Segmentation models with pretrained backbones. PyTorch.

A DeepLab V3+ Model with choice of Encoder for Binary Segmentation. Implemented with Tensorflow.

Image segmentation implemented using pytorch on a COCO format Dataset of Ingredients with various models including U-NET, U-NET++, SegNet and DeepLabV3+

A DeepLab V3+ Model with ResNet 50 Encoder to perform Binary Segmentation Tasks. Implemented with PyTorch.

Point cloud painting with semantic labels

Base on tensorrt version 8.2.4, compare inference speed for different tensorrt api.

A guide to use MMSegmentation to develop and benchmark different models on custom dataset.

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.

Run inference on binary (2 classes only) Deeplab model cropping large images in a sliding window approach.

An implementation of Deeplabv3plus in TensorFlow2 for semantic land cover segmentation

[CVPR2020] f-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation https://arxiv.org/abs/2001.10331

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

Semantic Segmentation for Urban Scene understanding - Cityscapes dataset

A semantic segmentation for a human parsing task in Tensorflow Python

In this project, I developed and trained a model that uses the Deep Lab V3 Plus architecture for image segmentation — trained particularly on human figures (faced, bodies, et cetera). The model as well as the code to run the model has been provided.

HistoSeg is an Encoder-Decoder DCNN which utilizes the novel Quick Attention Modules and Multi Loss function to generate segmentation masks from histopathological images with greater accuracy. This repo contains the code to Test and Train the HistoSeg

Enhanced Image Segmentation with Iterative Image Inpainting

Implementation of a Deep Neural Architecture to perform real-time semantic segmentation of forest fires in aerial imagery captured by drones.

Dockerized Semantic Image Segmentation service using Tensorflow, Keras, and DeepLabv3+