This model is trained for ice hockey player orientation classification, 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
Extract frames from a video using OpenCV.
Detect player bounding boxes with a YOLO model.
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].
Classify the direction of each cropped player image using the SqueezeNet model:
