Upload README.md with huggingface_hub

README.md

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+---
+tags:
+- computer-vision
+- defect-detection
+- pytorch
+- onnx
+library_name: pytorch
+---
+
+# Defect Classifier (ResNet18)
+
+Steel surface defect classification model trained on NEU-DET dataset.
+
+## Model Details
+
+- **Architecture**: ResNet18
+- **Classes**: 6 defect types (crazing, inclusion, patches, pitted_surface, rolled-in_scale, scratches)
+- **Input**: 224x224 RGB images
+- **Formats**: PyTorch (.pth), ONNX (.onnx)
+
+## Files
+
+- `pytorch_model.pth`: PyTorch checkpoint
+- `model.onnx`: ONNX format for deployment
+- `metadata.json`: Model configuration
+- `training_history.json`: Training metrics
+
+## Usage
+
+### PyTorch
+
+```python
+import torch
+from torchvision import models
+from huggingface_hub import hf_hub_download
+
+# Download model
+model_path = hf_hub_download(repo_id="Seif-melz/defect-classifier-resnet18", filename="pytorch_model.pth")
+
+# Load model
+model = models.resnet18()
+model.fc = torch.nn.Linear(model.fc.in_features, 6)
+checkpoint = torch.load(model_path, map_location='cpu')
+model.load_state_dict(checkpoint['model_state_dict'])
+model.eval()
+```
+
+### ONNX
+
+```python
+import onnxruntime as ort
+from huggingface_hub import hf_hub_download
+
+# Download ONNX model
+model_path = hf_hub_download(repo_id="Seif-melz/defect-classifier-resnet18", filename="model.onnx")
+
+# Load with ONNX Runtime
+session = ort.InferenceSession(model_path)
+```
+
+## Training
+
+See training history in `training_history.json` for detailed metrics.