A Keras port of Single Shot MultiBox Detector

PyTorch Implementation of Fully Convolutional Networks. (Training code to reproduce the original result is available.)

Tensorflow implementation of Fully Convolutional Networks for Semantic Segmentation (http://fcn.berkeleyvision.org)

A Kitti Road Segmentation model implemented in tensorflow.

PytorchAutoDrive: Segmentation models (ERFNet, ENet, DeepLab, FCN...) and Lane detection models (SCNN, RESA, LSTR, LaneATT, BézierLaneNet...) based on PyTorch with fast training, visualization, benchmarking & deployment help

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Tensorflow implementation of Automatic Portrait Matting on paper "Automatic Portrait Segmentation for Image Stylization"

Pixel-wise segmentation on VOC2012 dataset using pytorch.

CVPR2022 (Oral) - Rethinking Semantic Segmentation: A Prototype View

ResUNet, a semantic segmentation model inspired by the deep residual learning and UNet. An architecture that take advantages from both(Residual and UNet) models.

Chainer Implementation of Fully Convolutional Networks. (Training code to reproduce the original result is available.)

PyTorch Implementation of Fully Convolutional Networks (a very simple and easy demo).

Implemention of FCN-8 and FCN-16 with Keras and uses CRF as post processing

Get started with Semantic Segmentation based on Keras, including FCN32/FCN8/SegNet/U-Net

A Python Library for High-Level Semantic Segmentation Models based on TensorFlow and Keras with pretrained backbones.

This repository contains the code of HyperDenseNet, a hyper-densely connected CNN to segment medical images in multi-modal image scenarios.

UNet is a fully convolutional network(FCN) that does image segmentation. Its goal is to predict each pixel's class. It is built upon the FCN and modified in a way that it yields better segmentation in medical imaging.

The code includes all the file that you need in the training stage for FCN

Tensorflow implementation : U-net and FCN with global convolution

Edge-aware U-Net with CRF-RNN layer for Medical Image Segmentation