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I propose adding a Semantic Segmentation Module utilizing the U-Net architecture. This feature will enable pixel-wise segmentation, enhancing our repository's capabilities for applications in various domains like fashion and medical imaging.
Key Components:
U-Net Implementation: Integrate the U-Net architecture for efficient segmentation.
Data Handling: Leverage the existing VITON dataset class for seamless image and mask loading.
Training Pipeline: Utilize the train_fn function for model training, incorporating mixed precision and loss logging.
Evaluation Metrics: Include functions like check_accuracy for model performance monitoring.
Benefits:
Expands the functionality for users interested in semantic segmentation.
Provides a quick setup for training and evaluation on custom datasets.
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I propose adding a Semantic Segmentation Module utilizing the U-Net architecture. This feature will enable pixel-wise segmentation, enhancing our repository's capabilities for applications in various domains like fashion and medical imaging.
Key Components:
U-Net Implementation: Integrate the U-Net architecture for efficient segmentation. Data Handling: Leverage the existing VITON dataset class for seamless image and mask loading. Training Pipeline: Utilize the train_fn function for model training, incorporating mixed precision and loss logging. Evaluation Metrics: Include functions like check_accuracy for model performance monitoring. Benefits:
Expands the functionality for users interested in semantic segmentation. Provides a quick setup for training and evaluation on custom datasets.