Datasets:
Formats:
parquet
Size:
10K - 100K
metadata
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%
Train & Test Split
>>> 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
>>> 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
>>> 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
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
- How many pose feature dimensions do you actually have? (for MLP sizing)
- What's the effective dimensionality after PCA? (bottleneck size)
- Which targets are correlated? (shared representation design)
- How hard is each classification task? (loss weighting)
- Which features matter most? (attention/gating design)
- Should you use ordinal regression? (loss function choice)
- How much class imbalance? (sampling strategy)
- Are there redundant dimensions to prune? (efficiency)
Full Analysis
can be found in ./analysis/analysis_results.json
Addendum
Per Feature Accuracy
PCA Analysis
Target Correlations
TSNE Visualization
