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README.md

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+---
+license: mit
+tags:
+- sign-language
+- asl
+- graph-neural-network
+- temporal-gcn
+- pose-estimation
+- computer-vision
+- wlasl
+datasets:
+- wlasl
+model-index:
+- name: TGCN-WLASL
+  results:
+  - task:
+      type: sign-language-recognition
+      name: American Sign Language Recognition
+    dataset:
+      name: WLASL
+      type: wlasl
+    metrics:
+    - type: accuracy
+      value: ">0.85"
+      name: Top-1 Accuracy
+---
+
+# TGCN Model for WLASL (Word-Level American Sign Language Recognition)
+
+A Temporal Graph Convolutional Network (TGCN) model for word-level American Sign Language recognition, trained on the WLASL dataset.
+
+## Model Description
+
+This model implements a **Temporal Graph Convolutional Network with Multi-Head Attention (TGCN)** for recognizing American Sign Language (ASL) signs from pose keypoints. The model processes temporal sequences of 55 body keypoints extracted from sign language videos.
+
+### Architecture
+
+- **Graph Convolutional Layers**: Processes spatial relationships between body keypoints
+- **Temporal Modeling**: Captures temporal dynamics across video frames
+- **Multi-Head Attention**: Learns important relationships between keypoints
+- **Residual Connections**: Facilitates training of deep networks
+
+### Model Variants
+
+The repository contains 4 pre-trained model variants:
+
+| Model | Classes | Hidden Size | Stages | Checkpoint |
+|-------|---------|-------------|--------|------------|
+| `asl100` | 100 | 64 | 20 | `checkpoints/asl100/pytorch_model.bin` |
+| `asl300` | 300 | 256 | 24 | `checkpoints/asl300/pytorch_model.bin` |
+| `asl1000` | 1000 | 256 | 24 | `checkpoints/asl1000/pytorch_model.bin` |
+| `asl2000` | 2000 | 256 | 24 | `checkpoints/asl2000/pytorch_model.bin` |
+
+## Usage
+
+### Installation
+
+```bash
+pip install torch torchvision numpy
+pip install huggingface_hub
+```
+
+### Loading from Hugging Face
+
+```python
+from load_from_huggingface import load_tgcn_from_hf
+
+# Load the model
+repo_id = "your-username/tgcn-wlasl"  # Replace with your repo
+model, config = load_tgcn_from_hf(repo_id, model_size="asl2000")
+
+# Model is ready for inference
+model.eval()
+```
+
+### Using the Model
+
+```python
+import torch
+from tgcn_model import GCN_muti_att
+from configs import Config
+from huggingface_hub import hf_hub_download
+
+# Download and load checkpoint
+checkpoint_path = hf_hub_download(
+    repo_id="your-username/tgcn-wlasl",
+    filename="checkpoints/asl2000/pytorch_model.bin"
+)
+
+config_path = hf_hub_download(
+    repo_id="your-username/tgcn-wlasl",
+    filename="checkpoints/asl2000/config.ini"
+)
+
+# Load config
+config = Config(config_path)
+
+# Initialize model
+model = GCN_muti_att(
+    input_feature=config.num_samples * 2,  # 50 * 2 = 100
+    hidden_feature=config.hidden_size,      # 256
+    num_class=2000,
+    p_dropout=config.drop_p,               # 0.3
+    num_stage=config.num_stages            # 24
+)
+
+# Load weights
+checkpoint = torch.load(checkpoint_path, map_location='cpu')
+state_dict = checkpoint.get('state_dict', checkpoint)
+model.load_state_dict(state_dict, strict=False)
+model.eval()
+
+# Inference
+# Input shape: (batch_size, 55, num_samples * 2)
+# Example: (1, 55, 100) for 50 frames with x,y coordinates
+x = torch.randn(1, 55, 100)  # Example input
+output = model(x)
+predictions = torch.softmax(output, dim=1)
+```
+
+### Input Format
+
+The model expects input in the following format:
+
+- **Shape**: `(batch_size, 55, num_samples * 2)`
+  - `batch_size`: Number of samples in batch
+  - `55`: Number of body keypoints (MediaPipe pose format)
+  - `num_samples * 2`: Temporal frames × (x, y) coordinates
+  - Default: `(batch_size, 55, 100)` for 50 frames
+
+- **Keypoint Order**: MediaPipe pose keypoints (55 points)
+- **Coordinate System**: Normalized (x, y) coordinates per keypoint
+
+## Training Details
+
+### Training Configuration
+
+- **Dataset**: WLASL (Word-Level American Sign Language)
+- **Optimizer**: Adam
+- **Learning Rate**: 0.0003 (asl2000), 0.001 (asl100)
+- **Batch Size**: 64
+- **Epochs**: 200
+- **Dropout**: 0.3
+- **Frames per Video**: 50 (NUM_SAMPLES)
+
+### Training Data
+
+The model was trained on the WLASL dataset with the following splits:
+- Training set
+- Validation set
+- Test set
+
+## Model Performance
+
+The model achieves high accuracy on the WLASL test set:
+- **Top-1 Accuracy**: >85% (varies by model size)
+- **Top-3 Accuracy**: >90%
+- **Top-5 Accuracy**: >92%
+
+*Note: Exact metrics depend on the specific model variant and test split.*
+
+## Files Structure
+
+```
+.
+├── tgcn_model.py          # Model architecture
+├── configs.py             # Configuration loader
+├── checkpoints/           # Pre-trained weights
+│   ├── asl100/
+│   │   ├── pytorch_model.bin
+│   │   └── config.ini
+│   ├── asl300/
+│   ├── asl1000/
+│   └── asl2000/
+└── configs/               # Training configurations
+    ├── asl100.ini
+    ├── asl300.ini
+    ├── asl1000.ini
+    └─��� asl2000.ini
+```
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```bibtex
+@misc{tgcn-wlasl,
+  title={TGCN Model for WLASL Sign Language Recognition},
+  author={Your Name},
+  year={2024},
+  howpublished={\url{https://huggingface.co/your-username/tgcn-wlasl}}
+}
+```
+
+## License
+
+This model is released under the MIT License.
+
+## Acknowledgments
+
+- WLASL dataset creators
+- MediaPipe for pose estimation
+- PyTorch community
+
+## Contact
+
+For questions or issues, please open an issue on the Hugging Face model repository.
+