Datasets:
Formats:
parquet
Size:
10K - 100K
| dataset_info: | |
| features: | |
| - name: id | |
| dtype: string | |
| - name: image | |
| dtype: image | |
| - name: height | |
| dtype: int16 | |
| - name: weight | |
| dtype: int16 | |
| - name: body_type | |
| dtype: string | |
| - name: shirt_size | |
| dtype: string | |
| - name: pant_size | |
| dtype: string | |
| - name: body_pose_params | |
| list: float64 | |
| - name: pred_global_rots | |
| list: | |
| list: | |
| list: float64 | |
| - name: focal_length | |
| dtype: float64 | |
| - name: pred_joint_coords | |
| list: | |
| list: float64 | |
| - name: global_rot | |
| list: float64 | |
| - name: shape_params | |
| list: float64 | |
| - name: pred_cam_t | |
| list: float64 | |
| - name: pred_keypoints_2d | |
| list: | |
| list: float64 | |
| - name: pred_keypoints_3d | |
| list: | |
| list: float64 | |
| - name: bbox | |
| list: float64 | |
| - name: scale_params | |
| list: float64 | |
| - name: bmi | |
| dtype: float64 | |
| - name: height_cls | |
| dtype: | |
| class_label: | |
| names: | |
| '0': H_132_155 | |
| '1': H_157_157 | |
| '2': H_160_160 | |
| '3': H_163_163 | |
| '4': H_165_165 | |
| '5': H_168_168 | |
| '6': H_170_170 | |
| '7': H_173_173 | |
| '8': H_175_175 | |
| '9': H_178_178 | |
| '10': H_180_191 | |
| - name: weight_cls | |
| dtype: | |
| class_label: | |
| names: | |
| '0': W_38_53 | |
| '1': W_54_58 | |
| '2': W_59_62 | |
| '3': W_63_66 | |
| '4': W_67_70 | |
| '5': W_71_75 | |
| '6': W_76_79 | |
| '7': W_80_86 | |
| '8': W_87_95 | |
| '9': W_96_109 | |
| '10': W_110_186 | |
| - name: bmi_cls | |
| dtype: | |
| class_label: | |
| names: | |
| '0': BMI_12.5_20 | |
| '1': BMI_20.5_21.5 | |
| '2': BMI_22_23 | |
| '3': BMI_23.5_24.5 | |
| '4': BMI_25_26 | |
| '5': BMI_26.5_28 | |
| '6': BMI_28.5_30 | |
| '7': BMI_30.5_33.5 | |
| '8': BMI_34_38 | |
| '9': BMI_38.5_72.5 | |
| - name: pants_cls | |
| dtype: | |
| class_label: | |
| names: | |
| '0': PANTS_28_32 | |
| '1': PANTS_34_34 | |
| '2': PANTS_36_36 | |
| '3': PANTS_38_38 | |
| '4': PANTS_40_40 | |
| '5': PANTS_42_42 | |
| '6': PANTS_44_46 | |
| '7': PANTS_48_76 | |
| - name: shirt_cls | |
| dtype: | |
| class_label: | |
| names: | |
| '0': SHIRT_32_36 | |
| '1': SHIRT_40_40 | |
| '2': SHIRT_44_44 | |
| '3': SHIRT_46_46 | |
| '4': SHIRT_48_48 | |
| '5': SHIRT_50_66 | |
| - name: labels | |
| list: string | |
| splits: | |
| - name: train | |
| num_bytes: 1754024153 | |
| num_examples: 26817 | |
| - name: test | |
| num_bytes: 193214537 | |
| num_examples: 2977 | |
| download_size: 1711984856 | |
| dataset_size: 1947238690 | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: data/train-* | |
| - split: test | |
| path: data/test-* | |
| tags: | |
| - weight-estimation | |
| - height-estimation | |
| size_categories: | |
| - 10K<n<100K | |
| # mbg-2511-pose-estimation-no_shapes-labeled | |
| ## Summary | |
| TRAIN: 26,817 samples | |
| height_cls: 11 classes, balance 4.0%-13.4% | |
| weight_cls: 11 classes, balance 7.0%-10.0% | |
| bmi_cls: 10 classes, balance 8.4%-11.3% | |
| pants_cls: 8 classes, balance 7.8%-18.1% | |
| shirt_cls: 6 classes, balance 4.3%-30.3% | |
| TEST: 2,977 samples | |
| height_cls: 11 classes, balance 3.7%-12.8% | |
| weight_cls: 11 classes, balance 6.8%-10.2% | |
| bmi_cls: 10 classes, balance 9.0%-11.7% | |
| pants_cls: 8 classes, balance 7.5%-17.4% | |
| shirt_cls: 6 classes, balance 4.9%-31.1% | |
| %3C%2Fspan%3E%3C!-- HTML_TAG_END --> | |
| ### Train & Test Split | |
| ```python | |
| >>> ds | |
| DatasetDict({ | |
| train: Dataset({ | |
| features: ['id', 'image', 'height', 'weight', 'body_type', 'shirt_size', 'pant_size', 'body_pose_params', 'pred_global_rots', 'focal_length', 'pred_joint_coords', 'global_rot', 'shape_params', 'pred_cam_t', 'pred_keypoints_2d', 'pred_keypoints_3d', 'bbox', 'scale_params', 'bmi', 'height_cls', 'weight_cls', 'bmi_cls', 'pants_cls', 'shirt_cls', 'labels'], | |
| num_rows: 26817 | |
| }) | |
| test: Dataset({ | |
| features: ['id', 'image', 'height', 'weight', 'body_type', 'shirt_size', 'pant_size', 'body_pose_params', 'pred_global_rots', 'focal_length', 'pred_joint_coords', 'global_rot', 'shape_params', 'pred_cam_t', 'pred_keypoints_2d', 'pred_keypoints_3d', 'bbox', 'scale_params', 'bmi', 'height_cls', 'weight_cls', 'bmi_cls', 'pants_cls', 'shirt_cls', 'labels'], | |
| num_rows: 2977 | |
| }) | |
| }) | |
| ``` | |
| ### Features | |
| %3C!-- HTML_TAG_END --> | |
| %3C!-- HTML_TAG_END --> | |
| ```python | |
| >>> ds["train"].features | |
| { | |
| 'id': Value('string'), | |
| 'image': Image(mode=None, decode=True), | |
| 'height': Value('int16'), | |
| 'weight': Value('int16'), | |
| 'body_type': Value('string'), | |
| 'shirt_size': Value('string'), | |
| 'pant_size': Value('string'), | |
| 'body_pose_params': List(Value('float64')), | |
| 'pred_global_rots': List(List(List(Value('float64')))), | |
| 'focal_length': Value('float64'), | |
| 'pred_joint_coords': List(List(Value('float64'))), | |
| 'global_rot': List(Value('float64')), | |
| 'shape_params': List(Value('float64')), | |
| 'pred_cam_t': List(Value('float64')), | |
| 'pred_keypoints_2d': List(List(Value('float64'))), | |
| 'pred_keypoints_3d': List(List(Value('float64'))), | |
| 'bbox': List(Value('float64')), | |
| 'scale_params': List(Value('float64')), | |
| 'bmi': Value('float64'), | |
| 'height_cls': ClassLabel(names=['H_132_155', 'H_157_157', 'H_160_160', | |
| 'H_163_163', 'H_165_165', 'H_168_168', | |
| 'H_170_170', 'H_173_173', 'H_175_175', | |
| 'H_178_178', 'H_180_191']), | |
| 'weight_cls': ClassLabel(names=['W_38_53', 'W_54_58', 'W_59_62', 'W_63_66', | |
| 'W_67_70', 'W_71_75', 'W_76_79', 'W_80_86', | |
| 'W_87_95', 'W_96_109', 'W_110_186']), | |
| 'bmi_cls': ClassLabel(names=['BMI_12.5_20', 'BMI_20.5_21.5', 'BMI_22_23', | |
| 'BMI_23.5_24.5', 'BMI_25_26', 'BMI_26.5_28', | |
| 'BMI_28.5_30', 'BMI_30.5_33.5', 'BMI_34_38', | |
| 'BMI_38.5_72.5']), | |
| 'pants_cls': ClassLabel(names=['PANTS_28_32', 'PANTS_34_34', 'PANTS_36_36', | |
| 'PANTS_38_38', 'PANTS_40_40', 'PANTS_42_42', | |
| 'PANTS_44_46', 'PANTS_48_76']), | |
| 'shirt_cls': ClassLabel(names=['SHIRT_32_36', 'SHIRT_40_40', 'SHIRT_44_44', | |
| 'SHIRT_46_46', 'SHIRT_48_48', 'SHIRT_50_66']), | |
| 'labels': List(Value('string')) | |
| } | |
| ``` | |
| ### Sample | |
| ```python | |
| >>> ds['train'][0] | |
| id: 382327 | |
| image: <PIL.Image.Image image mode=RGB size=500x1037 at 0x2B95CD6D82C0> | |
| height: 160 | |
| weight: 50 | |
| body_type: rectangle | |
| shirt_size: 36 | |
| pant_size: 28 | |
| body_pose_params: [[ 0.03886835 -0.00208342 -0.02778961]...[0. 0.]] shape=(133,), dtype=float64 | |
| pred_global_rots: [[[[ 1. 0. 0. ] | |
| [ 0. 1. 0. ] | |
| [ 0. 0. 1. ]] | |
| [[ 0.99640441 0.05728238 -0.06242636] | |
| [-0.06423091 0.99120814 -0.11567552] | |
| [ 0.05525135 0.1192693 0.99132341]] | |
| [[-0.0984109 -0.05719048 0.99350119] | |
| [-0.99246413 0.07888412 -0.09376723] | |
| [-0.07300889 -0.99524194 -0.06452253]]]...[[[ 0.98913765 0.07291167 0.12763445] | |
| [-0.02712706 0.94393992 -0.32900089] | |
| [-0.14446725 0.3219648 0.93566442]] | |
| [[ 0.07291168 0.03137279 0.99684489] | |
| [ 0.94393992 -0.32483506 -0.05881885] | |
| [ 0.32196486 0.94525027 -0.05329826]]]] shape=(127, 3, 3), dtype=float64 | |
| focal_length: 1014.9290161132812 | |
| pred_joint_coords: [[[ 0. -0. -0. ] | |
| [ 0. -0.92398697 -0. ] | |
| [ 0.08633898 -0.8986935 -0.00758996]]...[[ 0.0923527 -1.56066072 -0.27626431] | |
| [ 0.0634833 -1.68751919 -0.22902386]]] shape=(127, 3), dtype=float64 | |
| global_rot: [0.11973767727613449, -0.05527949333190918, -0.06437363475561142] | |
| shape_params: [[-0.76408136 0.79042131 -1.15044034]...[ 0.12328251 -0.02765466]] shape=(45,), dtype=float64 | |
| pred_cam_t: [-0.006475623697042465, 1.0043303966522217, 1.6861722469329834] | |
| pred_keypoints_2d: [[[286.81719971 143.6751709 ] | |
| [311.28182983 119.13873291] | |
| [263.91549683 114.87661743]]...[[165.12356567 245.27871704] | |
| [272.43045044 236.9727478 ]]] shape=(70, 2), dtype=float64 | |
| pred_keypoints_3d: [[[ 0.05692464 -1.51793671 -0.29545864] | |
| [ 0.09171973 -1.55984902 -0.27438799] | |
| [ 0.02577711 -1.56417561 -0.27841634]]...[[-0.12643629 -1.43218017 -0.09684809] | |
| [ 0.04102116 -1.43791533 -0.12306281]]] shape=(70, 3), dtype=float64 | |
| bbox: [54.824241638183594, 207.05712890625, 494.9328308105469, 1034.299072265625] | |
| scale_params: [[ 0.00039525 -0.0016843 -0.00043011]...[ 0.39154088 -0.13409461]] shape=(28,), dtype=float64 | |
| bmi: 19.5 | |
| height_cls: 2 | |
| weight_cls: 0 | |
| bmi_cls: 0 | |
| pants_cls: 0 | |
| shirt_cls: 0 | |
| labels: ['H_160_160', 'W_38_53', 'BMI_12.5_20', 'PANTS_28_32', 'SHIRT_32_36'] | |
| ``` | |
| ## Bins | |
| %3C!-- HTML_TAG_END --> | |
| ### Bins for height | |
| - H_132_155: 2,088 samples (7.8%) | |
| - H_157_157: 2,042 samples (7.6%) | |
| - H_160_160: 2,846 samples (10.6%) | |
| - H_163_163: 3,602 samples (13.4%) | |
| - H_165_165: 3,272 samples (12.2%) | |
| - H_168_168: 3,466 samples (12.9%) | |
| - H_170_170: 3,175 samples (11.8%) | |
| - H_173_173: 2,380 samples (8.9%) | |
| - H_175_175: 1,725 samples (6.4%) | |
| - H_178_178: 1,154 samples (4.3%) | |
| - H_180_191: 1,067 samples (4.0%) | |
| ### Bins for weight | |
| - W_38_53: 2,529 samples (9.4%) | |
| - W_54_58: 2,496 samples (9.3%) | |
| - W_59_62: 2,392 samples (8.9%) | |
| - W_63_66: 2,562 samples (9.6%) | |
| - W_67_70: 2,601 samples (9.7%) | |
| - W_71_75: 2,681 samples (10.0%) | |
| - W_76_79: 1,869 samples (7.0%) | |
| - W_80_86: 2,645 samples (9.9%) | |
| - W_87_95: 2,335 samples (8.7%) | |
| - W_96_109: 2,389 samples (8.9%) | |
| - W_110_186: 2,318 samples (8.6%) | |
| ### Bins for bmi | |
| - BMI_12.5_20: 3,043 samples (11.3%) | |
| - BMI_20.5_21.5: 2,567 samples (9.6%) | |
| - BMI_22_23: 2,743 samples (10.2%) | |
| - BMI_23.5_24.5: 2,809 samples (10.5%) | |
| - BMI_25_26: 2,666 samples (9.9%) | |
| - BMI_26.5_28: 2,716 samples (10.1%) | |
| - BMI_28.5_30: 2,243 samples (8.4%) | |
| - BMI_30.5_33.5: 2,904 samples (10.8%) | |
| - BMI_34_38: 2,476 samples (9.2%) | |
| - BMI_38.5_72.5: 2,650 samples (9.9%) | |
| ### Bins for pant_size | |
| - PANTS_28_32: 4,858 samples (18.1%) | |
| - PANTS_34_34: 3,020 samples (11.3%) | |
| - PANTS_36_36: 3,467 samples (12.9%) | |
| - PANTS_38_38: 3,431 samples (12.8%) | |
| - PANTS_40_40: 3,331 samples (12.4%) | |
| - PANTS_42_42: 2,715 samples (10.1%) | |
| - PANTS_44_46: 3,890 samples (14.5%) | |
| - PANTS_48_76: 2,105 samples (7.8%) | |
| ### Bins for shirt_size | |
| - SHIRT_32_36: 7,034 samples (26.2%) | |
| - SHIRT_40_40: 8,134 samples (30.3%) | |
| - SHIRT_44_44: 5,542 samples (20.7%) | |
| - SHIRT_46_46: 3,171 samples (11.8%) | |
| - SHIRT_48_48: 1,158 samples (4.3%) | |
| - SHIRT_50_66: 1,778 samples (6.6%) | |
| ## Analysis | |
| ### Analyses Included | |
| | Analysis | Purpose | Architecture/Training Impact | | |
| |----------|---------|------------------------------| | |
| | **Feature Structure** | Exact shapes of each feature (e.g., keypoints Nx3) | Determines if you need attention/conv vs MLP | | |
| | **Feature Statistics** | Mean, std, skewness, outliers, constant dims | Normalization strategy, dead feature pruning | | |
| | **Feature Correlations** | Inter-group correlations | Feature fusion strategy, redundancy removal | | |
| | **PCA Analysis** | Intrinsic dimensionality | Bottleneck layer sizing | | |
| | **Feature Redundancy** | Effective rank per feature group | Per-group compression potential | | |
| | **Target Correlations** | Spearman + mutual info between targets | Shared representation depth, MTL benefits | | |
| | **Class Distributions** | Imbalance ratios, entropy | Class weights, sampling strategy | | |
| | **Joint Distribution** | Multi-label co-occurrence | Combined loss behavior | | |
| | **Feature Importance** | RF importance + linear probes | Feature weighting, gating | | |
| | **Feature Ablation** | Leave-one-out accuracy drop | Critical features identification | | |
| | **Per-Feature Accuracy** | Individual group predictive power | Which features to prioritize | | |
| | **Class Separability** | LDA + t-SNE | Expected ceiling, nonlinearity needs | | |
| | **Sample Difficulty** | k-NN consistency | Curriculum learning, hard mining | | |
| | **Train/Test Comparison** | Distribution shift | Generalization expectations | | |
| | **Ordinal Structure** | Monotonicity with raw values | Ordinal regression vs classification | | |
| | **Keypoint Correlations** | Which keypoint dims predict what | Body-aware feature selection | | |
| | **Normalization Strategy** | Outliers, bounds checking | Per-feature normalization choice | | |
| | **Class Weights** | Balanced + effective weights | Loss weighting values | | |
| ### Key Questions Answered | |
| 1. **How many pose feature dimensions do you actually have?** (for MLP sizing) | |
| 2. **What's the effective dimensionality after PCA?** (bottleneck size) | |
| 3. **Which targets are correlated?** (shared representation design) | |
| 4. **How hard is each classification task?** (loss weighting) | |
| 5. **Which features matter most?** (attention/gating design) | |
| 6. **Should you use ordinal regression?** (loss function choice) | |
| 7. **How much class imbalance?** (sampling strategy) | |
| 8. **Are there redundant dimensions to prune?** (efficiency) | |
| ### Full Analysis | |
| can be found in `./analysis/analysis_results.json` | |
| ## Addendum | |
| ### Per Feature Accuracy | |
| %3C!-- HTML_TAG_END --> | |
| ### PCA Analysis | |
| %3C!-- HTML_TAG_END --> | |
| ### Target Correlations | |
| %3C!-- HTML_TAG_END --> | |
| ### TSNE Visualization | |
| %3C!-- HTML_TAG_END --> | |