Updated model card

README.md

@@ -1,3 +1,55 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+
+# Model Card: Chinese Calligraphy Character Classifier (ResNet50-based)
+
+## Model Details
+- Architecture: ResNet50 pretrained on ImageNet + custom classifier head
+- Classes: 1200 Chinese calligraphy characters
+- Input: 224x224 RGB images (grayscale converted to RGB)
+- Framework: PyTorch
+
+## Intended Use
+- Handwritten Chinese calligraphy OCR and recognition
+- For research, cultural preservation, and academic purposes
+
+## Dataset
+- EthicalSplit5508v3
+- Train: 60,168 images | Val: 1,200 | Test: 1,200
+- 1200 classes with fixed splits
+
+## Training
+- Batch size: 64, Learning rate: 3e-5 with OneCycleLR scheduler
+- Epochs: up to 50, early stopping enabled
+- Optimizer: Adam with weight decay 1e-4
+- Loss: Cross-entropy with label smoothing (0.1)
+
+## Performance
+- Validation loss reduced from ~5.7 to ~1.06
+- Test accuracy: ~88%+
+- Model size: ~25M parameters
+
+## Limitations
+- May underperform on unseen handwriting styles or poor image quality
+- Uses RGB input; grayscale-specific training not applied
+- Dataset biases may affect generalization
+
+## Ethical Considerations
+- Dataset complies with ethical usage; no PII involved
+- Intended for cultural and academic use only
+
+## Usage Example
+```python
+model = ChineseClassifier(embed_dim=512, num_classes=1200, pretrainedEncoder=True, unfreezeEncoder=True)
+checkpoint = torch.load("best_checkpoint.pth", map_location=device)
+model.load_state_dict(checkpoint["model_state_dict"])
+model.eval()
+
+transform = CalligraphyCharacterDataset.defaultTransform()
+img = Image.open("path_to_image.jpg").convert("RGB")
+input_tensor = transform(img).unsqueeze(0).to(device)
+
+outputs = model(input_tensor)
+pred_idx = torch.argmax(outputs, dim=1).item()
+pred_char = idx2char[pred_idx]