Pretrained DeepLabv3 and DeepLabv3+ for Pascal VOC & Cityscapes

DeepLabv3+ built in TensorFlow

LightNet: Light-weight Networks for Semantic Image Segmentation (Cityscapes and Mapillary Vistas Dataset)

Support PointRend, Fast_SCNN, HRNet, Deeplabv3_plus(xception, resnet, mobilenet), ContextNet, FPENet, DABNet, EdaNet, ENet, Espnetv2, RefineNet, UNet, DANet, HRNet, DFANet, HardNet, LedNet, OCNet, EncNet, DuNet, CGNet, CCNet, BiSeNet, PSPNet, ICNet, FCN, deeplab)

A semantic segmentation toolbox based on PyTorch

mIOU=80.02 on cityscapes. My implementation of deeplabv3+ (also know as 'Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation' based on the dataset of cityscapes).

deeplabv3plus (Google's new algorithm for semantic segmentation) in keras：Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

The remote sensing image semantic segmentation repository based on tf.keras includes backbone networks such as resnet, densenet, mobilenet, and segmentation networks such as deeplabv3+, pspnet, panet, and refinenet.

All version of deeplab implemented in Pytorch

In this program, we are using image segmentation to remove the background from photos.

Human segmentation project(pytorch)

DeepLabV3Plus for Beginners in Cityscapes Dataset

Tensorflow-Keras semantic segmentation

In search of effective and efficient Pipeline for Distillating Knowledge in Convolutional Neural Networks

PyTorch Implementation of Semantic Segmentation CNNs: This repository features key architectures like UNet, DeepLabv3+, SegNet, FCN, and PSPNet. It's crafted to provide a solid foundation for Semantic Segmentation tasks using PyTorch.

Monocular Depth Estimation via a Fully Convolutional Deep Neural Network, utilising Atrous Convolutions, with 3D Point Cloud Visualisation.

A Tensorflow implementation of Deeplabv3+ trained on VOC2012.

I am aiming to write different Semantic Segmentation models from scratch with different pretrained backbones.

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