Displaying 1 to 20 from 59 results

semantic-segmentation-pytorch - Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset

  •    Python

This is a PyTorch implementation of semantic segmentation models on MIT ADE20K scene parsing dataset. This module differs from the built-in PyTorch BatchNorm as the mean and standard-deviation are reduced across all devices during training. The importance of synchronized batch normalization in object detection has been recently proved with a an extensive analysis in the paper MegDet: A Large Mini-Batch Object Detector, and we empirically find that it is also important for segmentation.

labelme - Image Polygonal Annotation with Python (polygon, rectangle, line, point and image-level flag annotation)

  •    Python

Labelme is a graphical image annotation tool inspired by http://labelme.csail.mit.edu. It is written in Python and uses Qt for its graphical interface. Fig 2. VOC dataset example of instance segmentation.

semantic-segmentation-editor - Web labeling tool for bitmap images and point clouds

  •    Javascript

A web based labeling tool for creating AI training data sets (2D and 3D). The tool has been developed in the context of autonomous driving research. It supports images (.jpg or .png) and point clouds (.pcd). It is a Meteor app developed with React, Paper.js and three.js. (Optional) You can modify settings.json to customize classes data.

mmsegmentation - OpenMMLab Semantic Segmentation Toolbox and Benchmark.

  •    Python

MMSegmentation is an open source semantic segmentation toolbox based on PyTorch. It is a part of the OpenMMLab project. The master branch works with PyTorch 1.3+.

RandLA-Net - 🔥RandLA-Net in Tensorflow (CVPR 2020, Oral & IEEE TPAMI 2021)

  •    Python

This code has been tested with Python 3.5, Tensorflow 1.11, CUDA 9.0 and cuDNN 7.4.1 on Ubuntu 16.04. Update 03/21/2020, pre-trained models and results are available now. You can download the pre-trained models and results here. Note that, please specify the model path in the main function (e.g., main_S3DIS.py) if you want to use the pre-trained model and have a quick try of our RandLA-Net.

PaddleSeg - End-to-end image segmentation kit based on PaddlePaddle.

  •    Python

Welcome to PaddleSeg! PaddleSeg is an end-to-end image segmentation development kit developed based on PaddlePaddle, which covers a large number of high-quality segmentation models in different directions such as high-performance and lightweight. With the help of modular design, we provide two application methods: Configuration Drive and API Calling. So one can conveniently complete the entire image segmentation application from training to deployment through configuration calls or API calls. High Performance Model: Based on the high-performance backbone trained by Baidu's self-developed semi-supervised label knowledge distillation scheme (SSLD), combined with the state of the art segmentation technology, we provides 50+ high-quality pre-training models, which are better than other open source implementations.

TuSimple-DUC - Understanding Convolution for Semantic Segmentation

  •    Python

by Panqu Wang, Pengfei Chen, Ye Yuan, Ding Liu, Zehua Huang, Xiaodi Hou, and Garrison Cottrell. This repository is for Understanding Convolution for Semantic Segmentation (WACV 2018), which achieved state-of-the-art result on the CityScapes, PASCAL VOC 2012, and Kitti Road benchmark.

superpoint_graph - Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs

  •    Python

Point Cloud Oversegmentation with Graph-Structured Deep Metric Learning https://arxiv.org/pdf/1904.02113. To switch to the stable branch with only SPG, switch to release.

SegFormer - Official PyTorch implementation of SegFormer

  •    Python

Figure 1: Performance of SegFormer-B0 to SegFormer-B5. SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers. Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, and Ping Luo. Technical Report 2021.

crfasrnn_keras - CRF-RNN Keras/Tensorflow version

  •    Python

This repository contains Keras/Tensorflow code for the "CRF-RNN" semantic image segmentation method, published in the ICCV 2015 paper Conditional Random Fields as Recurrent Neural Networks. This paper was initially described in an arXiv tech report. The online demo of this project won the Best Demo Prize at ICCV 2015. Original Caffe-based code of this project can be found here. Results produced with this Keras/Tensorflow code are almost identical to that with the Caffe-based version. The root directory of the clone will be referred to as crfasrnn_keras hereafter.

semantic_slam - Real time semantic slam in ROS with a hand held RGB-D camera

  •    C++

Semantic SLAM can generate a 3D voxel based semantic map using only a hand held RGB-D camera (e.g. Asus xtion) in real time. We use ORB_SLAM2 as SLAM backend, a CNN (PSPNet) to produce semantic prediction and fuse semantic information into a octomap. Note that our system can also be configured to generate rgb octomap without semantic information. This project is released under a GPLv3 license.

LEDNet - LEDNet: A Lightweight Encoder-Decoder Network for Real-time Semantic Segmentation

  •    Python

This project contains the code (Note: The code is test in the environment with python=3.6, cuda=9.0, PyTorch-0.4.1, also support Pytorch-0.4.1+) for: LEDNet: A Lightweight Encoder-Decoder Network for Real-time Semantic Segmentation by Yu Wang. Clone this repository.

pytorch-segmentation - Pytorch for Segmentation

  •    Python

This repo has been deprecated currently and I will not maintain it. Meanwhile, I strongly recommend you can refer to my new repo: TorchSeg, which offers fast, modular reference implementation and easy training of semantic segmentation algorithms in PyTorch. A repository contains some exiting networks and some experimental networks for semantic segmentation.

We have large collection of open source products. Follow the tags from Tag Cloud >>

Open source products are scattered around the web. Please provide information about the open source projects you own / you use. Add Projects.