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README.md

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+---
+language: en
+tags:
+  - sign-language
+  - asl
+  - transformer
+  - pytorch
+  - prototype-learning
+  - curriculum-learning
+---
+
+# ASL Sign Language Recognition — Training Results
+
+Trained by **SharoonArshad**
+
+## Results
+
+| Metric | Score |
+|--------|-------|
+| Overall macro-F1 | **77.24%** |
+| Accuracy | **68.51%** |
+| Rare signs F1 | **99.47%** |
+| Medium signs F1 | **59.25%** |
+| Common signs F1 | **58.68%** |
+| Training time | **52 minutes** |
+
+## Model Details
+- **Architecture**: Transformer Encoder + Prototype Classifier
+- **Parameters**: 3.57M
+- **Classes**: 4,618 ASL signs
+- **Input**: `[60 frames × 204 features]` body + hand landmarks
+- **Training**: 3-phase curriculum (common → rare → fine-tune)
+
+## Files
+
+| File | Description |
+|------|-------------|
+| `checkpoints/asl_v3_epoch050_score0.5728.pt` | Best PyTorch checkpoint |
+| `checkpoints/asl_model.onnx` | ONNX export for deployment |
+| `logs/training_history.json` | Loss + F1 for all 50 epochs |
+| `logs/test_results.json` | Final test set results |
+| `logs/train_v4.log` | Full training log |
+| `label_map.json` | Sign ID → Sign name mapping |
+| `tier_info.json` | Rare / medium / common class splits |
+| `class_distribution.json` | Samples per class |
+| `asl_results_complete.zip` | All files in one zip |
+
+## How to Load
+
+```python
+import torch
+from model_transformer import build_model
+from config import CFG
+
+ckpt = torch.load("asl_v3_epoch050_score0.5728.pt", map_location="cpu")
+CFG.model.num_classes = ckpt["cfg"]["num_classes"]  # 4618
+
+model = build_model(CFG, feature_dim_override=204)
+model.load_state_dict(ckpt["model_state"])
+model.eval()
+
+# Inference
+features     = torch.zeros(1, 60, 204)  # replace with real data
+padding_mask = torch.zeros(1, 60, dtype=torch.bool)
+logits, _    = model(features, padding_mask)
+predicted    = logits.argmax(dim=-1).item()
+print(f"Predicted sign ID: {predicted}")
+```