《深度学习与计算机视觉》配套代码

The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.

Real-Time Semantic Segmentation in Mobile device

A U-Net combined with a variational auto-encoder that is able to learn conditional distributions over semantic segmentations.

Pytorch implementation of Graph U-Nets (ICML19)

Programming assignments and quizzes from all courses within the GANs specialization offered by deeplearning.ai

[ICCVW 2025] SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentation

Official Implementation of the paper "A U-Net Based Discriminator for Generative Adversarial Networks" (CVPR 2020)

Using a U-Net for image segmentation, blending predicted patches smoothly is a must to please the human eye.

Winning solution for the Kaggle TGS Salt Identification Challenge.

Helper package with multiple U-Net implementations in Keras as well as useful utility tools helpful when working with image semantic segmentation tasks. This library and underlying tools come from multiple projects I performed working on semantic segmentation tasks

Official repo for Medical Image Segmentation Review: The Success of U-Net

U-Net Biomedical Image Segmentation

Python library for designing and training your own Diffusion Models with PyTorch.

Implementation of a U-net complete with efficient attention as well as the latest research findings

Deep Learning sample programs using PyTorch in C++

Official implementation of DoubleU-Net for Semantic Image Segmentation in TensorFlow & Pytorch (Nominated for Best Paper Award (IEEE CBMS))

Attention-Guided Version of 2D UNet for Automatic Brain Tumor Segmentation

Code for "Quantized Densely Connected U-Nets for Efficient Landmark Localization" (ECCV 2018) and "CU-Net: Coupled U-Nets" (BMVC 2018 oral)

Manage your machine learning experiments with trixi - modular, reproducible, high fashion. An experiment infrastructure optimized for PyTorch, but flexible enough to work for your framework and your tastes.