Add model card with performance metrics and usage example

README.md

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+---
+license: apache-2.0
+tags:
+  - bone-age
+  - medical-imaging
+  - tanner-whitehouse-2
+  - efficientnet-b3
+  - multi-task-learning
+  - pediatric-radiology
+language:
+  - en
+---
+
+# BoneAgeTW2 — EfficientNet-B3 Multi-Head TW2 Stage Classifier
+
+**Paper:** [arXiv:2607.23224](https://arxiv.org/abs/2607.23224)  
+**Code:** [github.com/jmmana/BoneAgeTW2](https://github.com/jmmana/BoneAgeTW2)  
+**Author:** Juan Manuel Castillo Pinto — Universidad de La Salle, Bogotá
+
+---
+
+## What is this?
+
+A PyTorch checkpoint of the stage classifier from **BoneAgeTW2**, an end-to-end deep learning
+system for automated **Tanner-Whitehouse 2 (TW2)** skeletal maturation assessment.
+
+The model uses a shared **EfficientNet-B3** backbone with **20 independent classification heads**
+(one per TW2 bone site) to simultaneously predict the maturation stage of each bone in a
+hand radiograph.
+
+## Model Performance (Validation, n=1,262 — RSNA Bone Age Dataset)
+
+| Metric | Value |
+|---|---|
+| Mean exact accuracy (all 20 bones) | **65.82%** |
+| Mean within-1 accuracy (all 20 bones) | **96.77%** |
+| End-to-end MAE (RUS pathway) | **14.71 months** |
+| Carpal bones exact accuracy | 65.1–84.4% |
+| RUS long bones exact accuracy | 56.7–66.8% |
+
+The within-1 accuracy of 96.77% falls within the human radiologist inter-rater range (King et al., 1994).
+
+## Checkpoint
+
+| File | Size | Epochs | Loss |
+|---|---|---|---|
+| `models/stage_classifier_epoch2.pt` | 72 MB | 2 | 0.9477 |
+
+## Loading the Model
+
+```python
+import torch, timm
+from huggingface_hub import hf_hub_download
+
+# Download
+ckpt_path = hf_hub_download("maktub83/BoneAgeTW2", "models/stage_classifier_epoch2.pt")
+checkpoint = torch.load(ckpt_path, map_location="cpu")
+
+# Rebuild backbone
+backbone = timm.create_model("efficientnet_b3", pretrained=False, num_classes=0)
+backbone.load_state_dict(checkpoint["backbone"])
+backbone.eval()
+
+# feat_dim = 1536
+feat_dim = checkpoint["feat_dim"]
+
+# Each head: Linear(feat_dim, n_stages)
+# See github.com/jmmana/BoneAgeTW2/training/04_train_stage_classifier.py
+# for build_model() and BONE_NAMES
+```
+
+## TW2 Bones (20 heads)
+
+**RUS (13):** radius, ulna, mc1, mc3, mc5, pp1, pp3, pp5, mp3, mp5, dp1, dp3, dp5  
+**Carpal (7):** capitate, hamate, triquetral, lunate, scaphoid, trapezoid, trapezium
+
+## Pseudo-Label Method
+
+Training labels were generated by **Gaussian inversion** of the published TW2 reference
+distributions: for each bone, age, and sex, the MAP stage from `gaussian_params.json`
+is assigned as the pseudo-label. This produces 252,220 annotations from 12,611 RSNA
+radiographs without any manual effort.
+
+## Citation
+
+```bibtex
+@article{castillo2026boneagetw2,
+  author  = {Castillo~Pinto, Juan~Manuel},
+  title   = {{BoneAgeTW2}: Automated Skeletal Maturation Assessment via the {Tanner-Whitehouse 2} Method, Deep Learning, and Clinical Report Generation with Distribution Curves},
+  journal = {arXiv preprint arXiv:2607.23224},
+  year    = {2026}
+}
+```