timesformer-base-finetuned-k400-finetuned-MicroLens_classification

This model is a fine-tuned version of facebook/timesformer-base-finetuned-k400 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.6900
• Accuracy: 0.5327
• 0 Precision: 0.5219
• 0 Recall: 0.8770
• 0 F1-score: 0.6544
• 0 Support: 88401.0
• 1 Precision: 0.5927
• 1 Recall: 0.1822
• 1 F1-score: 0.2787
• 1 Support: 86829.0
• Accuracy F1-score: 0.5327
• Macro avg Precision: 0.5573
• Macro avg Recall: 0.5296
• Macro avg F1-score: 0.4666
• Macro avg Support: 175230.0
• Weighted avg Precision: 0.5570
• Weighted avg Recall: 0.5327
• Weighted avg F1-score: 0.4682
• Weighted avg Support: 175230.0

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• training_steps: 11600

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	0 Precision	0 Recall	0 F1-score	0 Support	1 Precision	1 Recall	1 F1-score	1 Support	Accuracy F1-score	Macro avg Precision	Macro avg Recall	Macro avg F1-score	Macro avg Support	Weighted avg Precision	Weighted avg Recall	Weighted avg F1-score	Weighted avg Support
0.6962	0.05	580	0.6924	0.5439	0.5404	0.6402	0.5861	88401.0	0.5489	0.4457	0.4920	86829.0	0.5439	0.5447	0.5430	0.5390	175230.0	0.5446	0.5439	0.5395	175230.0
0.7128	1.05	1160	0.6996	0.4982	0.5020	0.6570	0.5691	88401.0	0.4908	0.3366	0.3993	86829.0	0.4982	0.4964	0.4968	0.4842	175230.0	0.4965	0.4982	0.4850	175230.0
0.7141	2.0501	1741	0.7067	0.5070	0.5611	0.1043	0.1759	88401.0	0.5014	0.9170	0.6483	86829.0	0.5070	0.5313	0.5106	0.4121	175230.0	0.5315	0.5070	0.4100	175230.0
0.7048	3.0501	2322	0.6925	0.5304	0.5485	0.3911	0.4566	88401.0	0.5202	0.6722	0.5865	86829.0	0.5304	0.5344	0.5317	0.5216	175230.0	0.5345	0.5304	0.5210	175230.0
0.6834	4.05	2902	0.6919	0.5342	0.5977	0.2343	0.3366	88401.0	0.5185	0.8395	0.6410	86829.0	0.5342	0.5581	0.5369	0.4888	175230.0	0.5585	0.5342	0.4875	175230.0
0.6908	5.0501	3483	0.6900	0.5327	0.5219	0.8770	0.6544	88401.0	0.5927	0.1822	0.2787	86829.0	0.5327	0.5573	0.5296	0.4666	175230.0	0.5570	0.5327	0.4682	175230.0

Framework versions

• Transformers 4.46.3
• Pytorch 2.0.0+cu117
• Datasets 3.1.0
• Tokenizers 0.20.3