awesome-semantic-segmentation

TF

, , ,

TF

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Keras

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Matlab

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Matlab

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FCN with VGG(Resnet, Densenet) backbone:

pytorch

The easiest implementation of fully convolutional networks (FCN8s VGG):

TernausNet (UNet model with VGG11 encoder pre-trained on Kaggle Carvana dataset :

pytorch

TernausNetV2: Fully Convolutional Network for Instance Segmentation:

A 2021 guide to Semantic Segmentation ( )

Evolution of Image Segmentation using Deep Convolutional Neural Network: A Survey 2020 ( )

A peek of Semantic Segmentation 2018 ( )

Semantic Segmentation guide 2018 ( )

An overview of semantic image segmentation ( )

Recent progress in semantic image segmentation 2018 ( , )

A 2017 Guide to Semantic Segmentation Deep Learning Review ( )

Review popular network architecture ( )

Lecture 11 - Detection and Segmentation - CS231n ( , ):

A Survey of Semantic Segmentation 2016 ( )

Dstl Satellite Imagery Competition, 3rd Place Winners' Interview: Vladimir & Sergey: ,

Carvana Image Masking Challenge–1st Place Winner's Interview: ,

Data Science Bowl 2017, Predicting Lung Cancer: Solution Write-up, Team Deep Breath:

Code and explain

MICCAI 2017 Robotic Instrument Segmentation:

2018 Data Science Bowl Find the nuclei in divergent images to advance medical discovery: , , , , ,

Airbus Ship Detection Challenge: ,

iMaterialist (Fashion) 2020 at FGVC7:

Understanding Clouds from Satellite Images: , ,

Global Wheat Detection: , ,

Severstal: Steel Defect Detection: , ,

Human Protein Atlas Image Classification: ,

: 400+ classes of real-world data

: 164k images, 72 classes: 80 thing classes, 91 stuff classes and 1 class 'unlabeled'

: This dataset consists of segmentation ground truths for roads, lanes, vehicles and objects on road. The dataset contains 30 classes and of 50 cities collected over different environmental and weather conditions

: 20k+ images

Semantic Segmentation in PyTorch

(by yassouali):

Semantic Segmentation Suite

(by George Seif):

Segmentation Training Pipeline

:

PaddlePaddle/PaddleSeg

: Easy-to-use image segmentation library with awesome pre-trained model zoo, supporting wide-range of practical tasks in Semantic Segmentation, Interactive Segmentation, Panoptic Segmentation, Image Matting, 3D Segmentation, etc

(Deconvolution, Upconvolution)