Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.

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

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

3クラス(肌、服、髪)のセマンティックセグメンテーションを実施するモデル(A model that performs semantic segmentation of 3 classes(skin, clothes, hair))

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

Undergraduate Thesis : an improved Deeplabv3+ algorithm

Point cloud painting with semantic labels

DeepLabV3+ based network for fake faces detection

A multi task neural network implemented from scratch, performing object detection with SSD and semantic segmentation with DeeplabV3+ simultaneosly!

DeepLab v3+ model in PyTorch. Support different backbones.

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)

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

Semantic segmentation with railsem19 conducted in advance to carry out detecting railway-related objects performed by the KRRI.

totally failed project

This GitHub repository hosts a project focused on the detection of parked cars in the city of Granada through advanced image segmentation techniques.

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

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.