Update README.md

README.md

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-license: mit
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+---
+license: mit
+---
+
+# PlayerDirection SqueezeNet Model
+
+## Overview
+This model is trained for ice hockey player orientation detection, classifying cropped player images into one of eight orientations: Top, Top-Right, Right, Bottom-Right, Bottom, Bottom-Left, Left, and Top-Left. It is based on the SqueezeNet architecture and achieves an F1 score of **75%**.
+
+## Model Details
+- **Architecture**: SqueezeNet (modified for 8-class classification).
+- **Training Configuration**:
+  - Learning rate: 1e-4
+  - Batch size: 24
+  - Epochs: 300
+  - Weight decay: 1e-4
+  - Dropout: 0.3
+  - Early stopping: patience = 50
+  - Augmentations: Color jitter (no rotation)
+- **Performance**:
+  - Accuracy: ~75%
+  - F1 Score: ~75%
+
+## Usage
+1. Extract frames from a video using OpenCV.
+2. Detect player bounding boxes with a YOLO model.
+3. Crop player images, resize them to 224x224, and preprocess with the given PyTorch transformations:
+   - Resize to (224, 224)
+   - Normalize with mean=[0.485, 0.456, 0.406] and std=[0.229, 0.224, 0.225].
+4. Classify the direction of each cropped player image using the SqueezeNet model:
+   ```python
+   with torch.no_grad():
+       output = model(image_tensor)
+       direction_class = torch.argmax(output, dim=1).item()
+