Скачать или смотреть 8 Image Segmentation UNET

Image segmentation, a fundamental task in computer vision that requires assigning a class label to every pixel in an image, contrasting with simpler tasks like classification or object detection. Since standard convolutional networks lose necessary spatial information, specialized architectures are introduced. Among these, the U-Net architecture is highlighted for semantic segmentation, particularly in biomedical images.

U-Net features a U-shaped design with an encoder that reduces spatial resolution while extracting high-level features, a bottleneck that captures the most compressed representation, and a decoder that progressively restores the resolution to generate the segmented mask. The critical element of U-Net is the use of skip connections, which efficiently transfer fine spatial details from the encoder blocks directly to the decoder blocks, thereby enabling the network to combine global contextual information with local precision for accurate segmentation, especially of fine structures and edges. A practical example using the Oxford-IIIT Pet dataset demonstrates the effectiveness of U-Net, confirming its solid performance even when trained with a reduced dataset.