A PyTorch implementation of U-Net architecture to perform pixel-level semantic segmentation on the Football Scene Dataset. Includes custom mask preprocessing, robust data augmentation with Albumentations, and fine-tuning with Class Weighted CrossEntropyLoss to resolve data imbalance for small object detection.
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A PyTorch implementation of U-Net architecture to perform pixel-level semantic segmentation on the Football Scene Dataset. Includes custom mask preprocessing, robust data augmentation with Albumentations, and fine-tuning with Class Weighted CrossEntropyLoss to resolve data imbalance for small object detection.
emseekim/UNet-Semantic-Segmentation-Football
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A PyTorch implementation of U-Net architecture to perform pixel-level semantic segmentation on the Football Scene Dataset. Includes custom mask preprocessing, robust data augmentation with Albumentations, and fine-tuning with Class Weighted CrossEntropyLoss to resolve data imbalance for small object detection.
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