U-Net is a powerful CNN architecture for semantic segmentation that uses a U-shaped structure of encoder-decoder paths with skip connections to enable precise pixel-wise classification. Originally des
igned for biomedical imaging, it can be implemented using PyTorch and requires proper dataset preparation, model definition, and training optimization.
Reasons to Read -- Learn:
how to implement a complete U-Net architecture in PyTorch, including detailed code for model definition and training loop setup.
step-by-step process of preparing and structuring your dataset for image segmentation tasks, including proper organization of images and masks into train/val/test splits.
practical optimization techniques for U-Net training, including learning rate scheduling, data augmentation strategies, and evaluation metrics like Dice coefficient and IoU.
publisher: @priyanshu011109
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