Research artifact — do not use for medical, fitness, or individual assessment. Trained on celebrity images; under-predicts heavier subjects by 14-15 kg.

Load & predict

Model Card — Human Weight Prediction from a Full-Body Image

1. Model details

• Architecture. ResNet-50 (ImageNet-pretrained) trunk + regression head Linear(2048→256) → ReLU → Dropout(0.3) → Linear(256→1).
• Hybrid variants.
  • Feature fusion (FusionRegressor): CNN 2048-d penultimate features concatenated with a small MLP over a 7-d anthropometric vector → shared head.
  • Late fusion: trained-CNN predictions + an XGBoost regressor on the anthropometric features, combined by a linear meta-learner.
• Anthropometric features (7-d). Six scale-invariant pose features — shoulder-width/torso, hip-width/torso, shoulder/hip ratio, leg-length/torso, arm-length/torso, bbox width/height — plus a pose_valid flag. Extracted with MediaPipe PoseLandmarker (Tasks API, 33-point BlazePose). Normalising by torso length makes them invariant to camera distance (a single image has no absolute scale).
• Training. Huber (Smooth-L1) loss; AdamW with split LRs (head 1e-4, backbone 1e-5, weight-decay 1e-4); cosine schedule; AMP on CUDA; early stopping on validation MAE (patience 7); batch 64; ≤40 epochs; input 224×224 with ImageNet normalisation. Targets standardised (mean 65.8, std 16.0 kg); metrics inverted to kg.
• Frameworks. PyTorch / torchvision, MediaPipe, scikit-learn, XGBoost.

2. Intended use

• Intended. Research and education — studying how far appearance + body-shape cues can estimate weight from one image; coursework / IEEE write-up.
• Out of scope (do not use for). Any medical, clinical, insurance, fitness, employment, or individual assessment. Outputs are point estimates with large per-person error (see §5) and a known directional bias against heavier bodies.

3. Data

• Source. Celeb-FBI (alecccdd/celeb-fbi) — 7,208 full-body celebrity photos.
• Splits. Provided test split (721) held out untouched. Training pool split 90/10 into train 5,032 / val 559, BMI-stratified and seeded (seed 42); the four ablation models share this identical split.
• Labels. weight (kg, target), height (cm, for BMI), gender, age; missing values use a -1 sentinel and are dropped (units confirmed kg/cm in EDA).
• Pose coverage. Landmarks detected on 6,480 / 6,487 train (99.9%) and 721 / 721 test images, so the anthropometric branch is real signal, not imputation.
• Leakage. Per-image id checked across splits — no id shared. Residual risk remains: a celebrity may appear under multiple ids, so subject-level leakage cannot be fully excluded (documented limitation).

4. Metrics (test set, 721 images)

5. Error analysis & limitations

• Dominant failure: heavy subjects are under-predicted. The worst-20 cases are large/muscular men (true 102–109 kg) predicted ~70 kg — 30+ kg under. The model is accurate (~6 kg) on the well-represented normal/underweight bands and collapses on the rare overweight/obese bands.
• Root cause is dataset bias, not the features. Celebrities skew lean and tall, so the training distribution has few heavy exemplars; the model regresses toward the mean (65.8 kg) for out-of-distribution bodies. Adding anthropometric features helps only marginally because the limitation is coverage, not signal.
• No absolute scale. Weight fundamentally needs size, which a single image lacks; scale-invariant ratios supply shape but not size — hence anthropometric- only is the weakest model (9.7 kg).
• Other risks. Heavy clothing, unusual poses, occlusion, and extreme framing degrade both the CNN and pose features. Gender/age fairness beyond the gender/BMI breakdowns here was not exhaustively audited.

6. Ethical considerations

7. Reproduce the evaluation

1. pip install -r requirements.txt
2. Obtain the best checkpoint (*_best.pt, written to the Drive cache during training) and the cached pose-feature parquets.
3. Run notebooks/97_colab_eval.ipynb (or the equivalent src.eval calls) — it regenerates the ablation table, BMI confusion matrices, error-by-group charts, and the worst-k grid into reports/.