Displaying 1 to 20 from 49 results

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.




Labelbox - The most versatile data labeling platform for training expert AI.

  •    TypeScript

Labelbox is a data labeling tool that's purpose built for machine learning applications. Start labeling data in minutes using pre-made labeling interfaces, or create your own pluggable interface to suit the needs of your data labeling task. Labelbox is lightweight for single users or small teams and scales up to support large teams and massive data sets. Simple image labeling: Labelbox makes it quick and easy to do basic image classification or segmentation tasks. To get started, simply upload your data or a CSV file containing URLs pointing to your data hosted on a server, select a labeling interface, (optional) invite collaborators and start labeling.

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

  •    Jupyter

TernausNet is a modification of the celebrated UNet architecture that is widely used for binary Image Segmentation. For more details, please refer to our arXiv paper. This architecture was a part of the winning solutiuon (1st out of 735 teams) in the Carvana Image Masking Challenge.


Surface-Defect-Detection - 📈 Constantly summarizing open source dataset and critical papers in the field of surface defect research which are of great importance

  •    Python

At present, surface defect equipment based on machine vision has widely replaced artificial visual inspection in various industrial fields, including 3C, automobiles, home appliances, machinery manufacturing, semiconductors and electronics, chemical, pharmaceutical, aerospace, light industry and other industries. Traditional surface defect detection methods based on machine vision often use conventional image processing algorithms or artificially designed features plus classifiers. Generally speaking, imaging schemes are usually designed by using the different properties of the inspected surface or defects. A reasonable imaging scheme helps to obtain images with uniform illumination and clearly reflect the surface defects of the object. In recent years, many defect detection methods based on deep learning have also been widely used in various industrial scenarios. Compared with the clear classification, detection and segmentation tasks in computer vision, the requirements for defect detection are very general. In fact, its requirements can be divided into three different levels: "what is the defect" (classification), "where is the defect" (positioning) And "How many defects are" (split).

keras-rcnn - Keras package for region-based convolutional neural networks (RCNNs)

  •    Python

keras-rcnn is the Keras package for region-based convolutional neural networks. The data is made up of a list of dictionaries corresponding to images.

ImageSegmentation - Perform image segmentation and background removal in javascript using superpixes

  •    Javascript

Image Eraser allows users to perform image segmentation inside browser using a vector editor (FabricJS) and JS implementations of superpixel algorithms.

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.

js-segment-annotator - Javascript image annotation tool based on image segmentation.

  •    Javascript

Javascript image annotation tool based on image segmentation. A browser must support HTML canvas to use this tool.

u-net - U-Net: Convolutional Networks for Biomedical Image Segmentation

  •    Python

This tutorial shows how to use Keras library to build deep neural network for ultrasound image nerve segmentation. More info on this Kaggle competition can be found on https://www.kaggle.com/c/ultrasound-nerve-segmentation. This deep neural network achieves ~0.57 score on the leaderboard based on test images, and can be a good staring point for further, more serious approaches.

robot-surgery-segmentation - Wining solution and its improvement for MICCAI 2017 Robotic Instrument Segmentation Sub-Challenge

  •    Jupyter

Here we present our wining solution and its improvement for MICCAI 2017 Robotic Instrument Segmentation Sub-Challenge. In this work, we describe our winning solution for MICCAI 2017 Endoscopic Vision Sub-Challenge: Robotic Instrument Segmentation and demonstrate further improvement over that result. Our approach is originally based on U-Net network architecture that we improved using state-of-the-art semantic segmentation neural networks known as LinkNet and TernausNet. Our results shows superior performance for a binary as well as for multi-class robotic instrument segmentation. We believe that our methods can lay a good foundation for the tracking and pose estimation in the vicinity of surgical scenes.

TernausNetV2 - TernausNetV2: Fully Convolutional Network for Instance Segmentation

  •    Jupyter

We present network definition and weights for our second place solution in CVPR 2018 DeepGlobe Building Extraction Challenge. Automatic building detection in urban areas is an important task that creates new opportunities for large scale urban planning and population monitoring. In a CVPR 2018 Deepglobe Building Extraction Challenge participants were asked to create algorithms that would be able to perform binary instance segmentation of the building footprints from satellite imagery. Our team finished second and in this work we share the description of our approach, network weights and code that is sufficient for inference.

seg-torch - Segmentation with deep learning

  •    Lua

However this code includes radical differences (such as data loading, augmentation, memory optimization) and it has more generic type of implementation suitable for use in any custom project. You only need to modify data-loader files data/custom-gen.lua and data/custom.lua. Be warned this is susceptible to bugs. Any pull request is appreciated.

nifty - A nifty library for graph based image segmentation.

  •    C++

A nifty library for 2D and 3D image segmentation, graph based segmentation an opt. This library provided building blocks for segmentation algorithms and complex segmentation pipelines. The core is implemented in C++ but the suggested language to use this library from is python. A very tentative documentation of the nifty python module.

ImageSeg-KMeans - 💠 Image Segmentation using K-Means

  •    Python

The program reads in an image, segments it using K-Means clustering and outputs the segmented image. It is worth playing with the number of iterations, low numbers will run quicker.