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U-Net Encoder-Decoder Architecture for Image Segmentation

U-Net is a fully-convolutional encoder-decoder architecture for image segmentation. A contracting encoder path progressively downsamples to capture context, a symmetric expanding decoder path upsamples to recover spatial resolution, and skip connections copy high-resolution encoder features into the decoder so that precise boundaries are preserved. Its U-shaped design makes it the standard backbone for biomedical and other dense pixel-labeling tasks, especially when training data are limited.

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image segmentation training using U-Net architecture in Python

U-Net architecture operates on a symmetric encoder-decoder framework designed for biomedical image segmentation by integrating spatial feature extraction with skip connections to preserve high-resolu…