《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

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

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

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

U-Net Biomedical Image Segmentation

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

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

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.