 6:14:43 time: 1.2290 data_time: 0.0665 memory: 6240 grad_norm: 1.1286 loss: 0.7108 detection_loss_cls: 0.7108 2024/07/08 05:55:46 - mmengine - INFO - Iter(train) [101650/120000] base_lr: 1.3207e-05 lr: 3.0188e-06 eta: 6:13:42 time: 1.2287 data_time: 0.0665 memory: 6240 grad_norm: 1.1284 loss: 0.7102 detection_loss_cls: 0.7102 2024/07/08 05:56:47 - mmengine - INFO - Iter(train) [101700/120000] base_lr: 1.3147e-05 lr: 3.0134e-06 eta: 6:12:40 time: 1.2284 data_time: 0.0665 memory: 6240 grad_norm: 1.1284 loss: 0.7098 detection_loss_cls: 0.7098 2024/07/08 05:57:48 - mmengine - INFO - Iter(train) [101750/120000] base_lr: 1.3088e-05 lr: 3.0080e-06 eta: 6:11:39 time: 1.2286 data_time: 0.0664 memory: 6240 grad_norm: 1.1287 loss: 0.7092 detection_loss_cls: 0.7092 2024/07/08 05:58:49 - mmengine - INFO - Iter(train) [101800/120000] base_lr: 1.3028e-05 lr: 3.0026e-06 eta: 6:10:38 time: 1.2287 data_time: 0.0665 memory: 6240 grad_norm: 1.1287 loss: 0.7093 detection_loss_cls: 0.7093 2024/07/08 05:59:51 - mmengine - INFO - Iter(train) [101850/120000] base_lr: 1.2969e-05 lr: 2.9972e-06 eta: 6:09:37 time: 1.2287 data_time: 0.0664 memory: 6240 grad_norm: 1.1299 loss: 0.7082 detection_loss_cls: 0.7082 2024/07/08 06:00:52 - mmengine - INFO - Iter(train) [101900/120000] base_lr: 1.2910e-05 lr: 2.9918e-06 eta: 6:08:36 time: 1.2287 data_time: 0.0664 memory: 6240 grad_norm: 1.1300 loss: 0.7085 detection_loss_cls: 0.7085 2024/07/08 06:01:52 - mmengine - INFO - Iter(train) [101950/120000] base_lr: 1.2850e-05 lr: 2.9864e-06 eta: 6:07:35 time: 1.2283 data_time: 0.0664 memory: 6240 grad_norm: 1.1300 loss: 0.7080 detection_loss_cls: 0.7080 2024/07/08 06:02:53 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/08 06:02:53 - mmengine - INFO - Iter(train) [102000/120000] base_lr: 1.2792e-05 lr: 2.9810e-06 eta: 6:06:34 time: 1.2283 data_time: 0.0664 memory: 6240 grad_norm: 1.1302 loss: 0.7074 detection_loss_cls: 0.7074 2024/07/08 06:02:53 - mmengine - INFO - Saving checkpoint at 102000 iterations 2024/07/08 06:04:01 - mmengine - INFO - Iter(train) [102050/120000] base_lr: 1.2733e-05 lr: 2.9757e-06 eta: 6:05:34 time: 1.2280 data_time: 0.0664 memory: 6240 grad_norm: 1.1303 loss: 0.7076 detection_loss_cls: 0.7076 2024/07/08 06:05:02 - mmengine - INFO - Iter(train) [102100/120000] base_lr: 1.2674e-05 lr: 2.9704e-06 eta: 6:04:33 time: 1.2281 data_time: 0.0663 memory: 6240 grad_norm: 1.1305 loss: 0.7073 detection_loss_cls: 0.7073 2024/07/08 06:06:03 - mmengine - INFO - Iter(train) [102150/120000] base_lr: 1.2616e-05 lr: 2.9651e-06 eta: 6:03:32 time: 1.2278 data_time: 0.0664 memory: 6240 grad_norm: 1.1298 loss: 0.7082 detection_loss_cls: 0.7082 2024/07/08 06:07:04 - mmengine - INFO - Iter(train) [102200/120000] base_lr: 1.2557e-05 lr: 2.9598e-06 eta: 6:02:31 time: 1.2281 data_time: 0.0664 memory: 6240 grad_norm: 1.1303 loss: 0.7084 detection_loss_cls: 0.7084 2024/07/08 06:08:05 - mmengine - INFO - Iter(train) [102250/120000] base_lr: 1.2499e-05 lr: 2.9545e-06 eta: 6:01:30 time: 1.2282 data_time: 0.0664 memory: 6240 grad_norm: 1.1308 loss: 0.7081 detection_loss_cls: 0.7081 2024/07/08 06:09:06 - mmengine - INFO - Iter(train) [102300/120000] base_lr: 1.2441e-05 lr: 2.9492e-06 eta: 6:00:28 time: 1.2280 data_time: 0.0663 memory: 6240 grad_norm: 1.1302 loss: 0.7074 detection_loss_cls: 0.7074 2024/07/08 06:10:07 - mmengine - INFO - Iter(train) [102350/120000] base_lr: 1.2383e-05 lr: 2.9439e-06 eta: 5:59:27 time: 1.2279 data_time: 0.0663 memory: 6240 grad_norm: 1.1300 loss: 0.7072 detection_loss_cls: 0.7072 2024/07/08 06:11:08 - mmengine - INFO - Iter(train) [102400/120000] base_lr: 1.2326e-05 lr: 2.9387e-06 eta: 5:58:26 time: 1.2280 data_time: 0.0663 memory: 6240 grad_norm: 1.1305 loss: 0.7068 detection_loss_cls: 0.7068 2024/07/08 06:12:08 - mmengine - INFO - Iter(train) [102450/120000] base_lr: 1.2268e-05 lr: 2.9335e-06 eta: 5:57:25 time: 1.2279 data_time: 0.0662 memory: 6240 grad_norm: 1.1296 loss: 0.7054 detection_loss_cls: 0.7054 2024/07/08 06:13:10 - mmengine - INFO - Iter(train) [102500/120000] base_lr: 1.2211e-05 lr: 2.9282e-06 eta: 5:56:24 time: 1.2280 data_time: 0.0662 memory: 6240 grad_norm: 1.1298 loss: 0.7052 detection_loss_cls: 0.7052 2024/07/08 06:14:11 - mmengine - INFO - Iter(train) [102550/120000] base_lr: 1.2154e-05 lr: 2.9230e-06 eta: 5:55:23 time: 1.2279 data_time: 0.0662 memory: 6240 grad_norm: 1.1298 loss: 0.7047 detection_loss_cls: 0.7047 2024/07/08 06:15:11 - mmengine - INFO - Iter(train) [102600/120000] base_lr: 1.2096e-05 lr: 2.9179e-06 eta: 5:54:21 time: 1.2277 data_time: 0.0662 memory: 6240 grad_norm: 1.1292 loss: 0.7048 detection_loss_cls: 0.7048 2024/07/08 06:16:12 - mmengine - INFO - Iter(train) [102650/120000] base_lr: 1.2039e-05 lr: 2.9127e-06 eta: 5:53:20 time: 1.2277 data_time: 0.0661 memory: 6240 grad_norm: 1.1295 loss: 0.7045 detection_loss_cls: 0.7045 2024/07/08 06:17:13 - mmengine - INFO - Iter(train) [102700/120000] base_lr: 1.1983e-05 lr: 2.9075e-06 eta: 5:52:19 time: 1.2276 data_time: 0.0661 memory: 6240 grad_norm: 1.1291 loss: 0.7048 detection_loss_cls: 0.7048 2024/07/08 06:18:14 - mmengine - INFO - Iter(train) [102750/120000] base_lr: 1.1926e-05 lr: 2.9024e-06 eta: 5:51:18 time: 1.2276 data_time: 0.0661 memory: 6240 grad_norm: 1.1289 loss: 0.7047 detection_loss_cls: 0.7047 2024/07/08 06:19:15 - mmengine - INFO - Iter(train) [102800/120000] base_lr: 1.1870e-05 lr: 2.8972e-06 eta: 5:50:17 time: 1.2276 data_time: 0.0661 memory: 6240 grad_norm: 1.1286 loss: 0.7053 detection_loss_cls: 0.7053 2024/07/08 06:20:16 - mmengine - INFO - Iter(train) [102850/120000] base_lr: 1.1813e-05 lr: 2.8921e-06 eta: 5:49:16 time: 1.2276 data_time: 0.0662 memory: 6240 grad_norm: 1.1281 loss: 0.7050 detection_loss_cls: 0.7050 2024/07/08 06:21:17 - mmengine - INFO - Iter(train) [102900/120000] base_lr: 1.1757e-05 lr: 2.8870e-06 eta: 5:48:15 time: 1.2275 data_time: 0.0661 memory: 6240 grad_norm: 1.1286 loss: 0.7038 detection_loss_cls: 0.7038 2024/07/08 06:22:18 - mmengine - INFO - Iter(train) [102950/120000] base_lr: 1.1701e-05 lr: 2.8819e-06 eta: 5:47:14 time: 1.2276 data_time: 0.0661 memory: 6240 grad_norm: 1.1288 loss: 0.7037 detection_loss_cls: 0.7037 2024/07/08 06:23:19 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/08 06:23:19 - mmengine - INFO - Iter(train) [103000/120000] base_lr: 1.1645e-05 lr: 2.8768e-06 eta: 5:46:12 time: 1.2277 data_time: 0.0661 memory: 6240 grad_norm: 1.1284 loss: 0.7035 detection_loss_cls: 0.7035 2024/07/08 06:23:19 - mmengine - INFO - Saving checkpoint at 103000 iterations 2024/07/08 06:24:28 - mmengine - INFO - Iter(train) [103050/120000] base_lr: 1.1589e-05 lr: 2.8718e-06 eta: 5:45:13 time: 1.2276 data_time: 0.0660 memory: 6240 grad_norm: 1.1286 loss: 0.7035 detection_loss_cls: 0.7035 2024/07/08 06:25:29 - mmengine - INFO - Iter(train) [103100/120000] base_lr: 1.1534e-05 lr: 2.8667e-06 eta: 5:44:12 time: 1.2277 data_time: 0.0661 memory: 6240 grad_norm: 1.1288 loss: 0.7039 detection_loss_cls: 0.7039 2024/07/08 06:26:29 - mmengine - INFO - Iter(train) [103150/120000] base_lr: 1.1478e-05 lr: 2.8617e-06 eta: 5:43:11 time: 1.2276 data_time: 0.0660 memory: 6240 grad_norm: 1.1290 loss: 0.7027 detection_loss_cls: 0.7027 2024/07/08 06:27:30 - mmengine - INFO - Iter(train) [103200/120000] base_lr: 1.1423e-05 lr: 2.8567e-06 eta: 5:42:09 time: 1.2275 data_time: 0.0659 memory: 6240 grad_norm: 1.1288 loss: 0.7014 detection_loss_cls: 0.7014 2024/07/08 06:28:31 - mmengine - INFO - Iter(train) [103250/120000] base_lr: 1.1368e-05 lr: 2.8516e-06 eta: 5:41:08 time: 1.2277 data_time: 0.0659 memory: 6240 grad_norm: 1.1274 loss: 0.7030 detection_loss_cls: 0.7030 2024/07/08 06:29:31 - mmengine - INFO - Iter(train) [103300/120000] base_lr: 1.1313e-05 lr: 2.8467e-06 eta: 5:40:07 time: 1.2276 data_time: 0.0659 memory: 6240 grad_norm: 1.1280 loss: 0.7034 detection_loss_cls: 0.7034 2024/07/08 06:30:32 - mmengine - INFO - Iter(train) [103350/120000] base_lr: 1.1258e-05 lr: 2.8417e-06 eta: 5:39:06 time: 1.2276 data_time: 0.0659 memory: 6240 grad_norm: 1.1268 loss: 0.7031 detection_loss_cls: 0.7031 2024/07/08 06:31:34 - mmengine - INFO - Iter(train) [103400/120000] base_lr: 1.1204e-05 lr: 2.8367e-06 eta: 5:38:05 time: 1.2279 data_time: 0.0659 memory: 6240 grad_norm: 1.1281 loss: 0.7024 detection_loss_cls: 0.7024 2024/07/08 06:32:34 - mmengine - INFO - Iter(train) [103450/120000] base_lr: 1.1149e-05 lr: 2.8317e-06 eta: 5:37:04 time: 1.2279 data_time: 0.0659 memory: 6240 grad_norm: 1.1288 loss: 0.7020 detection_loss_cls: 0.7020 2024/07/08 06:33:35 - mmengine - INFO - Iter(train) [103500/120000] base_lr: 1.1095e-05 lr: 2.8268e-06 eta: 5:36:02 time: 1.2278 data_time: 0.0659 memory: 6240 grad_norm: 1.1291 loss: 0.7019 detection_loss_cls: 0.7019 2024/07/08 06:34:37 - mmengine - INFO - Iter(train) [103550/120000] base_lr: 1.1041e-05 lr: 2.8219e-06 eta: 5:35:01 time: 1.2280 data_time: 0.0659 memory: 6240 grad_norm: 1.1291 loss: 0.7019 detection_loss_cls: 0.7019 2024/07/08 06:35:37 - mmengine - INFO - Iter(train) [103600/120000] base_lr: 1.0987e-05 lr: 2.8170e-06 eta: 5:34:00 time: 1.2280 data_time: 0.0659 memory: 6240 grad_norm: 1.1293 loss: 0.7019 detection_loss_cls: 0.7019 2024/07/08 06:36:38 - mmengine - INFO - Iter(train) [103650/120000] base_lr: 1.0933e-05 lr: 2.8121e-06 eta: 5:32:59 time: 1.2281 data_time: 0.0658 memory: 6240 grad_norm: 1.1295 loss: 0.7019 detection_loss_cls: 0.7019 2024/07/08 06:37:39 - mmengine - INFO - Iter(train) [103700/120000] base_lr: 1.0879e-05 lr: 2.8072e-06 eta: 5:31:58 time: 1.2281 data_time: 0.0658 memory: 6240 grad_norm: 1.1294 loss: 0.7024 detection_loss_cls: 0.7024 2024/07/08 06:38:40 - mmengine - INFO - Iter(train) [103750/120000] base_lr: 1.0826e-05 lr: 2.8023e-06 eta: 5:30:57 time: 1.2280 data_time: 0.0658 memory: 6240 grad_norm: 1.1300 loss: 0.7021 detection_loss_cls: 0.7021 2024/07/08 06:39:41 - mmengine - INFO - Iter(train) [103800/120000] base_lr: 1.0772e-05 lr: 2.7975e-06 eta: 5:29:56 time: 1.2280 data_time: 0.0659 memory: 6240 grad_norm: 1.1301 loss: 0.7026 detection_loss_cls: 0.7026 2024/07/08 06:40:42 - mmengine - INFO - Iter(train) [103850/120000] base_lr: 1.0719e-05 lr: 2.7926e-06 eta: 5:28:54 time: 1.2281 data_time: 0.0659 memory: 6240 grad_norm: 1.1298 loss: 0.7027 detection_loss_cls: 0.7027 2024/07/08 06:41:42 - mmengine - INFO - Iter(train) [103900/120000] base_lr: 1.0666e-05 lr: 2.7878e-06 eta: 5:27:53 time: 1.2280 data_time: 0.0659 memory: 6240 grad_norm: 1.1300 loss: 0.7024 detection_loss_cls: 0.7024 2024/07/08 06:42:44 - mmengine - INFO - Iter(train) [103950/120000] base_lr: 1.0613e-05 lr: 2.7830e-06 eta: 5:26:52 time: 1.2281 data_time: 0.0659 memory: 6240 grad_norm: 1.1297 loss: 0.7020 detection_loss_cls: 0.7020 2024/07/08 06:43:45 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/08 06:43:45 - mmengine - INFO - Iter(train) [104000/120000] base_lr: 1.0560e-05 lr: 2.7782e-06 eta: 5:25:51 time: 1.2284 data_time: 0.0659 memory: 6240 grad_norm: 1.1301 loss: 0.7016 detection_loss_cls: 0.7016 2024/07/08 06:43:45 - mmengine - INFO - Saving checkpoint at 104000 iterations 2024/07/08 06:44:52 - mmengine - INFO - Iter(train) [104050/120000] base_lr: 1.0507e-05 lr: 2.7734e-06 eta: 5:24:51 time: 1.2229 data_time: 0.0607 memory: 6240 grad_norm: 1.1299 loss: 0.7020 detection_loss_cls: 0.7020 2024/07/08 06:45:52 - mmengine - INFO - Iter(train) [104100/120000] base_lr: 1.0455e-05 lr: 2.7686e-06 eta: 5:23:50 time: 1.2225 data_time: 0.0607 memory: 6240 grad_norm: 1.1295 loss: 0.7025 detection_loss_cls: 0.7025 2024/07/08 06:46:52 - mmengine - INFO - Iter(train) [104150/120000] base_lr: 1.0403e-05 lr: 2.7639e-06 eta: 5:22:49 time: 1.2225 data_time: 0.0607 memory: 6240 grad_norm: 1.1302 loss: 0.7033 detection_loss_cls: 0.7033 2024/07/08 06:47:52 - mmengine - INFO - Iter(train) [104200/120000] base_lr: 1.0350e-05 lr: 2.7591e-06 eta: 5:21:47 time: 1.2223 data_time: 0.0608 memory: 6240 grad_norm: 1.1298 loss: 0.7039 detection_loss_cls: 0.7039 2024/07/08 06:48:53 - mmengine - INFO - Iter(train) [104250/120000] base_lr: 1.0298e-05 lr: 2.7544e-06 eta: 5:20:46 time: 1.2224 data_time: 0.0608 memory: 6240 grad_norm: 1.1293 loss: 0.7036 detection_loss_cls: 0.7036 2024/07/08 06:49:53 - mmengine - INFO - Iter(train) [104300/120000] base_lr: 1.0247e-05 lr: 2.7497e-06 eta: 5:19:45 time: 1.2225 data_time: 0.0609 memory: 6240 grad_norm: 1.1280 loss: 0.7036 detection_loss_cls: 0.7036 2024/07/08 06:50:53 - mmengine - INFO - Iter(train) [104350/120000] base_lr: 1.0195e-05 lr: 2.7450e-06 eta: 5:18:44 time: 1.2222 data_time: 0.0609 memory: 6240 grad_norm: 1.1286 loss: 0.7040 detection_loss_cls: 0.7040 2024/07/08 06:51:53 - mmengine - INFO - Iter(train) [104400/120000] base_lr: 1.0143e-05 lr: 2.7403e-06 eta: 5:17:42 time: 1.2222 data_time: 0.0609 memory: 6240 grad_norm: 1.1282 loss: 0.7051 detection_loss_cls: 0.7051 2024/07/08 06:52:53 - mmengine - INFO - Iter(train) [104450/120000] base_lr: 1.0092e-05 lr: 2.7356e-06 eta: 5:16:41 time: 1.2222 data_time: 0.0609 memory: 6240 grad_norm: 1.1279 loss: 0.7051 detection_loss_cls: 0.7051 2024/07/08 06:53:53 - mmengine - INFO - Iter(train) [104500/120000] base_lr: 1.0041e-05 lr: 2.7310e-06 eta: 5:15:40 time: 1.2220 data_time: 0.0610 memory: 6240 grad_norm: 1.1281 loss: 0.7055 detection_loss_cls: 0.7055 2024/07/08 06:54:54 - mmengine - INFO - Iter(train) [104550/120000] base_lr: 9.9896e-06 lr: 2.7263e-06 eta: 5:14:38 time: 1.2220 data_time: 0.0610 memory: 6240 grad_norm: 1.1280 loss: 0.7058 detection_loss_cls: 0.7058 2024/07/08 06:55:54 - mmengine - INFO - Iter(train) [104600/120000] base_lr: 9.9387e-06 lr: 2.7217e-06 eta: 5:13:37 time: 1.2221 data_time: 0.0610 memory: 6240 grad_norm: 1.1271 loss: 0.7053 detection_loss_cls: 0.7053 2024/07/08 06:56:54 - mmengine - INFO - Iter(train) [104650/120000] base_lr: 9.8879e-06 lr: 2.7171e-06 eta: 5:12:36 time: 1.2219 data_time: 0.0610 memory: 6240 grad_norm: 1.1273 loss: 0.7050 detection_loss_cls: 0.7050 2024/07/08 06:57:54 - mmengine - INFO - Iter(train) [104700/120000] base_lr: 9.8373e-06 lr: 2.7125e-06 eta: 5:11:35 time: 1.2217 data_time: 0.0610 memory: 6240 grad_norm: 1.1280 loss: 0.7044 detection_loss_cls: 0.7044 2024/07/08 06:58:54 - mmengine - INFO - Iter(train) [104750/120000] base_lr: 9.7869e-06 lr: 2.7079e-06 eta: 5:10:33 time: 1.2219 data_time: 0.0609 memory: 6240 grad_norm: 1.1282 loss: 0.7034 detection_loss_cls: 0.7034 2024/07/08 06:59:54 - mmengine - INFO - Iter(train) [104800/120000] base_lr: 9.7366e-06 lr: 2.7033e-06 eta: 5:09:32 time: 1.2217 data_time: 0.0609 memory: 6240 grad_norm: 1.1284 loss: 0.7025 detection_loss_cls: 0.7025 2024/07/08 07:00:54 - mmengine - INFO - Iter(train) [104850/120000] base_lr: 9.6864e-06 lr: 2.6988e-06 eta: 5:08:31 time: 1.2215 data_time: 0.0609 memory: 6240 grad_norm: 1.1287 loss: 0.7022 detection_loss_cls: 0.7022 2024/07/08 07:01:55 - mmengine - INFO - Iter(train) [104900/120000] base_lr: 9.6364e-06 lr: 2.6942e-06 eta: 5:07:30 time: 1.2217 data_time: 0.0609 memory: 6240 grad_norm: 1.1286 loss: 0.7030 detection_loss_cls: 0.7030 2024/07/08 07:02:54 - mmengine - INFO - Iter(train) [104950/120000] base_lr: 9.5866e-06 lr: 2.6897e-06 eta: 5:06:28 time: 1.2213 data_time: 0.0609 memory: 6240 grad_norm: 1.1287 loss: 0.7029 detection_loss_cls: 0.7029 2024/07/08 07:03:54 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/08 07:03:54 - mmengine - INFO - Iter(train) [105000/120000] base_lr: 9.5369e-06 lr: 2.6852e-06 eta: 5:05:27 time: 1.2211 data_time: 0.0609 memory: 6240 grad_norm: 1.1285 loss: 0.7027 detection_loss_cls: 0.7027 2024/07/08 07:03:54 - mmengine - INFO - Saving checkpoint at 105000 iterations 2024/07/08 07:04:43 - mmengine - INFO - Iter(val) [ 50/105] eta: 0:00:44 time: 0.8000 data_time: 0.0290 memory: 6809 2024/07/08 07:05:23 - mmengine - INFO - Iter(val) [100/105] eta: 0:00:04 time: 0.8001 data_time: 0.0290 memory: 6807 2024/07/08 07:05:28 - mmengine - INFO - Evaluating bbox... 2024/07/08 07:05:54 - mmengine - INFO - bbox_mAP_copypaste: 0.421 0.590 0.451 0.205 0.467 0.603 2024/07/08 07:05:55 - mmengine - INFO - Iter(val) [105/105] coco/bbox_mAP: 0.4210 coco/bbox_mAP_50: 0.5900 coco/bbox_mAP_75: 0.4510 coco/bbox_mAP_s: 0.2050 coco/bbox_mAP_m: 0.4670 coco/bbox_mAP_l: 0.6030 data_time: 0.0288 time: 0.7985 2024/07/08 07:06:55 - mmengine - INFO - Iter(train) [105050/120000] base_lr: 9.4874e-06 lr: 2.6807e-06 eta: 5:04:30 time: 1.2262 data_time: 0.0661 memory: 6804 grad_norm: 1.1289 loss: 0.7032 detection_loss_cls: 0.7032 2024/07/08 07:07:56 - mmengine - INFO - Iter(train) [105100/120000] base_lr: 9.4380e-06 lr: 2.6762e-06 eta: 5:03:29 time: 1.2265 data_time: 0.0661 memory: 6241 grad_norm: 1.1290 loss: 0.7033 detection_loss_cls: 0.7033 2024/07/08 07:08:58 - mmengine - INFO - Iter(train) [105150/120000] base_lr: 9.3888e-06 lr: 2.6717e-06 eta: 5:02:28 time: 1.2265 data_time: 0.0662 memory: 6241 grad_norm: 1.1296 loss: 0.7036 detection_loss_cls: 0.7036 2024/07/08 07:09:58 - mmengine - INFO - Iter(train) [105200/120000] base_lr: 9.3398e-06 lr: 2.6673e-06 eta: 5:01:27 time: 1.2265 data_time: 0.0662 memory: 6241 grad_norm: 1.1283 loss: 0.7043 detection_loss_cls: 0.7043 2024/07/08 07:10:59 - mmengine - INFO - Iter(train) [105250/120000] base_lr: 9.2909e-06 lr: 2.6628e-06 eta: 5:00:26 time: 1.2262 data_time: 0.0662 memory: 6241 grad_norm: 1.1280 loss: 0.7045 detection_loss_cls: 0.7045 2024/07/08 07:12:00 - mmengine - INFO - Iter(train) [105300/120000] base_lr: 9.2422e-06 lr: 2.6584e-06 eta: 4:59:25 time: 1.2262 data_time: 0.0662 memory: 6241 grad_norm: 1.1275 loss: 0.7043 detection_loss_cls: 0.7043 2024/07/08 07:13:01 - mmengine - INFO - Iter(train) [105350/120000] base_lr: 9.1936e-06 lr: 2.6540e-06 eta: 4:58:24 time: 1.2263 data_time: 0.0662 memory: 6241 grad_norm: 1.1271 loss: 0.7046 detection_loss_cls: 0.7046 2024/07/08 07:14:01 - mmengine - INFO - Iter(train) [105400/120000] base_lr: 9.1452e-06 lr: 2.6496e-06 eta: 4:57:22 time: 1.2262 data_time: 0.0662 memory: 6241 grad_norm: 1.1270 loss: 0.7040 detection_loss_cls: 0.7040 2024/07/08 07:15:02 - mmengine - INFO - Iter(train) [105450/120000] base_lr: 9.0969e-06 lr: 2.6452e-06 eta: 4:56:21 time: 1.2263 data_time: 0.0662 memory: 6241 grad_norm: 1.1271 loss: 0.7038 detection_loss_cls: 0.7038 2024/07/08 07:16:03 - mmengine - INFO - Iter(train) [105500/120000] base_lr: 9.0488e-06 lr: 2.6408e-06 eta: 4:55:20 time: 1.2264 data_time: 0.0663 memory: 6241 grad_norm: 1.1265 loss: 0.7042 detection_loss_cls: 0.7042 2024/07/08 07:17:04 - mmengine - INFO - Iter(train) [105550/120000] base_lr: 9.0009e-06 lr: 2.6364e-06 eta: 4:54:19 time: 1.2262 data_time: 0.0663 memory: 6241 grad_norm: 1.1263 loss: 0.7038 detection_loss_cls: 0.7038 2024/07/08 07:18:05 - mmengine - INFO - Iter(train) [105600/120000] base_lr: 8.9531e-06 lr: 2.6321e-06 eta: 4:53:18 time: 1.2262 data_time: 0.0664 memory: 6241 grad_norm: 1.1265 loss: 0.7050 detection_loss_cls: 0.7050 2024/07/08 07:19:06 - mmengine - INFO - Iter(train) [105650/120000] base_lr: 8.9055e-06 lr: 2.6278e-06 eta: 4:52:17 time: 1.2264 data_time: 0.0664 memory: 6241 grad_norm: 1.1266 loss: 0.7045 detection_loss_cls: 0.7045 2024/07/08 07:20:07 - mmengine - INFO - Iter(train) [105700/120000] base_lr: 8.8580e-06 lr: 2.6235e-06 eta: 4:51:16 time: 1.2264 data_time: 0.0664 memory: 6241 grad_norm: 1.1268 loss: 0.7050 detection_loss_cls: 0.7050 2024/07/08 07:21:08 - mmengine - INFO - Iter(train) [105750/120000] base_lr: 8.8107e-06 lr: 2.6192e-06 eta: 4:50:14 time: 1.2263 data_time: 0.0664 memory: 6241 grad_norm: 1.1268 loss: 0.7047 detection_loss_cls: 0.7047 2024/07/08 07:22:08 - mmengine - INFO - Iter(train) [105800/120000] base_lr: 8.7635e-06 lr: 2.6149e-06 eta: 4:49:13 time: 1.2261 data_time: 0.0663 memory: 6241 grad_norm: 1.1272 loss: 0.7043 detection_loss_cls: 0.7043 2024/07/08 07:23:09 - mmengine - INFO - Iter(train) [105850/120000] base_lr: 8.7165e-06 lr: 2.6106e-06 eta: 4:48:12 time: 1.2259 data_time: 0.0663 memory: 6241 grad_norm: 1.1269 loss: 0.7050 detection_loss_cls: 0.7050 2024/07/08 07:24:10 - mmengine - INFO - Iter(train) [105900/120000] base_lr: 8.6697e-06 lr: 2.6063e-06 eta: 4:47:11 time: 1.2259 data_time: 0.0664 memory: 6241 grad_norm: 1.1270 loss: 0.7057 detection_loss_cls: 0.7057 2024/07/08 07:25:11 - mmengine - INFO - Iter(train) [105950/120000] base_lr: 8.6230e-06 lr: 2.6021e-06 eta: 4:46:10 time: 1.2261 data_time: 0.0665 memory: 6241 grad_norm: 1.1270 loss: 0.7071 detection_loss_cls: 0.7071 2024/07/08 07:26:11 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/08 07:26:11 - mmengine - INFO - Iter(train) [106000/120000] base_lr: 8.5765e-06 lr: 2.5979e-06 eta: 4:45:09 time: 1.2261 data_time: 0.0665 memory: 6241 grad_norm: 1.1266 loss: 0.7074 detection_loss_cls: 0.7074 2024/07/08 07:26:11 - mmengine - INFO - Saving checkpoint at 106000 iterations 2024/07/08 07:27:20 - mmengine - INFO - Iter(train) [106050/120000] base_lr: 8.5301e-06 lr: 2.5936e-06 eta: 4:44:09 time: 1.2263 data_time: 0.0665 memory: 6241 grad_norm: 1.1267 loss: 0.7079 detection_loss_cls: 0.7079 2024/07/08 07:28:21 - mmengine - INFO - Iter(train) [106100/120000] base_lr: 8.4839e-06 lr: 2.5894e-06 eta: 4:43:08 time: 1.2263 data_time: 0.0665 memory: 6241 grad_norm: 1.1266 loss: 0.7079 detection_loss_cls: 0.7079 2024/07/08 07:29:22 - mmengine - INFO - Iter(train) [106150/120000] base_lr: 8.4378e-06 lr: 2.5853e-06 eta: 4:42:06 time: 1.2262 data_time: 0.0665 memory: 6241 grad_norm: 1.1268 loss: 0.7075 detection_loss_cls: 0.7075 2024/07/08 07:30:23 - mmengine - INFO - Iter(train) [106200/120000] base_lr: 8.3920e-06 lr: 2.5811e-06 eta: 4:41:05 time: 1.2261 data_time: 0.0665 memory: 6241 grad_norm: 1.1263 loss: 0.7067 detection_loss_cls: 0.7067 2024/07/08 07:31:23 - mmengine - INFO - Iter(train) [106250/120000] base_lr: 8.3462e-06 lr: 2.5769e-06 eta: 4:40:04 time: 1.2260 data_time: 0.0665 memory: 6241 grad_norm: 1.1259 loss: 0.7069 detection_loss_cls: 0.7069 2024/07/08 07:32:25 - mmengine - INFO - Iter(train) [106300/120000] base_lr: 8.3007e-06 lr: 2.5728e-06 eta: 4:39:03 time: 1.2261 data_time: 0.0665 memory: 6241 grad_norm: 1.1256 loss: 0.7065 detection_loss_cls: 0.7065 2024/07/08 07:33:26 - mmengine - INFO - Iter(train) [106350/120000] base_lr: 8.2552e-06 lr: 2.5687e-06 eta: 4:38:02 time: 1.2263 data_time: 0.0665 memory: 6241 grad_norm: 1.1259 loss: 0.7061 detection_loss_cls: 0.7061 2024/07/08 07:34:27 - mmengine - INFO - Iter(train) [106400/120000] base_lr: 8.2100e-06 lr: 2.5645e-06 eta: 4:37:01 time: 1.2263 data_time: 0.0665 memory: 6241 grad_norm: 1.1258 loss: 0.7067 detection_loss_cls: 0.7067 2024/07/08 07:35:28 - mmengine - INFO - Iter(train) [106450/120000] base_lr: 8.1649e-06 lr: 2.5604e-06 eta: 4:36:00 time: 1.2263 data_time: 0.0665 memory: 6241 grad_norm: 1.1267 loss: 0.7074 detection_loss_cls: 0.7074 2024/07/08 07:36:29 - mmengine - INFO - Iter(train) [106500/120000] base_lr: 8.1200e-06 lr: 2.5564e-06 eta: 4:34:59 time: 1.2263 data_time: 0.0665 memory: 6241 grad_norm: 1.1262 loss: 0.7068 detection_loss_cls: 0.7068 2024/07/08 07:37:30 - mmengine - INFO - Iter(train) [106550/120000] base_lr: 8.0752e-06 lr: 2.5523e-06 eta: 4:33:57 time: 1.2263 data_time: 0.0665 memory: 6241 grad_norm: 1.1263 loss: 0.7076 detection_loss_cls: 0.7076 2024/07/08 07:38:31 - mmengine - INFO - Iter(train) [106600/120000] base_lr: 8.0306e-06 lr: 2.5482e-06 eta: 4:32:56 time: 1.2265 data_time: 0.0666 memory: 6241 grad_norm: 1.1268 loss: 0.7076 detection_loss_cls: 0.7076 2024/07/08 07:39:32 - mmengine - INFO - Iter(train) [106650/120000] base_lr: 7.9861e-06 lr: 2.5442e-06 eta: 4:31:55 time: 1.2265 data_time: 0.0665 memory: 6241 grad_norm: 1.1266 loss: 0.7075 detection_loss_cls: 0.7075 2024/07/08 07:40:33 - mmengine - INFO - Iter(train) [106700/120000] base_lr: 7.9418e-06 lr: 2.5402e-06 eta: 4:30:54 time: 1.2266 data_time: 0.0665 memory: 6241 grad_norm: 1.1268 loss: 0.7070 detection_loss_cls: 0.7070 2024/07/08 07:41:34 - mmengine - INFO - Iter(train) [106750/120000] base_lr: 7.8977e-06 lr: 2.5362e-06 eta: 4:29:53 time: 1.2264 data_time: 0.0665 memory: 6241 grad_norm: 1.1271 loss: 0.7069 detection_loss_cls: 0.7069 2024/07/08 07:42:35 - mmengine - INFO - Iter(train) [106800/120000] base_lr: 7.8537e-06 lr: 2.5322e-06 eta: 4:28:52 time: 1.2264 data_time: 0.0666 memory: 6241 grad_norm: 1.1270 loss: 0.7068 detection_loss_cls: 0.7068 2024/07/08 07:43:36 - mmengine - INFO - Iter(train) [106850/120000] base_lr: 7.8099e-06 lr: 2.5282e-06 eta: 4:27:51 time: 1.2265 data_time: 0.0666 memory: 6241 grad_norm: 1.1267 loss: 0.7069 detection_loss_cls: 0.7069 2024/07/08 07:44:37 - mmengine - INFO - Iter(train) [106900/120000] base_lr: 7.7662e-06 lr: 2.5242e-06 eta: 4:26:50 time: 1.2265 data_time: 0.0666 memory: 6241 grad_norm: 1.1261 loss: 0.7082 detection_loss_cls: 0.7082 2024/07/08 07:45:38 - mmengine - INFO - Iter(train) [106950/120000] base_lr: 7.7227e-06 lr: 2.5202e-06 eta: 4:25:48 time: 1.2264 data_time: 0.0667 memory: 6241 grad_norm: 1.1264 loss: 0.7083 detection_loss_cls: 0.7083 2024/07/08 07:46:39 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/08 07:46:39 - mmengine - INFO - Iter(train) [107000/120000] base_lr: 7.6794e-06 lr: 2.5163e-06 eta: 4:24:47 time: 1.2266 data_time: 0.0667 memory: 6241 grad_norm: 1.1263 loss: 0.7083 detection_loss_cls: 0.7083 2024/07/08 07:46:39 - mmengine - INFO - Saving checkpoint at 107000 iterations 2024/07/08 07:47:47 - mmengine - INFO - Iter(train) [107050/120000] base_lr: 7.6362e-06 lr: 2.5124e-06 eta: 4:23:47 time: 1.2265 data_time: 0.0667 memory: 6241 grad_norm: 1.1262 loss: 0.7096 detection_loss_cls: 0.7096 2024/07/08 07:48:48 - mmengine - INFO - Iter(train) [107100/120000] base_lr: 7.5932e-06 lr: 2.5085e-06 eta: 4:22:46 time: 1.2263 data_time: 0.0667 memory: 6241 grad_norm: 1.1264 loss: 0.7097 detection_loss_cls: 0.7097 2024/07/08 07:49:49 - mmengine - INFO - Iter(train) [107150/120000] base_lr: 7.5503e-06 lr: 2.5046e-06 eta: 4:21:45 time: 1.2263 data_time: 0.0667 memory: 6241 grad_norm: 1.1261 loss: 0.7107 detection_loss_cls: 0.7107 2024/07/08 07:50:49 - mmengine - INFO - Iter(train) [107200/120000] base_lr: 7.5076e-06 lr: 2.5007e-06 eta: 4:20:44 time: 1.2262 data_time: 0.0668 memory: 6241 grad_norm: 1.1267 loss: 0.7121 detection_loss_cls: 0.7121 2024/07/08 07:51:49 - mmengine - INFO - Iter(train) [107250/120000] base_lr: 7.4651e-06 lr: 2.4968e-06 eta: 4:19:42 time: 1.2260 data_time: 0.0667 memory: 6241 grad_norm: 1.1268 loss: 0.7104 detection_loss_cls: 0.7104 2024/07/08 07:52:50 - mmengine - INFO - Iter(train) [107300/120000] base_lr: 7.4227e-06 lr: 2.4930e-06 eta: 4:18:41 time: 1.2260 data_time: 0.0667 memory: 6241 grad_norm: 1.1267 loss: 0.7101 detection_loss_cls: 0.7101 2024/07/08 07:53:50 - mmengine - INFO - Iter(train) [107350/120000] base_lr: 7.3804e-06 lr: 2.4891e-06 eta: 4:17:40 time: 1.2259 data_time: 0.0667 memory: 6241 grad_norm: 1.1267 loss: 0.7102 detection_loss_cls: 0.7102 2024/07/08 07:54:50 - mmengine - INFO - Iter(train) [107400/120000] base_lr: 7.3384e-06 lr: 2.4853e-06 eta: 4:16:39 time: 1.2256 data_time: 0.0668 memory: 6241 grad_norm: 1.1259 loss: 0.7104 detection_loss_cls: 0.7104 2024/07/08 07:55:50 - mmengine - INFO - Iter(train) [107450/120000] base_lr: 7.2965e-06 lr: 2.4815e-06 eta: 4:15:38 time: 1.2255 data_time: 0.0668 memory: 6241 grad_norm: 1.1256 loss: 0.7109 detection_loss_cls: 0.7109 2024/07/08 07:56:51 - mmengine - INFO - Iter(train) [107500/120000] base_lr: 7.2548e-06 lr: 2.4777e-06 eta: 4:14:36 time: 1.2254 data_time: 0.0668 memory: 6241 grad_norm: 1.1256 loss: 0.7098 detection_loss_cls: 0.7098 2024/07/08 07:57:51 - mmengine - INFO - Iter(train) [107550/120000] base_lr: 7.2132e-06 lr: 2.4739e-06 eta: 4:13:35 time: 1.2251 data_time: 0.0668 memory: 6241 grad_norm: 1.1260 loss: 0.7099 detection_loss_cls: 0.7099 2024/07/08 07:58:52 - mmengine - INFO - Iter(train) [107600/120000] base_lr: 7.1718e-06 lr: 2.4702e-06 eta: 4:12:34 time: 1.2253 data_time: 0.0668 memory: 6241 grad_norm: 1.1256 loss: 0.7104 detection_loss_cls: 0.7104 2024/07/08 07:59:53 - mmengine - INFO - Iter(train) [107650/120000] base_lr: 7.1305e-06 lr: 2.4664e-06 eta: 4:11:33 time: 1.2250 data_time: 0.0668 memory: 6241 grad_norm: 1.1257 loss: 0.7107 detection_loss_cls: 0.7107 2024/07/08 08:00:53 - mmengine - INFO - Iter(train) [107700/120000] base_lr: 7.0894e-06 lr: 2.4627e-06 eta: 4:10:32 time: 1.2249 data_time: 0.0668 memory: 6241 grad_norm: 1.1261 loss: 0.7102 detection_loss_cls: 0.7102 2024/07/08 08:01:54 - mmengine - INFO - Iter(train) [107750/120000] base_lr: 7.0485e-06 lr: 2.4590e-06 eta: 4:09:30 time: 1.2251 data_time: 0.0669 memory: 6241 grad_norm: 1.1261 loss: 0.7114 detection_loss_cls: 0.7114 2024/07/08 08:02:54 - mmengine - INFO - Iter(train) [107800/120000] base_lr: 7.0077e-06 lr: 2.4552e-06 eta: 4:08:29 time: 1.2249 data_time: 0.0669 memory: 6241 grad_norm: 1.1257 loss: 0.7114 detection_loss_cls: 0.7114 2024/07/08 08:03:56 - mmengine - INFO - Iter(train) [107850/120000] base_lr: 6.9671e-06 lr: 2.4516e-06 eta: 4:07:28 time: 1.2249 data_time: 0.0669 memory: 6241 grad_norm: 1.1258 loss: 0.7113 detection_loss_cls: 0.7113 2024/07/08 08:04:56 - mmengine - INFO - Iter(train) [107900/120000] base_lr: 6.9266e-06 lr: 2.4479e-06 eta: 4:06:27 time: 1.2250 data_time: 0.0670 memory: 6241 grad_norm: 1.1259 loss: 0.7129 detection_loss_cls: 0.7129 2024/07/08 08:05:57 - mmengine - INFO - Iter(train) [107950/120000] base_lr: 6.8863e-06 lr: 2.4442e-06 eta: 4:05:26 time: 1.2248 data_time: 0.0670 memory: 6241 grad_norm: 1.1262 loss: 0.7135 detection_loss_cls: 0.7135 2024/07/08 08:06:58 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/08 08:06:58 - mmengine - INFO - Iter(train) [108000/120000] base_lr: 6.8462e-06 lr: 2.4406e-06 eta: 4:04:25 time: 1.2246 data_time: 0.0670 memory: 6241 grad_norm: 1.1262 loss: 0.7146 detection_loss_cls: 0.7146 2024/07/08 08:06:58 - mmengine - INFO - Saving checkpoint at 108000 iterations 2024/07/08 08:08:06 - mmengine - INFO - Iter(train) [108050/120000] base_lr: 6.8062e-06 lr: 2.4369e-06 eta: 4:03:24 time: 1.2249 data_time: 0.0671 memory: 6241 grad_norm: 1.1266 loss: 0.7152 detection_loss_cls: 0.7152 2024/07/08 08:09:06 - mmengine - INFO - Iter(train) [108100/120000] base_lr: 6.7664e-06 lr: 2.4333e-06 eta: 4:02:23 time: 1.2250 data_time: 0.0671 memory: 6241 grad_norm: 1.1272 loss: 0.7154 detection_loss_cls: 0.7154 2024/07/08 08:10:06 - mmengine - INFO - Iter(train) [108150/120000] base_lr: 6.7268e-06 lr: 2.4297e-06 eta: 4:01:22 time: 1.2250 data_time: 0.0671 memory: 6241 grad_norm: 1.1265 loss: 0.7140 detection_loss_cls: 0.7140 2024/07/08 08:11:07 - mmengine - INFO - Iter(train) [108200/120000] base_lr: 6.6873e-06 lr: 2.4261e-06 eta: 4:00:21 time: 1.2251 data_time: 0.0672 memory: 6241 grad_norm: 1.1264 loss: 0.7147 detection_loss_cls: 0.7147 2024/07/08 08:12:07 - mmengine - INFO - Iter(train) [108250/120000] base_lr: 6.6480e-06 lr: 2.4225e-06 eta: 3:59:20 time: 1.2250 data_time: 0.0671 memory: 6241 grad_norm: 1.1277 loss: 0.7142 detection_loss_cls: 0.7142 2024/07/08 08:13:08 - mmengine - INFO - Iter(train) [108300/120000] base_lr: 6.6088e-06 lr: 2.4190e-06 eta: 3:58:18 time: 1.2250 data_time: 0.0671 memory: 6241 grad_norm: 1.1280 loss: 0.7144 detection_loss_cls: 0.7144 2024/07/08 08:14:08 - mmengine - INFO - Iter(train) [108350/120000] base_lr: 6.5698e-06 lr: 2.4154e-06 eta: 3:57:17 time: 1.2252 data_time: 0.0670 memory: 6241 grad_norm: 1.1270 loss: 0.7140 detection_loss_cls: 0.7140 2024/07/08 08:15:08 - mmengine - INFO - Iter(train) [108400/120000] base_lr: 6.5310e-06 lr: 2.4119e-06 eta: 3:56:16 time: 1.2251 data_time: 0.0670 memory: 6241 grad_norm: 1.1272 loss: 0.7136 detection_loss_cls: 0.7136 2024/07/08 08:16:08 - mmengine - INFO - Iter(train) [108450/120000] base_lr: 6.4923e-06 lr: 2.4084e-06 eta: 3:55:15 time: 1.2253 data_time: 0.0671 memory: 6241 grad_norm: 1.1277 loss: 0.7141 detection_loss_cls: 0.7141 2024/07/08 08:17:09 - mmengine - INFO - Iter(train) [108500/120000] base_lr: 6.4538e-06 lr: 2.4049e-06 eta: 3:54:14 time: 1.2256 data_time: 0.0670 memory: 6241 grad_norm: 1.1274 loss: 0.7137 detection_loss_cls: 0.7137 2024/07/08 08:18:10 - mmengine - INFO - Iter(train) [108550/120000] base_lr: 6.4154e-06 lr: 2.4014e-06 eta: 3:53:12 time: 1.2255 data_time: 0.0670 memory: 6241 grad_norm: 1.1278 loss: 0.7143 detection_loss_cls: 0.7143 2024/07/08 08:19:10 - mmengine - INFO - Iter(train) [108600/120000] base_lr: 6.3773e-06 lr: 2.3979e-06 eta: 3:52:11 time: 1.2255 data_time: 0.0671 memory: 6241 grad_norm: 1.1280 loss: 0.7142 detection_loss_cls: 0.7142 2024/07/08 08:20:11 - mmengine - INFO - Iter(train) [108650/120000] base_lr: 6.3392e-06 lr: 2.3945e-06 eta: 3:51:10 time: 1.2257 data_time: 0.0671 memory: 6241 grad_norm: 1.1282 loss: 0.7144 detection_loss_cls: 0.7144 2024/07/08 08:21:11 - mmengine - INFO - Iter(train) [108700/120000] base_lr: 6.3014e-06 lr: 2.3910e-06 eta: 3:50:09 time: 1.2257 data_time: 0.0671 memory: 6241 grad_norm: 1.1274 loss: 0.7151 detection_loss_cls: 0.7151 2024/07/08 08:22:11 - mmengine - INFO - Iter(train) [108750/120000] base_lr: 6.2637e-06 lr: 2.3876e-06 eta: 3:49:08 time: 1.2257 data_time: 0.0672 memory: 6241 grad_norm: 1.1276 loss: 0.7151 detection_loss_cls: 0.7151 2024/07/08 08:23:11 - mmengine - INFO - Iter(train) [108800/120000] base_lr: 6.2261e-06 lr: 2.3842e-06 eta: 3:48:06 time: 1.2258 data_time: 0.0671 memory: 6241 grad_norm: 1.1291 loss: 0.7150 detection_loss_cls: 0.7150 2024/07/08 08:24:11 - mmengine - INFO - Iter(train) [108850/120000] base_lr: 6.1887e-06 lr: 2.3808e-06 eta: 3:47:05 time: 1.2257 data_time: 0.0671 memory: 6241 grad_norm: 1.1291 loss: 0.7149 detection_loss_cls: 0.7149 2024/07/08 08:25:12 - mmengine - INFO - Iter(train) [108900/120000] base_lr: 6.1515e-06 lr: 2.3774e-06 eta: 3:46:04 time: 1.2258 data_time: 0.0671 memory: 6241 grad_norm: 1.1292 loss: 0.7147 detection_loss_cls: 0.7147 2024/07/08 08:26:12 - mmengine - INFO - Iter(train) [108950/120000] base_lr: 6.1145e-06 lr: 2.3740e-06 eta: 3:45:03 time: 1.2260 data_time: 0.0671 memory: 6241 grad_norm: 1.1292 loss: 0.7145 detection_loss_cls: 0.7145 2024/07/08 08:27:12 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/08 08:27:12 - mmengine - INFO - Iter(train) [109000/120000] base_lr: 6.0776e-06 lr: 2.3707e-06 eta: 3:44:02 time: 1.2260 data_time: 0.0671 memory: 6241 grad_norm: 1.1292 loss: 0.7148 detection_loss_cls: 0.7148 2024/07/08 08:27:12 - mmengine - INFO - Saving checkpoint at 109000 iterations 2024/07/08 08:28:21 - mmengine - INFO - Iter(train) [109050/120000] base_lr: 6.0409e-06 lr: 2.3674e-06 eta: 3:43:01 time: 1.2210 data_time: 0.0620 memory: 6241 grad_norm: 1.1289 loss: 0.7153 detection_loss_cls: 0.7153 2024/07/08 08:29:22 - mmengine - INFO - Iter(train) [109100/120000] base_lr: 6.0043e-06 lr: 2.3640e-06 eta: 3:42:00 time: 1.2210 data_time: 0.0620 memory: 6241 grad_norm: 1.1292 loss: 0.7149 detection_loss_cls: 0.7149 2024/07/08 08:30:22 - mmengine - INFO - Iter(train) [109150/120000] base_lr: 5.9679e-06 lr: 2.3607e-06 eta: 3:40:59 time: 1.2207 data_time: 0.0619 memory: 6241 grad_norm: 1.1290 loss: 0.7143 detection_loss_cls: 0.7143 2024/07/08 08:31:23 - mmengine - INFO - Iter(train) [109200/120000] base_lr: 5.9316e-06 lr: 2.3574e-06 eta: 3:39:58 time: 1.2208 data_time: 0.0619 memory: 6241 grad_norm: 1.1297 loss: 0.7141 detection_loss_cls: 0.7141 2024/07/08 08:32:24 - mmengine - INFO - Iter(train) [109250/120000] base_lr: 5.8956e-06 lr: 2.3541e-06 eta: 3:38:57 time: 1.2209 data_time: 0.0619 memory: 6241 grad_norm: 1.1303 loss: 0.7137 detection_loss_cls: 0.7137 2024/07/08 08:33:25 - mmengine - INFO - Iter(train) [109300/120000] base_lr: 5.8597e-06 lr: 2.3509e-06 eta: 3:37:56 time: 1.2208 data_time: 0.0620 memory: 6241 grad_norm: 1.1305 loss: 0.7143 detection_loss_cls: 0.7143 2024/07/08 08:34:26 - mmengine - INFO - Iter(train) [109350/120000] base_lr: 5.8239e-06 lr: 2.3476e-06 eta: 3:36:55 time: 1.2209 data_time: 0.0620 memory: 6241 grad_norm: 1.1310 loss: 0.7142 detection_loss_cls: 0.7142 2024/07/08 08:35:27 - mmengine - INFO - Iter(train) [109400/120000] base_lr: 5.7883e-06 lr: 2.3444e-06 eta: 3:35:53 time: 1.2210 data_time: 0.0619 memory: 6241 grad_norm: 1.1309 loss: 0.7137 detection_loss_cls: 0.7137 2024/07/08 08:36:28 - mmengine - INFO - Iter(train) [109450/120000] base_lr: 5.7529e-06 lr: 2.3412e-06 eta: 3:34:52 time: 1.2209 data_time: 0.0620 memory: 6241 grad_norm: 1.1312 loss: 0.7141 detection_loss_cls: 0.7141 2024/07/08 08:37:29 - mmengine - INFO - Iter(train) [109500/120000] base_lr: 5.7176e-06 lr: 2.3380e-06 eta: 3:33:51 time: 1.2211 data_time: 0.0619 memory: 6241 grad_norm: 1.1310 loss: 0.7125 detection_loss_cls: 0.7125 2024/07/08 08:38:30 - mmengine - INFO - Iter(train) [109550/120000] base_lr: 5.6825e-06 lr: 2.3348e-06 eta: 3:32:50 time: 1.2211 data_time: 0.0619 memory: 6241 grad_norm: 1.1308 loss: 0.7131 detection_loss_cls: 0.7131 2024/07/08 08:39:31 - mmengine - INFO - Iter(train) [109600/120000] base_lr: 5.6476e-06 lr: 2.3316e-06 eta: 3:31:49 time: 1.2211 data_time: 0.0618 memory: 6241 grad_norm: 1.1311 loss: 0.7119 detection_loss_cls: 0.7119 2024/07/08 08:40:33 - mmengine - INFO - Iter(train) [109650/120000] base_lr: 5.6128e-06 lr: 2.3284e-06 eta: 3:30:48 time: 1.2213 data_time: 0.0619 memory: 6241 grad_norm: 1.1318 loss: 0.7133 detection_loss_cls: 0.7133 2024/07/08 08:41:34 - mmengine - INFO - Iter(train) [109700/120000] base_lr: 5.5782e-06 lr: 2.3253e-06 eta: 3:29:47 time: 1.2213 data_time: 0.0619 memory: 6241 grad_norm: 1.1324 loss: 0.7132 detection_loss_cls: 0.7132 2024/07/08 08:42:34 - mmengine - INFO - Iter(train) [109750/120000] base_lr: 5.5438e-06 lr: 2.3222e-06 eta: 3:28:46 time: 1.2212 data_time: 0.0619 memory: 6241 grad_norm: 1.1326 loss: 0.7136 detection_loss_cls: 0.7136 2024/07/08 08:43:35 - mmengine - INFO - Iter(train) [109800/120000] base_lr: 5.5095e-06 lr: 2.3190e-06 eta: 3:27:45 time: 1.2214 data_time: 0.0619 memory: 6241 grad_norm: 1.1325 loss: 0.7135 detection_loss_cls: 0.7135 2024/07/08 08:44:36 - mmengine - INFO - Iter(train) [109850/120000] base_lr: 5.4754e-06 lr: 2.3159e-06 eta: 3:26:43 time: 1.2215 data_time: 0.0619 memory: 6241 grad_norm: 1.1322 loss: 0.7130 detection_loss_cls: 0.7130 2024/07/08 08:45:37 - mmengine - INFO - Iter(train) [109900/120000] base_lr: 5.4414e-06 lr: 2.3129e-06 eta: 3:25:42 time: 1.2213 data_time: 0.0618 memory: 6241 grad_norm: 1.1331 loss: 0.7124 detection_loss_cls: 0.7124 2024/07/08 08:46:38 - mmengine - INFO - Iter(train) [109950/120000] base_lr: 5.4076e-06 lr: 2.3098e-06 eta: 3:24:41 time: 1.2214 data_time: 0.0618 memory: 6241 grad_norm: 1.1334 loss: 0.7114 detection_loss_cls: 0.7114 2024/07/08 08:47:39 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/08 08:47:39 - mmengine - INFO - Iter(train) [110000/120000] base_lr: 5.3740e-06 lr: 2.3067e-06 eta: 3:23:40 time: 1.2213 data_time: 0.0618 memory: 6241 grad_norm: 1.1342 loss: 0.7116 detection_loss_cls: 0.7116 2024/07/08 08:47:39 - mmengine - INFO - Saving checkpoint at 110000 iterations 2024/07/08 08:48:26 - mmengine - INFO - Iter(val) [ 50/105] eta: 0:00:44 time: 0.7999 data_time: 0.0290 memory: 6807 2024/07/08 08:49:07 - mmengine - INFO - Iter(val) [100/105] eta: 0:00:04 time: 0.7999 data_time: 0.0290 memory: 6807 2024/07/08 08:49:12 - mmengine - INFO - Evaluating bbox... 2024/07/08 08:49:38 - mmengine - INFO - bbox_mAP_copypaste: 0.421 0.590 0.451 0.205 0.466 0.604 2024/07/08 08:49:38 - mmengine - INFO - Iter(val) [105/105] coco/bbox_mAP: 0.4210 coco/bbox_mAP_50: 0.5900 coco/bbox_mAP_75: 0.4510 coco/bbox_mAP_s: 0.2050 coco/bbox_mAP_m: 0.4660 coco/bbox_mAP_l: 0.6040 data_time: 0.0279 time: 0.7930 2024/07/08 08:50:39 - mmengine - INFO - Iter(train) [110050/120000] base_lr: 5.3406e-06 lr: 2.3037e-06 eta: 3:22:42 time: 1.2263 data_time: 0.0669 memory: 6804 grad_norm: 1.1338 loss: 0.7107 detection_loss_cls: 0.7107 2024/07/08 08:51:40 - mmengine - INFO - Iter(train) [110100/120000] base_lr: 5.3073e-06 lr: 2.3007e-06 eta: 3:21:41 time: 1.2263 data_time: 0.0669 memory: 6245 grad_norm: 1.1343 loss: 0.7112 detection_loss_cls: 0.7112 2024/07/08 08:52:40 - mmengine - INFO - Iter(train) [110150/120000] base_lr: 5.2741e-06 lr: 2.2976e-06 eta: 3:20:40 time: 1.2263 data_time: 0.0669 memory: 6245 grad_norm: 1.1342 loss: 0.7108 detection_loss_cls: 0.7108 2024/07/08 08:53:41 - mmengine - INFO - Iter(train) [110200/120000] base_lr: 5.2412e-06 lr: 2.2947e-06 eta: 3:19:38 time: 1.2262 data_time: 0.0670 memory: 6245 grad_norm: 1.1348 loss: 0.7110 detection_loss_cls: 0.7110 2024/07/08 08:54:42 - mmengine - INFO - Iter(train) [110250/120000] base_lr: 5.2083e-06 lr: 2.2917e-06 eta: 3:18:37 time: 1.2262 data_time: 0.0669 memory: 6245 grad_norm: 1.1351 loss: 0.7109 detection_loss_cls: 0.7109 2024/07/08 08:55:43 - mmengine - INFO - Iter(train) [110300/120000] base_lr: 5.1757e-06 lr: 2.2887e-06 eta: 3:17:36 time: 1.2263 data_time: 0.0669 memory: 6245 grad_norm: 1.1355 loss: 0.7115 detection_loss_cls: 0.7115 2024/07/08 08:56:43 - mmengine - INFO - Iter(train) [110350/120000] base_lr: 5.1432e-06 lr: 2.2857e-06 eta: 3:16:35 time: 1.2260 data_time: 0.0669 memory: 6245 grad_norm: 1.1353 loss: 0.7107 detection_loss_cls: 0.7107 2024/07/08 08:57:45 - mmengine - INFO - Iter(train) [110400/120000] base_lr: 5.1109e-06 lr: 2.2828e-06 eta: 3:15:34 time: 1.2261 data_time: 0.0669 memory: 6245 grad_norm: 1.1353 loss: 0.7103 detection_loss_cls: 0.7103 2024/07/08 08:58:46 - mmengine - INFO - Iter(train) [110450/120000] base_lr: 5.0788e-06 lr: 2.2799e-06 eta: 3:14:33 time: 1.2261 data_time: 0.0669 memory: 6245 grad_norm: 1.1347 loss: 0.7106 detection_loss_cls: 0.7106 2024/07/08 08:59:46 - mmengine - INFO - Iter(train) [110500/120000] base_lr: 5.0468e-06 lr: 2.2770e-06 eta: 3:13:32 time: 1.2261 data_time: 0.0669 memory: 6245 grad_norm: 1.1349 loss: 0.7105 detection_loss_cls: 0.7105 2024/07/08 09:00:48 - mmengine - INFO - Iter(train) [110550/120000] base_lr: 5.0150e-06 lr: 2.2741e-06 eta: 3:12:30 time: 1.2263 data_time: 0.0670 memory: 6245 grad_norm: 1.1351 loss: 0.7105 detection_loss_cls: 0.7105 2024/07/08 09:01:49 - mmengine - INFO - Iter(train) [110600/120000] base_lr: 4.9833e-06 lr: 2.2712e-06 eta: 3:11:29 time: 1.2262 data_time: 0.0669 memory: 6245 grad_norm: 1.1350 loss: 0.7097 detection_loss_cls: 0.7097 2024/07/08 09:02:50 - mmengine - INFO - Iter(train) [110650/120000] base_lr: 4.9518e-06 lr: 2.2683e-06 eta: 3:10:28 time: 1.2261 data_time: 0.0669 memory: 6245 grad_norm: 1.1355 loss: 0.7100 detection_loss_cls: 0.7100 2024/07/08 09:03:51 - mmengine - INFO - Iter(train) [110700/120000] base_lr: 4.9205e-06 lr: 2.2655e-06 eta: 3:09:27 time: 1.2262 data_time: 0.0670 memory: 6245 grad_norm: 1.1357 loss: 0.7101 detection_loss_cls: 0.7101 2024/07/08 09:04:52 - mmengine - INFO - Iter(train) [110750/120000] base_lr: 4.8893e-06 lr: 2.2627e-06 eta: 3:08:26 time: 1.2262 data_time: 0.0670 memory: 6245 grad_norm: 1.1355 loss: 0.7101 detection_loss_cls: 0.7101 2024/07/08 09:05:52 - mmengine - INFO - Iter(train) [110800/120000] base_lr: 4.8583e-06 lr: 2.2598e-06 eta: 3:07:25 time: 1.2260 data_time: 0.0670 memory: 6245 grad_norm: 1.1351 loss: 0.7105 detection_loss_cls: 0.7105 2024/07/08 09:06:53 - mmengine - INFO - Iter(train) [110850/120000] base_lr: 4.8275e-06 lr: 2.2570e-06 eta: 3:06:24 time: 1.2261 data_time: 0.0669 memory: 6245 grad_norm: 1.1354 loss: 0.7100 detection_loss_cls: 0.7100 2024/07/08 09:07:54 - mmengine - INFO - Iter(train) [110900/120000] base_lr: 4.7968e-06 lr: 2.2543e-06 eta: 3:05:22 time: 1.2261 data_time: 0.0669 memory: 6245 grad_norm: 1.1353 loss: 0.7098 detection_loss_cls: 0.7098 2024/07/08 09:08:55 - mmengine - INFO - Iter(train) [110950/120000] base_lr: 4.7663e-06 lr: 2.2515e-06 eta: 3:04:21 time: 1.2260 data_time: 0.0669 memory: 6245 grad_norm: 1.1354 loss: 0.7101 detection_loss_cls: 0.7101 2024/07/08 09:09:56 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/08 09:09:56 - mmengine - INFO - Iter(train) [111000/120000] base_lr: 4.7360e-06 lr: 2.2487e-06 eta: 3:03:20 time: 1.2260 data_time: 0.0669 memory: 6245 grad_norm: 1.1356 loss: 0.7096 detection_loss_cls: 0.7096 2024/07/08 09:09:56 - mmengine - INFO - Saving checkpoint at 111000 iterations 2024/07/08 09:11:05 - mmengine - INFO - Iter(train) [111050/120000] base_lr: 4.7058e-06 lr: 2.2460e-06 eta: 3:02:20 time: 1.2261 data_time: 0.0670 memory: 6245 grad_norm: 1.1362 loss: 0.7091 detection_loss_cls: 0.7091 2024/07/08 09:12:05 - mmengine - INFO - Iter(train) [111100/120000] base_lr: 4.6758e-06 lr: 2.2433e-06 eta: 3:01:19 time: 1.2260 data_time: 0.0669 memory: 6245 grad_norm: 1.1363 loss: 0.7081 detection_loss_cls: 0.7081 2024/07/08 09:13:06 - mmengine - INFO - Iter(train) [111150/120000] base_lr: 4.6460e-06 lr: 2.2405e-06 eta: 3:00:17 time: 1.2260 data_time: 0.0669 memory: 6245 grad_norm: 1.1367 loss: 0.7074 detection_loss_cls: 0.7074 2024/07/08 09:14:07 - mmengine - INFO - Iter(train) [111200/120000] base_lr: 4.6163e-06 lr: 2.2378e-06 eta: 2:59:16 time: 1.2261 data_time: 0.0668 memory: 6245 grad_norm: 1.1367 loss: 0.7060 detection_loss_cls: 0.7060 2024/07/08 09:15:07 - mmengine - INFO - Iter(train) [111250/120000] base_lr: 4.5868e-06 lr: 2.2352e-06 eta: 2:58:15 time: 1.2262 data_time: 0.0669 memory: 6245 grad_norm: 1.1364 loss: 0.7065 detection_loss_cls: 0.7065 2024/07/08 09:16:09 - mmengine - INFO - Iter(train) [111300/120000] base_lr: 4.5574e-06 lr: 2.2325e-06 eta: 2:57:14 time: 1.2265 data_time: 0.0669 memory: 6245 grad_norm: 1.1362 loss: 0.7067 detection_loss_cls: 0.7067 2024/07/08 09:17:09 - mmengine - INFO - Iter(train) [111350/120000] base_lr: 4.5282e-06 lr: 2.2298e-06 eta: 2:56:13 time: 1.2266 data_time: 0.0669 memory: 6245 grad_norm: 1.1366 loss: 0.7069 detection_loss_cls: 0.7069 2024/07/08 09:18:10 - mmengine - INFO - Iter(train) [111400/120000] base_lr: 4.4992e-06 lr: 2.2272e-06 eta: 2:55:12 time: 1.2266 data_time: 0.0669 memory: 6245 grad_norm: 1.1368 loss: 0.7075 detection_loss_cls: 0.7075 2024/07/08 09:19:11 - mmengine - INFO - Iter(train) [111450/120000] base_lr: 4.4704e-06 lr: 2.2246e-06 eta: 2:54:11 time: 1.2268 data_time: 0.0668 memory: 6245 grad_norm: 1.1368 loss: 0.7066 detection_loss_cls: 0.7066 2024/07/08 09:20:11 - mmengine - INFO - Iter(train) [111500/120000] base_lr: 4.4417e-06 lr: 2.2220e-06 eta: 2:53:09 time: 1.2269 data_time: 0.0667 memory: 6245 grad_norm: 1.1364 loss: 0.7061 detection_loss_cls: 0.7061 2024/07/08 09:21:12 - mmengine - INFO - Iter(train) [111550/120000] base_lr: 4.4132e-06 lr: 2.2194e-06 eta: 2:52:08 time: 1.2269 data_time: 0.0667 memory: 6245 grad_norm: 1.1367 loss: 0.7052 detection_loss_cls: 0.7052 2024/07/08 09:22:13 - mmengine - INFO - Iter(train) [111600/120000] base_lr: 4.3848e-06 lr: 2.2168e-06 eta: 2:51:07 time: 1.2270 data_time: 0.0667 memory: 6245 grad_norm: 1.1373 loss: 0.7047 detection_loss_cls: 0.7047 2024/07/08 09:23:14 - mmengine - INFO - Iter(train) [111650/120000] base_lr: 4.3566e-06 lr: 2.2142e-06 eta: 2:50:06 time: 1.2269 data_time: 0.0666 memory: 6245 grad_norm: 1.1369 loss: 0.7037 detection_loss_cls: 0.7037 2024/07/08 09:24:13 - mmengine - INFO - Iter(train) [111700/120000] base_lr: 4.3286e-06 lr: 2.2117e-06 eta: 2:49:05 time: 1.2268 data_time: 0.0666 memory: 6245 grad_norm: 1.1368 loss: 0.7039 detection_loss_cls: 0.7039 2024/07/08 09:25:15 - mmengine - INFO - Iter(train) [111750/120000] base_lr: 4.3007e-06 lr: 2.2092e-06 eta: 2:48:04 time: 1.2268 data_time: 0.0666 memory: 6245 grad_norm: 1.1370 loss: 0.7040 detection_loss_cls: 0.7040 2024/07/08 09:26:15 - mmengine - INFO - Iter(train) [111800/120000] base_lr: 4.2730e-06 lr: 2.2066e-06 eta: 2:47:02 time: 1.2269 data_time: 0.0666 memory: 6245 grad_norm: 1.1372 loss: 0.7036 detection_loss_cls: 0.7036 2024/07/08 09:27:15 - mmengine - INFO - Iter(train) [111850/120000] base_lr: 4.2455e-06 lr: 2.2041e-06 eta: 2:46:01 time: 1.2267 data_time: 0.0666 memory: 6245 grad_norm: 1.1371 loss: 0.7035 detection_loss_cls: 0.7035 2024/07/08 09:28:17 - mmengine - INFO - Iter(train) [111900/120000] base_lr: 4.2182e-06 lr: 2.2017e-06 eta: 2:45:00 time: 1.2268 data_time: 0.0665 memory: 6245 grad_norm: 1.1370 loss: 0.7026 detection_loss_cls: 0.7026 2024/07/08 09:29:18 - mmengine - INFO - Iter(train) [111950/120000] base_lr: 4.1910e-06 lr: 2.1992e-06 eta: 2:43:59 time: 1.2269 data_time: 0.0665 memory: 6245 grad_norm: 1.1370 loss: 0.7025 detection_loss_cls: 0.7025 2024/07/08 09:30:18 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/08 09:30:18 - mmengine - INFO - Iter(train) [112000/120000] base_lr: 4.1639e-06 lr: 2.1967e-06 eta: 2:42:58 time: 1.2267 data_time: 0.0665 memory: 6245 grad_norm: 1.1372 loss: 0.7021 detection_loss_cls: 0.7021 2024/07/08 09:30:18 - mmengine - INFO - Saving checkpoint at 112000 iterations 2024/07/08 09:31:27 - mmengine - INFO - Iter(train) [112050/120000] base_lr: 4.1371e-06 lr: 2.1943e-06 eta: 2:41:57 time: 1.2271 data_time: 0.0665 memory: 6245 grad_norm: 1.1366 loss: 0.7023 detection_loss_cls: 0.7023 2024/07/08 09:32:29 - mmengine - INFO - Iter(train) [112100/120000] base_lr: 4.1104e-06 lr: 2.1919e-06 eta: 2:40:56 time: 1.2275 data_time: 0.0665 memory: 6245 grad_norm: 1.1364 loss: 0.7021 detection_loss_cls: 0.7021 2024/07/08 09:33:30 - mmengine - INFO - Iter(train) [112150/120000] base_lr: 4.0838e-06 lr: 2.1894e-06 eta: 2:39:55 time: 1.2277 data_time: 0.0665 memory: 6245 grad_norm: 1.1365 loss: 0.7023 detection_loss_cls: 0.7023 2024/07/08 09:34:32 - mmengine - INFO - Iter(train) [112200/120000] base_lr: 4.0575e-06 lr: 2.1870e-06 eta: 2:38:54 time: 1.2280 data_time: 0.0664 memory: 6245 grad_norm: 1.1364 loss: 0.7022 detection_loss_cls: 0.7022 2024/07/08 09:35:33 - mmengine - INFO - Iter(train) [112250/120000] base_lr: 4.0313e-06 lr: 2.1847e-06 eta: 2:37:53 time: 1.2282 data_time: 0.0664 memory: 6245 grad_norm: 1.1353 loss: 0.7023 detection_loss_cls: 0.7023 2024/07/08 09:36:35 - 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mmengine - INFO - Iter(train) [112550/120000] base_lr: 3.8776e-06 lr: 2.1707e-06 eta: 2:31:47 time: 1.2300 data_time: 0.0664 memory: 6245 grad_norm: 1.1363 loss: 0.7010 detection_loss_cls: 0.7010 2024/07/08 09:42:44 - mmengine - INFO - Iter(train) [112600/120000] base_lr: 3.8525e-06 lr: 2.1684e-06 eta: 2:30:45 time: 1.2300 data_time: 0.0665 memory: 6245 grad_norm: 1.1363 loss: 0.7020 detection_loss_cls: 0.7020 2024/07/08 09:43:46 - mmengine - INFO - Iter(train) [112650/120000] base_lr: 3.8277e-06 lr: 2.1662e-06 eta: 2:29:44 time: 1.2304 data_time: 0.0665 memory: 6245 grad_norm: 1.1362 loss: 0.7016 detection_loss_cls: 0.7016 2024/07/08 09:44:47 - mmengine - INFO - Iter(train) [112700/120000] base_lr: 3.8030e-06 lr: 2.1639e-06 eta: 2:28:43 time: 1.2308 data_time: 0.0665 memory: 6245 grad_norm: 1.1365 loss: 0.7009 detection_loss_cls: 0.7009 2024/07/08 09:45:49 - mmengine - INFO - Iter(train) [112750/120000] base_lr: 3.7784e-06 lr: 2.1617e-06 eta: 2:27:42 time: 1.2312 data_time: 0.0665 memory: 6245 grad_norm: 1.1359 loss: 0.7020 detection_loss_cls: 0.7020 2024/07/08 09:46:51 - mmengine - INFO - Iter(train) [112800/120000] base_lr: 3.7540e-06 lr: 2.1595e-06 eta: 2:26:41 time: 1.2316 data_time: 0.0665 memory: 6245 grad_norm: 1.1344 loss: 0.7021 detection_loss_cls: 0.7021 2024/07/08 09:47:52 - mmengine - INFO - Iter(train) [112850/120000] base_lr: 3.7298e-06 lr: 2.1573e-06 eta: 2:25:40 time: 1.2319 data_time: 0.0666 memory: 6245 grad_norm: 1.1344 loss: 0.7024 detection_loss_cls: 0.7024 2024/07/08 09:48:53 - mmengine - INFO - Iter(train) [112900/120000] base_lr: 3.7058e-06 lr: 2.1551e-06 eta: 2:24:39 time: 1.2320 data_time: 0.0665 memory: 6245 grad_norm: 1.1338 loss: 0.7019 detection_loss_cls: 0.7019 2024/07/08 09:49:55 - mmengine - INFO - Iter(train) [112950/120000] base_lr: 3.6819e-06 lr: 2.1529e-06 eta: 2:23:38 time: 1.2325 data_time: 0.0666 memory: 6245 grad_norm: 1.1340 loss: 0.7034 detection_loss_cls: 0.7034 2024/07/08 09:50:56 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/08 09:50:56 - mmengine - INFO - Iter(train) [113000/120000] base_lr: 3.6582e-06 lr: 2.1507e-06 eta: 2:22:37 time: 1.2328 data_time: 0.0666 memory: 6245 grad_norm: 1.1342 loss: 0.7038 detection_loss_cls: 0.7038 2024/07/08 09:50:56 - mmengine - INFO - Saving checkpoint at 113000 iterations 2024/07/08 09:52:06 - mmengine - INFO - Iter(train) [113050/120000] base_lr: 3.6347e-06 lr: 2.1486e-06 eta: 2:21:36 time: 1.2329 data_time: 0.0667 memory: 6245 grad_norm: 1.1361 loss: 0.7032 detection_loss_cls: 0.7032 2024/07/08 09:53:08 - mmengine - INFO - Iter(train) [113100/120000] base_lr: 3.6113e-06 lr: 2.1465e-06 eta: 2:20:35 time: 1.2332 data_time: 0.0667 memory: 6245 grad_norm: 1.1360 loss: 0.7037 detection_loss_cls: 0.7037 2024/07/08 09:54:09 - mmengine - INFO - Iter(train) [113150/120000] base_lr: 3.5881e-06 lr: 2.1444e-06 eta: 2:19:34 time: 1.2333 data_time: 0.0667 memory: 6245 grad_norm: 1.1356 loss: 0.7040 detection_loss_cls: 0.7040 2024/07/08 09:55:10 - mmengine - INFO - Iter(train) [113200/120000] base_lr: 3.5651e-06 lr: 2.1423e-06 eta: 2:18:33 time: 1.2333 data_time: 0.0667 memory: 6245 grad_norm: 1.1349 loss: 0.7045 detection_loss_cls: 0.7045 2024/07/08 09:56:11 - mmengine - INFO - Iter(train) [113250/120000] base_lr: 3.5422e-06 lr: 2.1402e-06 eta: 2:17:32 time: 1.2334 data_time: 0.0667 memory: 6245 grad_norm: 1.1350 loss: 0.7041 detection_loss_cls: 0.7041 2024/07/08 09:57:13 - mmengine - INFO - Iter(train) [113300/120000] base_lr: 3.5195e-06 lr: 2.1381e-06 eta: 2:16:31 time: 1.2336 data_time: 0.0666 memory: 6245 grad_norm: 1.1349 loss: 0.7036 detection_loss_cls: 0.7036 2024/07/08 09:58:14 - mmengine - INFO - Iter(train) [113350/120000] base_lr: 3.4970e-06 lr: 2.1361e-06 eta: 2:15:29 time: 1.2335 data_time: 0.0666 memory: 6245 grad_norm: 1.1344 loss: 0.7030 detection_loss_cls: 0.7030 2024/07/08 09:59:15 - mmengine - INFO - Iter(train) [113400/120000] base_lr: 3.4746e-06 lr: 2.1341e-06 eta: 2:14:28 time: 1.2337 data_time: 0.0667 memory: 6245 grad_norm: 1.1344 loss: 0.7039 detection_loss_cls: 0.7039 2024/07/08 10:00:17 - mmengine - INFO - Iter(train) [113450/120000] base_lr: 3.4524e-06 lr: 2.1320e-06 eta: 2:13:27 time: 1.2339 data_time: 0.0667 memory: 6245 grad_norm: 1.1343 loss: 0.7040 detection_loss_cls: 0.7040 2024/07/08 10:01:18 - mmengine - INFO - Iter(train) [113500/120000] base_lr: 3.4304e-06 lr: 2.1300e-06 eta: 2:12:26 time: 1.2339 data_time: 0.0667 memory: 6245 grad_norm: 1.1352 loss: 0.7050 detection_loss_cls: 0.7050 2024/07/08 10:02:20 - mmengine - INFO - Iter(train) [113550/120000] base_lr: 3.4085e-06 lr: 2.1280e-06 eta: 2:11:25 time: 1.2341 data_time: 0.0668 memory: 6245 grad_norm: 1.1364 loss: 0.7059 detection_loss_cls: 0.7059 2024/07/08 10:03:21 - mmengine - INFO - Iter(train) [113600/120000] base_lr: 3.3868e-06 lr: 2.1261e-06 eta: 2:10:24 time: 1.2343 data_time: 0.0668 memory: 6245 grad_norm: 1.1359 loss: 0.7065 detection_loss_cls: 0.7065 2024/07/08 10:04:22 - mmengine - INFO - Iter(train) [113650/120000] base_lr: 3.3653e-06 lr: 2.1241e-06 eta: 2:09:23 time: 1.2341 data_time: 0.0668 memory: 6245 grad_norm: 1.1355 loss: 0.7055 detection_loss_cls: 0.7055 2024/07/08 10:05:25 - mmengine - INFO - Iter(train) [113700/120000] base_lr: 3.3439e-06 lr: 2.1222e-06 eta: 2:08:22 time: 1.2345 data_time: 0.0668 memory: 6245 grad_norm: 1.1343 loss: 0.7053 detection_loss_cls: 0.7053 2024/07/08 10:06:26 - mmengine - INFO - Iter(train) [113750/120000] base_lr: 3.3227e-06 lr: 2.1202e-06 eta: 2:07:21 time: 1.2346 data_time: 0.0668 memory: 6245 grad_norm: 1.1346 loss: 0.7054 detection_loss_cls: 0.7054 2024/07/08 10:07:27 - mmengine - INFO - Iter(train) [113800/120000] base_lr: 3.3017e-06 lr: 2.1183e-06 eta: 2:06:20 time: 1.2347 data_time: 0.0668 memory: 6245 grad_norm: 1.1345 loss: 0.7055 detection_loss_cls: 0.7055 2024/07/08 10:08:29 - mmengine - INFO - Iter(train) [113850/120000] base_lr: 3.2808e-06 lr: 2.1164e-06 eta: 2:05:18 time: 1.2350 data_time: 0.0667 memory: 6245 grad_norm: 1.1344 loss: 0.7049 detection_loss_cls: 0.7049 2024/07/08 10:09:30 - mmengine - INFO - Iter(train) [113900/120000] base_lr: 3.2601e-06 lr: 2.1146e-06 eta: 2:04:17 time: 1.2351 data_time: 0.0668 memory: 6245 grad_norm: 1.1338 loss: 0.7049 detection_loss_cls: 0.7049 2024/07/08 10:10:32 - mmengine - INFO - Iter(train) [113950/120000] base_lr: 3.2396e-06 lr: 2.1127e-06 eta: 2:03:16 time: 1.2352 data_time: 0.0668 memory: 6245 grad_norm: 1.1336 loss: 0.7054 detection_loss_cls: 0.7054 2024/07/08 10:11:34 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/08 10:11:34 - mmengine - INFO - Iter(train) [114000/120000] base_lr: 3.2193e-06 lr: 2.1108e-06 eta: 2:02:15 time: 1.2355 data_time: 0.0668 memory: 6245 grad_norm: 1.1334 loss: 0.7055 detection_loss_cls: 0.7055 2024/07/08 10:11:34 - mmengine - INFO - Saving checkpoint at 114000 iterations 2024/07/08 10:12:42 - mmengine - INFO - Iter(train) [114050/120000] base_lr: 3.1991e-06 lr: 2.1090e-06 eta: 2:01:14 time: 1.2305 data_time: 0.0617 memory: 6245 grad_norm: 1.1337 loss: 0.7058 detection_loss_cls: 0.7058 2024/07/08 10:13:43 - mmengine - INFO - Iter(train) [114100/120000] base_lr: 3.1790e-06 lr: 2.1072e-06 eta: 2:00:13 time: 1.2305 data_time: 0.0617 memory: 6245 grad_norm: 1.1333 loss: 0.7048 detection_loss_cls: 0.7048 2024/07/08 10:14:45 - mmengine - INFO - Iter(train) [114150/120000] base_lr: 3.1592e-06 lr: 2.1054e-06 eta: 1:59:12 time: 1.2307 data_time: 0.0618 memory: 6245 grad_norm: 1.1333 loss: 0.7055 detection_loss_cls: 0.7055 2024/07/08 10:15:45 - mmengine - INFO - Iter(train) [114200/120000] base_lr: 3.1395e-06 lr: 2.1036e-06 eta: 1:58:11 time: 1.2307 data_time: 0.0618 memory: 6245 grad_norm: 1.1329 loss: 0.7059 detection_loss_cls: 0.7059 2024/07/08 10:16:46 - mmengine - INFO - Iter(train) [114250/120000] base_lr: 3.1200e-06 lr: 2.1018e-06 eta: 1:57:10 time: 1.2306 data_time: 0.0618 memory: 6245 grad_norm: 1.1328 loss: 0.7055 detection_loss_cls: 0.7055 2024/07/08 10:17:47 - mmengine - INFO - Iter(train) [114300/120000] base_lr: 3.1006e-06 lr: 2.1001e-06 eta: 1:56:09 time: 1.2305 data_time: 0.0617 memory: 6245 grad_norm: 1.1328 loss: 0.7049 detection_loss_cls: 0.7049 2024/07/08 10:18:48 - mmengine - INFO - Iter(train) [114350/120000] base_lr: 3.0814e-06 lr: 2.0983e-06 eta: 1:55:08 time: 1.2306 data_time: 0.0617 memory: 6245 grad_norm: 1.1334 loss: 0.7059 detection_loss_cls: 0.7059 2024/07/08 10:19:48 - mmengine - INFO - Iter(train) [114400/120000] base_lr: 3.0624e-06 lr: 2.0966e-06 eta: 1:54:06 time: 1.2304 data_time: 0.0617 memory: 6245 grad_norm: 1.1335 loss: 0.7064 detection_loss_cls: 0.7064 2024/07/08 10:20:49 - mmengine - INFO - Iter(train) [114450/120000] base_lr: 3.0436e-06 lr: 2.0949e-06 eta: 1:53:05 time: 1.2305 data_time: 0.0617 memory: 6245 grad_norm: 1.1335 loss: 0.7060 detection_loss_cls: 0.7060 2024/07/08 10:21:50 - mmengine - INFO - Iter(train) [114500/120000] base_lr: 3.0249e-06 lr: 2.0932e-06 eta: 1:52:04 time: 1.2304 data_time: 0.0617 memory: 6245 grad_norm: 1.1338 loss: 0.7062 detection_loss_cls: 0.7062 2024/07/08 10:22:50 - mmengine - INFO - Iter(train) [114550/120000] base_lr: 3.0064e-06 lr: 2.0915e-06 eta: 1:51:03 time: 1.2300 data_time: 0.0617 memory: 6245 grad_norm: 1.1340 loss: 0.7056 detection_loss_cls: 0.7056 2024/07/08 10:23:51 - mmengine - INFO - Iter(train) [114600/120000] base_lr: 2.9880e-06 lr: 2.0898e-06 eta: 1:50:02 time: 1.2300 data_time: 0.0617 memory: 6245 grad_norm: 1.1346 loss: 0.7054 detection_loss_cls: 0.7054 2024/07/08 10:24:52 - mmengine - INFO - Iter(train) [114650/120000] base_lr: 2.9698e-06 lr: 2.0882e-06 eta: 1:49:01 time: 1.2301 data_time: 0.0616 memory: 6245 grad_norm: 1.1341 loss: 0.7041 detection_loss_cls: 0.7041 2024/07/08 10:25:52 - mmengine - INFO - Iter(train) [114700/120000] base_lr: 2.9518e-06 lr: 2.0865e-06 eta: 1:47:59 time: 1.2298 data_time: 0.0617 memory: 6245 grad_norm: 1.1341 loss: 0.7047 detection_loss_cls: 0.7047 2024/07/08 10:26:53 - mmengine - INFO - Iter(train) [114750/120000] base_lr: 2.9340e-06 lr: 2.0849e-06 eta: 1:46:58 time: 1.2299 data_time: 0.0616 memory: 6245 grad_norm: 1.1339 loss: 0.7046 detection_loss_cls: 0.7046 2024/07/08 10:27:53 - mmengine - INFO - Iter(train) [114800/120000] base_lr: 2.9163e-06 lr: 2.0833e-06 eta: 1:45:57 time: 1.2299 data_time: 0.0616 memory: 6245 grad_norm: 1.1343 loss: 0.7043 detection_loss_cls: 0.7043 2024/07/08 10:28:54 - mmengine - INFO - Iter(train) [114850/120000] base_lr: 2.8988e-06 lr: 2.0817e-06 eta: 1:44:56 time: 1.2297 data_time: 0.0616 memory: 6245 grad_norm: 1.1345 loss: 0.7044 detection_loss_cls: 0.7044 2024/07/08 10:29:55 - mmengine - INFO - Iter(train) [114900/120000] base_lr: 2.8815e-06 lr: 2.0801e-06 eta: 1:43:55 time: 1.2297 data_time: 0.0616 memory: 6245 grad_norm: 1.1347 loss: 0.7041 detection_loss_cls: 0.7041 2024/07/08 10:30:56 - mmengine - INFO - Iter(train) [114950/120000] base_lr: 2.8643e-06 lr: 2.0786e-06 eta: 1:42:54 time: 1.2298 data_time: 0.0616 memory: 6245 grad_norm: 1.1345 loss: 0.7039 detection_loss_cls: 0.7039 2024/07/08 10:31:56 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/08 10:31:56 - mmengine - INFO - Iter(train) [115000/120000] base_lr: 2.8473e-06 lr: 2.0770e-06 eta: 1:41:52 time: 1.2296 data_time: 0.0616 memory: 6245 grad_norm: 1.1343 loss: 0.7047 detection_loss_cls: 0.7047 2024/07/08 10:31:56 - mmengine - INFO - Saving checkpoint at 115000 iterations 2024/07/08 10:32:44 - mmengine - INFO - Iter(val) [ 50/105] eta: 0:00:43 time: 0.7997 data_time: 0.0290 memory: 6805 2024/07/08 10:33:24 - mmengine - INFO - Iter(val) [100/105] eta: 0:00:03 time: 0.7996 data_time: 0.0289 memory: 6808 2024/07/08 10:33:30 - mmengine - INFO - Evaluating bbox... 2024/07/08 10:33:56 - mmengine - INFO - bbox_mAP_copypaste: 0.421 0.589 0.453 0.204 0.467 0.605 2024/07/08 10:33:56 - mmengine - INFO - Iter(val) [105/105] coco/bbox_mAP: 0.4210 coco/bbox_mAP_50: 0.5890 coco/bbox_mAP_75: 0.4530 coco/bbox_mAP_s: 0.2040 coco/bbox_mAP_m: 0.4670 coco/bbox_mAP_l: 0.6050 data_time: 0.0280 time: 0.7884 2024/07/08 10:34:57 - mmengine - INFO - Iter(train) [115050/120000] base_lr: 2.8305e-06 lr: 2.0755e-06 eta: 1:40:53 time: 1.2348 data_time: 0.0667 memory: 6804 grad_norm: 1.1354 loss: 0.7044 detection_loss_cls: 0.7044 2024/07/08 10:35:59 - mmengine - INFO - Iter(train) [115100/120000] base_lr: 2.8138e-06 lr: 2.0740e-06 eta: 1:39:52 time: 1.2352 data_time: 0.0667 memory: 6240 grad_norm: 1.1351 loss: 0.7043 detection_loss_cls: 0.7043 2024/07/08 10:37:01 - mmengine - INFO - Iter(train) [115150/120000] base_lr: 2.7973e-06 lr: 2.0725e-06 eta: 1:38:51 time: 1.2356 data_time: 0.0668 memory: 6240 grad_norm: 1.1352 loss: 0.7050 detection_loss_cls: 0.7050 2024/07/08 10:38:03 - mmengine - INFO - Iter(train) [115200/120000] base_lr: 2.7810e-06 lr: 2.0710e-06 eta: 1:37:49 time: 1.2358 data_time: 0.0668 memory: 6240 grad_norm: 1.1346 loss: 0.7058 detection_loss_cls: 0.7058 2024/07/08 10:39:04 - mmengine - INFO - Iter(train) [115250/120000] base_lr: 2.7648e-06 lr: 2.0695e-06 eta: 1:36:48 time: 1.2360 data_time: 0.0668 memory: 6240 grad_norm: 1.1347 loss: 0.7059 detection_loss_cls: 0.7059 2024/07/08 10:40:06 - mmengine - INFO - Iter(train) [115300/120000] base_lr: 2.7488e-06 lr: 2.0681e-06 eta: 1:35:47 time: 1.2359 data_time: 0.0668 memory: 6240 grad_norm: 1.1348 loss: 0.7051 detection_loss_cls: 0.7051 2024/07/08 10:41:07 - mmengine - INFO - Iter(train) [115350/120000] base_lr: 2.7330e-06 lr: 2.0666e-06 eta: 1:34:46 time: 1.2362 data_time: 0.0668 memory: 6240 grad_norm: 1.1342 loss: 0.7044 detection_loss_cls: 0.7044 2024/07/08 10:42:08 - mmengine - INFO - Iter(train) [115400/120000] base_lr: 2.7173e-06 lr: 2.0652e-06 eta: 1:33:45 time: 1.2364 data_time: 0.0667 memory: 6240 grad_norm: 1.1338 loss: 0.7037 detection_loss_cls: 0.7037 2024/07/08 10:43:10 - mmengine - INFO - Iter(train) [115450/120000] base_lr: 2.7018e-06 lr: 2.0638e-06 eta: 1:32:44 time: 1.2365 data_time: 0.0668 memory: 6240 grad_norm: 1.1336 loss: 0.7043 detection_loss_cls: 0.7043 2024/07/08 10:44:11 - mmengine - INFO - Iter(train) [115500/120000] base_lr: 2.6865e-06 lr: 2.0624e-06 eta: 1:31:43 time: 1.2368 data_time: 0.0669 memory: 6240 grad_norm: 1.1343 loss: 0.7057 detection_loss_cls: 0.7057 2024/07/08 10:45:13 - mmengine - INFO - Iter(train) [115550/120000] base_lr: 2.6714e-06 lr: 2.0610e-06 eta: 1:30:42 time: 1.2371 data_time: 0.0669 memory: 6240 grad_norm: 1.1353 loss: 0.7054 detection_loss_cls: 0.7054 2024/07/08 10:46:15 - mmengine - INFO - Iter(train) [115600/120000] base_lr: 2.6564e-06 lr: 2.0597e-06 eta: 1:29:40 time: 1.2372 data_time: 0.0669 memory: 6240 grad_norm: 1.1363 loss: 0.7055 detection_loss_cls: 0.7055 2024/07/08 10:47:17 - mmengine - INFO - Iter(train) [115650/120000] base_lr: 2.6416e-06 lr: 2.0583e-06 eta: 1:28:39 time: 1.2376 data_time: 0.0669 memory: 6240 grad_norm: 1.1366 loss: 0.7061 detection_loss_cls: 0.7061 2024/07/08 10:48:18 - mmengine - INFO - Iter(train) [115700/120000] base_lr: 2.6269e-06 lr: 2.0570e-06 eta: 1:27:38 time: 1.2379 data_time: 0.0669 memory: 6240 grad_norm: 1.1370 loss: 0.7061 detection_loss_cls: 0.7061 2024/07/08 10:49:20 - mmengine - INFO - Iter(train) [115750/120000] base_lr: 2.6125e-06 lr: 2.0557e-06 eta: 1:26:37 time: 1.2381 data_time: 0.0669 memory: 6240 grad_norm: 1.1373 loss: 0.7055 detection_loss_cls: 0.7055 2024/07/08 10:50:22 - mmengine - INFO - Iter(train) [115800/120000] base_lr: 2.5981e-06 lr: 2.0544e-06 eta: 1:25:36 time: 1.2385 data_time: 0.0669 memory: 6240 grad_norm: 1.1375 loss: 0.7049 detection_loss_cls: 0.7049 2024/07/08 10:51:23 - mmengine - INFO - Iter(train) [115850/120000] base_lr: 2.5840e-06 lr: 2.0531e-06 eta: 1:24:35 time: 1.2387 data_time: 0.0669 memory: 6240 grad_norm: 1.1383 loss: 0.7056 detection_loss_cls: 0.7056 2024/07/08 10:52:25 - mmengine - INFO - Iter(train) [115900/120000] base_lr: 2.5700e-06 lr: 2.0518e-06 eta: 1:23:34 time: 1.2388 data_time: 0.0669 memory: 6240 grad_norm: 1.1384 loss: 0.7052 detection_loss_cls: 0.7052 2024/07/08 10:53:26 - mmengine - INFO - Iter(train) [115950/120000] base_lr: 2.5562e-06 lr: 2.0506e-06 eta: 1:22:32 time: 1.2388 data_time: 0.0669 memory: 6240 grad_norm: 1.1383 loss: 0.7047 detection_loss_cls: 0.7047 2024/07/08 10:54:27 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/08 10:54:27 - mmengine - INFO - Iter(train) [116000/120000] base_lr: 2.5426e-06 lr: 2.0493e-06 eta: 1:21:31 time: 1.2392 data_time: 0.0670 memory: 6240 grad_norm: 1.1377 loss: 0.7053 detection_loss_cls: 0.7053 2024/07/08 10:54:27 - mmengine - INFO - Saving checkpoint at 116000 iterations 2024/07/08 10:55:37 - mmengine - INFO - Iter(train) [116050/120000] base_lr: 2.5291e-06 lr: 2.0481e-06 eta: 1:20:31 time: 1.2391 data_time: 0.0669 memory: 6240 grad_norm: 1.1380 loss: 0.7043 detection_loss_cls: 0.7043 2024/07/08 10:56:39 - mmengine - INFO - Iter(train) [116100/120000] base_lr: 2.5158e-06 lr: 2.0469e-06 eta: 1:19:29 time: 1.2393 data_time: 0.0669 memory: 6240 grad_norm: 1.1385 loss: 0.7041 detection_loss_cls: 0.7041 2024/07/08 10:57:40 - mmengine - INFO - Iter(train) [116150/120000] base_lr: 2.5027e-06 lr: 2.0457e-06 eta: 1:18:28 time: 1.2393 data_time: 0.0670 memory: 6240 grad_norm: 1.1392 loss: 0.7060 detection_loss_cls: 0.7060 2024/07/08 10:58:42 - mmengine - INFO - Iter(train) [116200/120000] base_lr: 2.4898e-06 lr: 2.0445e-06 eta: 1:17:27 time: 1.2393 data_time: 0.0670 memory: 6240 grad_norm: 1.1404 loss: 0.7059 detection_loss_cls: 0.7059 2024/07/08 10:59:44 - mmengine - INFO - Iter(train) [116250/120000] base_lr: 2.4770e-06 lr: 2.0434e-06 eta: 1:16:26 time: 1.2393 data_time: 0.0669 memory: 6240 grad_norm: 1.1406 loss: 0.7054 detection_loss_cls: 0.7054 2024/07/08 11:00:45 - mmengine - INFO - Iter(train) [116300/120000] base_lr: 2.4643e-06 lr: 2.0422e-06 eta: 1:15:25 time: 1.2393 data_time: 0.0669 memory: 6240 grad_norm: 1.1412 loss: 0.7054 detection_loss_cls: 0.7054 2024/07/08 11:01:47 - mmengine - INFO - Iter(train) [116350/120000] base_lr: 2.4519e-06 lr: 2.0411e-06 eta: 1:14:24 time: 1.2393 data_time: 0.0669 memory: 6240 grad_norm: 1.1417 loss: 0.7060 detection_loss_cls: 0.7060 2024/07/08 11:02:49 - mmengine - INFO - Iter(train) [116400/120000] base_lr: 2.4396e-06 lr: 2.0400e-06 eta: 1:13:23 time: 1.2394 data_time: 0.0670 memory: 6240 grad_norm: 1.1415 loss: 0.7075 detection_loss_cls: 0.7075 2024/07/08 11:03:50 - mmengine - INFO - Iter(train) [116450/120000] base_lr: 2.4275e-06 lr: 2.0389e-06 eta: 1:12:21 time: 1.2396 data_time: 0.0670 memory: 6240 grad_norm: 1.1409 loss: 0.7080 detection_loss_cls: 0.7080 2024/07/08 11:04:52 - mmengine - INFO - Iter(train) [116500/120000] base_lr: 2.4155e-06 lr: 2.0378e-06 eta: 1:11:20 time: 1.2394 data_time: 0.0669 memory: 6240 grad_norm: 1.1411 loss: 0.7073 detection_loss_cls: 0.7073 2024/07/08 11:05:54 - mmengine - INFO - Iter(train) [116550/120000] base_lr: 2.4038e-06 lr: 2.0367e-06 eta: 1:10:19 time: 1.2396 data_time: 0.0669 memory: 6240 grad_norm: 1.1410 loss: 0.7064 detection_loss_cls: 0.7064 2024/07/08 11:06:55 - mmengine - INFO - Iter(train) [116600/120000] base_lr: 2.3922e-06 lr: 2.0357e-06 eta: 1:09:18 time: 1.2396 data_time: 0.0669 memory: 6240 grad_norm: 1.1405 loss: 0.7059 detection_loss_cls: 0.7059 2024/07/08 11:07:57 - mmengine - INFO - Iter(train) [116650/120000] base_lr: 2.3807e-06 lr: 2.0346e-06 eta: 1:08:17 time: 1.2395 data_time: 0.0669 memory: 6240 grad_norm: 1.1408 loss: 0.7064 detection_loss_cls: 0.7064 2024/07/08 11:08:59 - mmengine - INFO - Iter(train) [116700/120000] base_lr: 2.3695e-06 lr: 2.0336e-06 eta: 1:07:16 time: 1.2396 data_time: 0.0670 memory: 6240 grad_norm: 1.1411 loss: 0.7068 detection_loss_cls: 0.7068 2024/07/08 11:10:00 - mmengine - INFO - Iter(train) [116750/120000] base_lr: 2.3584e-06 lr: 2.0326e-06 eta: 1:06:15 time: 1.2394 data_time: 0.0669 memory: 6240 grad_norm: 1.1417 loss: 0.7059 detection_loss_cls: 0.7059 2024/07/08 11:11:01 - mmengine - INFO - Iter(train) [116800/120000] base_lr: 2.3474e-06 lr: 2.0316e-06 eta: 1:05:13 time: 1.2393 data_time: 0.0670 memory: 6240 grad_norm: 1.1416 loss: 0.7069 detection_loss_cls: 0.7069 2024/07/08 11:12:03 - mmengine - INFO - Iter(train) [116850/120000] base_lr: 2.3367e-06 lr: 2.0306e-06 eta: 1:04:12 time: 1.2394 data_time: 0.0669 memory: 6240 grad_norm: 1.1420 loss: 0.7063 detection_loss_cls: 0.7063 2024/07/08 11:13:04 - mmengine - INFO - Iter(train) [116900/120000] base_lr: 2.3261e-06 lr: 2.0296e-06 eta: 1:03:11 time: 1.2396 data_time: 0.0669 memory: 6240 grad_norm: 1.1433 loss: 0.7069 detection_loss_cls: 0.7069 2024/07/08 11:14:05 - mmengine - INFO - Iter(train) [116950/120000] base_lr: 2.3156e-06 lr: 2.0287e-06 eta: 1:02:10 time: 1.2393 data_time: 0.0669 memory: 6240 grad_norm: 1.1455 loss: 0.7062 detection_loss_cls: 0.7062 2024/07/08 11:15:07 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/08 11:15:07 - mmengine - INFO - Iter(train) [117000/120000] base_lr: 2.3054e-06 lr: 2.0278e-06 eta: 1:01:09 time: 1.2393 data_time: 0.0669 memory: 6240 grad_norm: 1.1454 loss: 0.7054 detection_loss_cls: 0.7054 2024/07/08 11:15:07 - mmengine - INFO - Saving checkpoint at 117000 iterations 2024/07/08 11:16:16 - mmengine - INFO - Iter(train) [117050/120000] base_lr: 2.2953e-06 lr: 2.0268e-06 eta: 1:00:08 time: 1.2394 data_time: 0.0669 memory: 6240 grad_norm: 1.1437 loss: 0.7066 detection_loss_cls: 0.7066 2024/07/08 11:17:17 - mmengine - INFO - Iter(train) [117100/120000] base_lr: 2.2854e-06 lr: 2.0259e-06 eta: 0:59:07 time: 1.2392 data_time: 0.0669 memory: 6240 grad_norm: 1.1436 loss: 0.7061 detection_loss_cls: 0.7061 2024/07/08 11:18:19 - mmengine - INFO - Iter(train) [117150/120000] base_lr: 2.2756e-06 lr: 2.0251e-06 eta: 0:58:06 time: 1.2393 data_time: 0.0668 memory: 6240 grad_norm: 1.1440 loss: 0.7062 detection_loss_cls: 0.7062 2024/07/08 11:19:21 - mmengine - INFO - Iter(train) [117200/120000] base_lr: 2.2661e-06 lr: 2.0242e-06 eta: 0:57:05 time: 1.2395 data_time: 0.0668 memory: 6240 grad_norm: 1.1442 loss: 0.7063 detection_loss_cls: 0.7063 2024/07/08 11:20:22 - mmengine - INFO - Iter(train) [117250/120000] base_lr: 2.2566e-06 lr: 2.0233e-06 eta: 0:56:03 time: 1.2394 data_time: 0.0668 memory: 6240 grad_norm: 1.1439 loss: 0.7069 detection_loss_cls: 0.7069 2024/07/08 11:21:24 - mmengine - INFO - Iter(train) [117300/120000] base_lr: 2.2474e-06 lr: 2.0225e-06 eta: 0:55:02 time: 1.2396 data_time: 0.0668 memory: 6240 grad_norm: 1.1440 loss: 0.7071 detection_loss_cls: 0.7071 2024/07/08 11:22:25 - mmengine - INFO - Iter(train) [117350/120000] base_lr: 2.2383e-06 lr: 2.0217e-06 eta: 0:54:01 time: 1.2397 data_time: 0.0668 memory: 6240 grad_norm: 1.1437 loss: 0.7073 detection_loss_cls: 0.7073 2024/07/08 11:23:27 - mmengine - INFO - Iter(train) [117400/120000] base_lr: 2.2294e-06 lr: 2.0209e-06 eta: 0:53:00 time: 1.2397 data_time: 0.0668 memory: 6240 grad_norm: 1.1436 loss: 0.7070 detection_loss_cls: 0.7070 2024/07/08 11:24:28 - mmengine - INFO - Iter(train) [117450/120000] base_lr: 2.2207e-06 lr: 2.0201e-06 eta: 0:51:59 time: 1.2396 data_time: 0.0668 memory: 6240 grad_norm: 1.1435 loss: 0.7065 detection_loss_cls: 0.7065 2024/07/08 11:25:30 - mmengine - INFO - Iter(train) [117500/120000] base_lr: 2.2121e-06 lr: 2.0193e-06 eta: 0:50:58 time: 1.2398 data_time: 0.0667 memory: 6240 grad_norm: 1.1427 loss: 0.7060 detection_loss_cls: 0.7060 2024/07/08 11:26:32 - mmengine - INFO - Iter(train) [117550/120000] base_lr: 2.2037e-06 lr: 2.0185e-06 eta: 0:49:56 time: 1.2397 data_time: 0.0667 memory: 6240 grad_norm: 1.1418 loss: 0.7056 detection_loss_cls: 0.7056 2024/07/08 11:27:33 - mmengine - INFO - Iter(train) [117600/120000] base_lr: 2.1955e-06 lr: 2.0178e-06 eta: 0:48:55 time: 1.2399 data_time: 0.0667 memory: 6240 grad_norm: 1.1422 loss: 0.7055 detection_loss_cls: 0.7055 2024/07/08 11:28:35 - mmengine - INFO - Iter(train) [117650/120000] base_lr: 2.1875e-06 lr: 2.0170e-06 eta: 0:47:54 time: 1.2399 data_time: 0.0667 memory: 6240 grad_norm: 1.1425 loss: 0.7055 detection_loss_cls: 0.7055 2024/07/08 11:29:36 - mmengine - INFO - Iter(train) [117700/120000] base_lr: 2.1796e-06 lr: 2.0163e-06 eta: 0:46:53 time: 1.2396 data_time: 0.0667 memory: 6240 grad_norm: 1.1428 loss: 0.7059 detection_loss_cls: 0.7059 2024/07/08 11:30:38 - mmengine - INFO - Iter(train) [117750/120000] base_lr: 2.1719e-06 lr: 2.0156e-06 eta: 0:45:52 time: 1.2398 data_time: 0.0668 memory: 6240 grad_norm: 1.1426 loss: 0.7058 detection_loss_cls: 0.7058 2024/07/08 11:31:40 - mmengine - INFO - Iter(train) [117800/120000] base_lr: 2.1643e-06 lr: 2.0149e-06 eta: 0:44:51 time: 1.2400 data_time: 0.0668 memory: 6240 grad_norm: 1.1432 loss: 0.7058 detection_loss_cls: 0.7058 2024/07/08 11:32:41 - mmengine - INFO - Iter(train) [117850/120000] base_lr: 2.1569e-06 lr: 2.0143e-06 eta: 0:43:50 time: 1.2398 data_time: 0.0668 memory: 6240 grad_norm: 1.1433 loss: 0.7064 detection_loss_cls: 0.7064 2024/07/08 11:33:44 - mmengine - INFO - Iter(train) [117900/120000] base_lr: 2.1497e-06 lr: 2.0136e-06 eta: 0:42:48 time: 1.2401 data_time: 0.0668 memory: 6240 grad_norm: 1.1439 loss: 0.7066 detection_loss_cls: 0.7066 2024/07/08 11:34:46 - mmengine - INFO - Iter(train) [117950/120000] base_lr: 2.1427e-06 lr: 2.0130e-06 eta: 0:41:47 time: 1.2403 data_time: 0.0668 memory: 6240 grad_norm: 1.1442 loss: 0.7059 detection_loss_cls: 0.7059 2024/07/08 11:35:47 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/08 11:35:47 - mmengine - INFO - Iter(train) [118000/120000] base_lr: 2.1358e-06 lr: 2.0123e-06 eta: 0:40:46 time: 1.2400 data_time: 0.0667 memory: 6240 grad_norm: 1.1441 loss: 0.7043 detection_loss_cls: 0.7043 2024/07/08 11:35:47 - mmengine - INFO - Saving checkpoint at 118000 iterations 2024/07/08 11:36:56 - mmengine - INFO - Iter(train) [118050/120000] base_lr: 2.1291e-06 lr: 2.0117e-06 eta: 0:39:45 time: 1.2402 data_time: 0.0666 memory: 6240 grad_norm: 1.1445 loss: 0.7046 detection_loss_cls: 0.7046 2024/07/08 11:37:57 - mmengine - INFO - Iter(train) [118100/120000] base_lr: 2.1226e-06 lr: 2.0111e-06 eta: 0:38:44 time: 1.2402 data_time: 0.0666 memory: 6240 grad_norm: 1.1447 loss: 0.7047 detection_loss_cls: 0.7047 2024/07/08 11:38:58 - mmengine - INFO - Iter(train) [118150/120000] base_lr: 2.1162e-06 lr: 2.0106e-06 eta: 0:37:43 time: 1.2400 data_time: 0.0666 memory: 6240 grad_norm: 1.1451 loss: 0.7038 detection_loss_cls: 0.7038 2024/07/08 11:40:00 - mmengine - INFO - Iter(train) [118200/120000] base_lr: 2.1100e-06 lr: 2.0100e-06 eta: 0:36:42 time: 1.2403 data_time: 0.0665 memory: 6240 grad_norm: 1.1457 loss: 0.7033 detection_loss_cls: 0.7033 2024/07/08 11:41:00 - mmengine - INFO - Iter(train) [118250/120000] base_lr: 2.1040e-06 lr: 2.0095e-06 eta: 0:35:40 time: 1.2404 data_time: 0.0665 memory: 6240 grad_norm: 1.1457 loss: 0.7037 detection_loss_cls: 0.7037 2024/07/08 11:42:02 - mmengine - INFO - Iter(train) [118300/120000] base_lr: 2.0981e-06 lr: 2.0089e-06 eta: 0:34:39 time: 1.2405 data_time: 0.0666 memory: 6240 grad_norm: 1.1459 loss: 0.7039 detection_loss_cls: 0.7039 2024/07/08 11:43:03 - mmengine - INFO - Iter(train) [118350/120000] base_lr: 2.0925e-06 lr: 2.0084e-06 eta: 0:33:38 time: 1.2406 data_time: 0.0666 memory: 6240 grad_norm: 1.1460 loss: 0.7033 detection_loss_cls: 0.7033 2024/07/08 11:44:04 - mmengine - INFO - Iter(train) [118400/120000] base_lr: 2.0869e-06 lr: 2.0079e-06 eta: 0:32:37 time: 1.2407 data_time: 0.0667 memory: 6240 grad_norm: 1.1457 loss: 0.7039 detection_loss_cls: 0.7039 2024/07/08 11:45:05 - mmengine - INFO - Iter(train) [118450/120000] base_lr: 2.0816e-06 lr: 2.0074e-06 eta: 0:31:36 time: 1.2407 data_time: 0.0667 memory: 6240 grad_norm: 1.1457 loss: 0.7046 detection_loss_cls: 0.7046 2024/07/08 11:46:06 - mmengine - INFO - Iter(train) [118500/120000] base_lr: 2.0764e-06 lr: 2.0069e-06 eta: 0:30:35 time: 1.2409 data_time: 0.0667 memory: 6240 grad_norm: 1.1454 loss: 0.7038 detection_loss_cls: 0.7038 2024/07/08 11:47:08 - mmengine - INFO - Iter(train) [118550/120000] base_lr: 2.0714e-06 lr: 2.0065e-06 eta: 0:29:33 time: 1.2412 data_time: 0.0667 memory: 6240 grad_norm: 1.1462 loss: 0.7044 detection_loss_cls: 0.7044 2024/07/08 11:48:09 - mmengine - INFO - Iter(train) [118600/120000] base_lr: 2.0666e-06 lr: 2.0061e-06 eta: 0:28:32 time: 1.2413 data_time: 0.0668 memory: 6240 grad_norm: 1.1457 loss: 0.7050 detection_loss_cls: 0.7050 2024/07/08 11:49:10 - mmengine - INFO - Iter(train) [118650/120000] base_lr: 2.0619e-06 lr: 2.0056e-06 eta: 0:27:31 time: 1.2415 data_time: 0.0668 memory: 6240 grad_norm: 1.1460 loss: 0.7057 detection_loss_cls: 0.7057 2024/07/08 11:50:11 - mmengine - INFO - Iter(train) [118700/120000] base_lr: 2.0574e-06 lr: 2.0052e-06 eta: 0:26:30 time: 1.2416 data_time: 0.0668 memory: 6240 grad_norm: 1.1461 loss: 0.7054 detection_loss_cls: 0.7054 2024/07/08 11:51:12 - mmengine - INFO - Iter(train) [118750/120000] base_lr: 2.0531e-06 lr: 2.0048e-06 eta: 0:25:29 time: 1.2416 data_time: 0.0668 memory: 6240 grad_norm: 1.1463 loss: 0.7056 detection_loss_cls: 0.7056 2024/07/08 11:52:14 - mmengine - INFO - Iter(train) [118800/120000] base_lr: 2.0489e-06 lr: 2.0044e-06 eta: 0:24:28 time: 1.2418 data_time: 0.0668 memory: 6240 grad_norm: 1.1461 loss: 0.7057 detection_loss_cls: 0.7057 2024/07/08 11:53:14 - mmengine - INFO - Iter(train) [118850/120000] base_lr: 2.0449e-06 lr: 2.0041e-06 eta: 0:23:26 time: 1.2418 data_time: 0.0667 memory: 6240 grad_norm: 1.1457 loss: 0.7058 detection_loss_cls: 0.7058 2024/07/08 11:54:15 - mmengine - INFO - Iter(train) [118900/120000] base_lr: 2.0411e-06 lr: 2.0037e-06 eta: 0:22:25 time: 1.2418 data_time: 0.0667 memory: 6240 grad_norm: 1.1456 loss: 0.7055 detection_loss_cls: 0.7055 2024/07/08 11:55:17 - mmengine - INFO - Iter(train) [118950/120000] base_lr: 2.0375e-06 lr: 2.0034e-06 eta: 0:21:24 time: 1.2419 data_time: 0.0667 memory: 6240 grad_norm: 1.1457 loss: 0.7054 detection_loss_cls: 0.7054 2024/07/08 11:56:18 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/08 11:56:18 - mmengine - INFO - Iter(train) [119000/120000] base_lr: 2.0340e-06 lr: 2.0031e-06 eta: 0:20:23 time: 1.2421 data_time: 0.0667 memory: 6240 grad_norm: 1.1463 loss: 0.7052 detection_loss_cls: 0.7052 2024/07/08 11:56:18 - mmengine - INFO - Saving checkpoint at 119000 iterations 2024/07/08 11:57:27 - mmengine - INFO - Iter(train) [119050/120000] base_lr: 2.0307e-06 lr: 2.0028e-06 eta: 0:19:22 time: 1.2369 data_time: 0.0615 memory: 6240 grad_norm: 1.1445 loss: 0.7050 detection_loss_cls: 0.7050 2024/07/08 11:58:29 - mmengine - INFO - Iter(train) [119100/120000] base_lr: 2.0275e-06 lr: 2.0025e-06 eta: 0:18:21 time: 1.2371 data_time: 0.0615 memory: 6240 grad_norm: 1.1450 loss: 0.7050 detection_loss_cls: 0.7050 2024/07/08 11:59:30 - mmengine - INFO - Iter(train) [119150/120000] base_lr: 2.0246e-06 lr: 2.0022e-06 eta: 0:17:19 time: 1.2368 data_time: 0.0615 memory: 6240 grad_norm: 1.1449 loss: 0.7046 detection_loss_cls: 0.7046 2024/07/08 12:00:32 - mmengine - INFO - Iter(train) [119200/120000] base_lr: 2.0218e-06 lr: 2.0020e-06 eta: 0:16:18 time: 1.2367 data_time: 0.0615 memory: 6240 grad_norm: 1.1450 loss: 0.7049 detection_loss_cls: 0.7049 2024/07/08 12:01:33 - mmengine - INFO - Iter(train) [119250/120000] base_lr: 2.0191e-06 lr: 2.0017e-06 eta: 0:15:17 time: 1.2369 data_time: 0.0615 memory: 6240 grad_norm: 1.1458 loss: 0.7047 detection_loss_cls: 0.7047 2024/07/08 12:02:35 - mmengine - INFO - Iter(train) [119300/120000] base_lr: 2.0167e-06 lr: 2.0015e-06 eta: 0:14:16 time: 1.2369 data_time: 0.0615 memory: 6240 grad_norm: 1.1457 loss: 0.7055 detection_loss_cls: 0.7055 2024/07/08 12:03:36 - mmengine - INFO - Iter(train) [119350/120000] base_lr: 2.0144e-06 lr: 2.0013e-06 eta: 0:13:15 time: 1.2369 data_time: 0.0615 memory: 6240 grad_norm: 1.1461 loss: 0.7057 detection_loss_cls: 0.7057 2024/07/08 12:04:38 - mmengine - INFO - Iter(train) [119400/120000] base_lr: 2.0123e-06 lr: 2.0011e-06 eta: 0:12:14 time: 1.2370 data_time: 0.0615 memory: 6240 grad_norm: 1.1467 loss: 0.7053 detection_loss_cls: 0.7053 2024/07/08 12:05:39 - mmengine - INFO - Iter(train) [119450/120000] base_lr: 2.0103e-06 lr: 2.0009e-06 eta: 0:11:12 time: 1.2370 data_time: 0.0614 memory: 6240 grad_norm: 1.1469 loss: 0.7042 detection_loss_cls: 0.7042 2024/07/08 12:06:41 - mmengine - INFO - Iter(train) [119500/120000] base_lr: 2.0085e-06 lr: 2.0008e-06 eta: 0:10:11 time: 1.2370 data_time: 0.0615 memory: 6240 grad_norm: 1.1463 loss: 0.7046 detection_loss_cls: 0.7046 2024/07/08 12:07:43 - mmengine - INFO - Iter(train) [119550/120000] base_lr: 2.0069e-06 lr: 2.0006e-06 eta: 0:09:10 time: 1.2370 data_time: 0.0614 memory: 6240 grad_norm: 1.1446 loss: 0.7045 detection_loss_cls: 0.7045 2024/07/08 12:08:44 - mmengine - INFO - Iter(train) [119600/120000] base_lr: 2.0055e-06 lr: 2.0005e-06 eta: 0:08:09 time: 1.2370 data_time: 0.0614 memory: 6240 grad_norm: 1.1433 loss: 0.7042 detection_loss_cls: 0.7042 2024/07/08 12:09:46 - mmengine - INFO - Iter(train) [119650/120000] base_lr: 2.0042e-06 lr: 2.0004e-06 eta: 0:07:08 time: 1.2367 data_time: 0.0615 memory: 6240 grad_norm: 1.1433 loss: 0.7053 detection_loss_cls: 0.7053 2024/07/08 12:10:47 - mmengine - INFO - Iter(train) [119700/120000] base_lr: 2.0031e-06 lr: 2.0003e-06 eta: 0:06:07 time: 1.2369 data_time: 0.0615 memory: 6240 grad_norm: 1.1430 loss: 0.7060 detection_loss_cls: 0.7060 2024/07/08 12:11:49 - mmengine - INFO - Iter(train) [119750/120000] base_lr: 2.0021e-06 lr: 2.0002e-06 eta: 0:05:05 time: 1.2368 data_time: 0.0615 memory: 6240 grad_norm: 1.1427 loss: 0.7059 detection_loss_cls: 0.7059 2024/07/08 12:12:50 - mmengine - INFO - Iter(train) [119800/120000] base_lr: 2.0014e-06 lr: 2.0001e-06 eta: 0:04:04 time: 1.2367 data_time: 0.0616 memory: 6240 grad_norm: 1.1427 loss: 0.7066 detection_loss_cls: 0.7066 2024/07/08 12:13:53 - mmengine - INFO - Iter(train) [119850/120000] base_lr: 2.0008e-06 lr: 2.0001e-06 eta: 0:03:03 time: 1.2369 data_time: 0.0615 memory: 6240 grad_norm: 1.1419 loss: 0.7060 detection_loss_cls: 0.7060 2024/07/08 12:14:54 - mmengine - INFO - Iter(train) [119900/120000] base_lr: 2.0003e-06 lr: 2.0000e-06 eta: 0:02:02 time: 1.2368 data_time: 0.0615 memory: 6240 grad_norm: 1.1426 loss: 0.7058 detection_loss_cls: 0.7058 2024/07/08 12:15:55 - mmengine - INFO - Iter(train) [119950/120000] base_lr: 2.0001e-06 lr: 2.0000e-06 eta: 0:01:01 time: 1.2368 data_time: 0.0615 memory: 6240 grad_norm: 1.1426 loss: 0.7056 detection_loss_cls: 0.7056 2024/07/08 12:16:57 - mmengine - INFO - Exp name: single_detection_base_672_prompt_beta_20240707_002241 2024/07/08 12:16:57 - mmengine - INFO - Iter(train) [120000/120000] base_lr: 2.0000e-06 lr: 2.0000e-06 eta: 0:00:00 time: 1.2370 data_time: 0.0614 memory: 6240 grad_norm: 1.1433 loss: 0.7047 detection_loss_cls: 0.7047 2024/07/08 12:16:57 - mmengine - INFO - Saving checkpoint at 120000 iterations 2024/07/08 12:17:45 - mmengine - INFO - Iter(val) [ 50/105] eta: 0:00:44 time: 0.7996 data_time: 0.0289 memory: 6807 2024/07/08 12:18:25 - mmengine - INFO - Iter(val) [100/105] eta: 0:00:04 time: 0.7996 data_time: 0.0289 memory: 6808 2024/07/08 12:18:31 - mmengine - INFO - Evaluating bbox... 2024/07/08 12:18:57 - mmengine - INFO - bbox_mAP_copypaste: 0.421 0.590 0.452 0.205 0.466 0.606 2024/07/08 12:18:57 - mmengine - INFO - Iter(val) [105/105] coco/bbox_mAP: 0.4210 coco/bbox_mAP_50: 0.5900 coco/bbox_mAP_75: 0.4520 coco/bbox_mAP_s: 0.2050 coco/bbox_mAP_m: 0.4660 coco/bbox_mAP_l: 0.6060 data_time: 0.0283 time: 0.7941