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albumentations - fast image augmentation library and easy to use wrapper around other libraries
You can use this Google Colaboratory notebook to adjust image augmentation parameters and see the resulting images.
image-augmentation machine-learning augmentation deep-learning detection fast-augmentations segmentation image-segmentation image-processing image-classification object-detectionuniversal-data-tool - Collaborate & label any type of data, images, text, or documents, in an easy web interface or desktop app
Try it out at udt.dev, download the desktop app or run on-premise. The Universal Data Tool is a web/desktop app for editing and annotating images, text, audio, documents and to view and edit any data defined in the extensible .udt.json and .udt.csv standard.
machine-learning csv computer-vision deep-learning image-annotation desktop dataset named-entity-recognition classification labeling image-segmentation hacktoberfest semantic-segmentation annotation-tool text-annotation labeling-tool entity-recognition annotate-images image-labeling-tool text-labelingPaddleSeg - End-to-end image segmentation kit based on PaddlePaddle.
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.
image-segmentation unet semantic-segmentation pspnet icnet deeplabv3 hrnetECCV2020-Code - ECCV 2020 论文开源项目合集，同时欢迎各位大佬提交issue，分享ECCV 2020开源项目
ECCV 2020 论文开源项目合集，同时欢迎各位大佬提交issue，分享ECCV 2020开源项目
computer-vision deep-learning neural-network image-classification object-detection image-segmentation eccv eccv2020 eccv-2020catalyst - Accelerated deep learning R&D
Accelerated deep learning R&D
infrastructure machine-learning natural-language-processing information-retrieval research reinforcement-learning computer-vision deep-learning text-classification distributed-computing image-processing pytorch image-classification metric-learning recommender-system object-detection image-segmentation reproducibility text-segmentationLabelbox - The most versatile data labeling platform for training expert AI.
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.
image-classification image-segmentation computer-vision tensorflow labeling annotations deep-learning recognition tools image-annotationTernausNet - UNet model with VGG11 encoder pre-trained on Kaggle Carvana dataset
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.
pytorch image-segmentationEfficient-Segmentation-Networks - Lightweight models for real-time semantic segmentationon PyTorch (include SQNet, LinkNet, SegNet, UNet, ENet, ERFNet, EDANet, ESPNet, ESPNetv2, LEDNet, ESNet, FSSNet, CGNet, DABNet, Fast-SCNN, ContextNet, FPENet, etc
This project aims at providing an easy-to-use, modifiable reference implementation for real-time semantic segmentation models using PyTorch. PyTorch and Torchvision needs to be installed before running the scripts, PyTorch v1.1 or later is supported.
computer-vision pytorch neural-networks segmentation image-segmentation semantic-segmentation cityscapes scene-understanding semantic-segmentation-models camvid real-time-semantic-segmentation efficient-segmentation-networks lightweight-semantic-segmentation driving-scene-understandingSurface-Defect-Detection - 📈 Constantly summarizing open source dataset and critical papers in the field of surface defect research which are of great importance
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).
deep-learning paper dataset defects image-segmentation surface surface-defects surface-detection pcb-surface-defect surface-defect-detection charmvekeras-rcnn - Keras package for region-based convolutional neural networks (RCNNs)
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.
deep-learning theano tensorflow cntk object-detection image-segmentationImageSegmentation - Perform image segmentation and background removal in javascript using superpixes
Image Eraser allows users to perform image segmentation inside browser using a vector editor (FabricJS) and JS implementations of superpixel algorithms.
image-segmentation superpixel-algorithms computer-visioncrfasrnn_keras - CRF-RNN Keras/Tensorflow version
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.
image-segmentation semantic-segmentation crf-as-rnn tensorflow keras crfasrnn crfasrnn-keras crfasrnn-tensorflow crf-rnn-tensorflow crf-rnn-kerasjs-segment-annotator - Javascript image annotation tool based on image segmentation.
Javascript image annotation tool based on image segmentation. A browser must support HTML canvas to use this tool.
image-segmentation javascript-image-annotationu-net - U-Net: Convolutional Networks for Biomedical Image Segmentation
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.
image-segmentation keras medical-imaging deep-neural-networks convolutional-networksrobot-surgery-segmentation - Wining solution and its improvement for MICCAI 2017 Robotic Instrument Segmentation Sub-Challenge
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.
medical-imaging robot-assisted-surgery computer-vision image-segmentation deep-learning pytorchTernausNetV2 - TernausNetV2: Fully Convolutional Network for Instance Segmentation
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.
satellite-imagery computer-vision image-segmentation deep-learning pytorchseg-torch - Segmentation with deep learning
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.
image-segmentation torch segmentation deep-learning seg-torchnifty - A nifty library for graph based image segmentation.
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.
segmentation image-segmentationImageSeg-KMeans - 💠 Image Segmentation using K-Means
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.
kmeans-clustering image-segmentation
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