The Tensorflow, Keras implementation of U-net, V-net, U-net++, UNET 3+, Attention U-net, R2U-net, ResUnet-a, U^2-Net, TransUNET, and Swin-UNET with optional ImageNet-trained backbones.

Pytorch implementation of ResUnet and ResUnet ++

Official code for ResUNetplusplus for medical image segmentation (TensorFlow & Pytorch implementation)

Official implementation of ResUNet++, CRF, and TTA for segmentation of medical images (IEEE JBIHI)

Label-Pixels is the tool for semantic segmentation of remote sensing images using Fully Convolutional Networks. Initially, it is designed for extracting the road network from remote sensing imagery and now, it can be used to extract different features from remote sensing imagery.

Implementation of the paper "ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data" in TensorFlow.

PyTorch implementation of medical semantic segmentations models, e.g. UNet, UNet++, DUCKNet, ResUNet, ResUNet++, and support knowledge distillation, distributed training, Optuna etc.

Brain Tumor Segmentation And Classification using artificial intelligence

Leibniz is a python package which provide facilities to express learnable partial differential equations with PyTorch

Step by Step ResUnet Model Architecture using Keras

An open-source UNet-based pipeline for nuclei segmentation in histopathology images using the PanNuke dataset. It features an interactive web app for easy data visualization and handling, making AI tools accessible even for non-experts. This project provides a foundation for training and exploring histopathology data.

Implements Deep Residual U-Net network.

Comprehensive Machine Learning Benchmarking for Fringe Projection Profilometry with Photorealistic Synthetic Data

Implementation of ResUnet++ using Tensorflow 2.0.

PyTorch Implementation of ResUnet++

GSNet implementation ported to a more modern stack, with additional options for backbone feature extractors and stability-aware reweighting from AnyGrasp.

Deep learning models to estimate the masses of galaxy clusters from lensed CMB maps