image-labeling
There are 97 repositories under image-labeling topic.
HumanSignal/label-studio
Label Studio is a multi-type data labeling and annotation tool with standardized output format
cvat-ai/cvat
Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale.
HumanSignal/awesome-data-labeling
A curated list of awesome data labeling tools
googlesamples/mlkit
A collection of sample apps to demonstrate how to use Google's ML Kit APIs on Android and iOS
jsbroks/coco-annotator
:pencil2: Web-based image segmentation tool for object detection, localization, and keypoints
Hitachi-Automotive-And-Industry-Lab/semantic-segmentation-editor
Web labeling tool for bitmap images and point clouds
abreheret/PixelAnnotationTool
Annotate quickly images.
jenly1314/MLKit
🌝 MLKit是一个强大易用的工具包。通过ML Kit您可以很轻松的实现文字识别、条码识别、图像标记、人脸检测、对象检测等功能。
virajmavani/semi-auto-image-annotation-tool
Anno-Mage: A Semi Automatic Image Annotation Tool which helps you in annotating images by suggesting you annotations for 80 object classes using a pre-trained model
developer0hye/Yolo_Label
GUI for marking bounded boxes of objects in images for training neural network YOLO
HumanSignal/label-studio-frontend
Data labeling react app that is backend agnostic and can be embedded into your applications — distributed as an NPM package
shoumikchow/bbox-visualizer
Make drawing and labeling bounding boxes easy as cake
Slava/label-tool
Web application for image labeling and segmentation
phurwicz/hover
:speedboat: Label data at scale. Fun and precision included.
bit-bots/imagetagger
An open source online platform for collaborative image labeling
heylight/canvas-select
一个轻量级图片标注javascript库，支持矩形、多边形、点、折线、圆形，支持再编辑，使得图像标注更简单。
kili-technology/kili-python-sdk
Simplest and fastest image and text annotation tool.
lzx1413/LabelImgTool
LabelImgTool is a graphical image annotation tool which supports CLS,DET and SEG(semantic&instance )
mrousavy/vision-camera-image-labeler
VisionCamera Frame Processor Plugin to label images using MLKit Vision
sumn2u/annotate-lab
Annotate-lab is an open-source image annotation tool for efficient dataset creation. With an intuitive interface and flexible export options, it streamlines your machine learning workflow. 🖼️✏️📑
Nestak2/image-sorter2
One-click image sorting/labelling script
Teoge/MarkToolForParkingLotPoint
A tool for parking-slot labeling under surround-view image
buni-rock/Pixie
Pixie is a GUI annotation tool which provides the bounding box, polygon, free drawing and semantic segmentation object labelling
emadehsan/marsjs
MobileNet on TensorFlow.js - label images from Unsplash in browser
mfl28/BoundingBoxEditor
A JavaFX desktop application for creating image-object-annotations with bounding boxes.
amusi/awesome-data-label-tools
开源的标注工具大全（含2D图像/视频/3D点云等）
datagym-ai/datagym-core
Open source annotation and labeling tool for image and video assets
icanerdogan/Google-MLKit-Android-Apps
This repository contains a collection of Android applications developed using Google ML Kit, demonstrating the power and versatility of machine learning features in mobile development. Each project is crafted using Java and Kotlin, showcasing various use cases and practical implementations.
PrithivirajDamodaran/ZSIC
Zero Shot Image Classification but more, Supports Multilingual labelling and a variety of CNN based models for a vision backbone by using OpenAI CLIP for $ conscious uses (Super simple, so a 10th-grader could totally write this but since no 10th-grader did, I did) - Prithivi Da
pixano/pixano-app
Pixano App is a web-based smart-annotation tool for computer vision applications.
zxch3n/image-labeler-react
A react component that helps labeling images for object detection
pixano/pixano-elements
Pixano Elements - Re-usable web components dedicated to data annotation tasks.
faustomorales/qsl
A quick and simple tool for labeling images, videos and time series data, right from Jupyter!
younesZdDz/react-bbox-annotator
A bounding box annotation component written for React.
robertarvind/Interactive-Semi-Automatic-Image-2D-Bounding-Box-Annotation-Tool-using-Multi-Template_Matching
Interactive Semi Automatic Image 2D Bounding Box Annotation and Labelling Tool using Multi Template Matching An Interactive Semi Automatic Image 2D Bounding Box Annotation/Labelling Tool to aid the Annotater/User to rapidly create 2D Bounding Box Single Object Detection masks for large number of training images in a semi automatic manner in order to train an object detection deep neural network such as Mask R-CNN or U-Net. As the Annotater/User starts annotating/labelling by drawing a bounding box for a few number of images in the selected folder then the algorithm suggests bounding box predictions for the rest of the yet to be annotated/labelled images in the folder. If the predictions are right then the user/annotater can simply press the keyboard key 'y' which indicates that the detected bounding box is correct. If the prediction is wrong then the user/annotater can manually draw a rectangular 2D bounding box over the correct ROI (Region of interest) in the image and then press the key 'y' to proceed further to the rest of the images in the folder. If the user/annotater made a mistake while drawing the 2D bounding box, then he/she can press the key 'n' in order to remove the incorrectly marked 2D bounding box and he/she can repeat the process for the same image until he/she draws the correct 2D bounding box and then after drawing the correct 2D bounding box, the user/annotater may press the key 'y' to continue to the rest of the images. The 2D bounding box prediction over the whole image data set improves as the user/annotater annotates/labels more number of images by drawing 2D bounding boxes. This tool allows the user/annotater to not only interactively and rapidly annotate large number of images but also to validate the predictions at the same time interactively. This tool helps the user/annotater to save a lot of time when annotating/labelling and validating the predictions for a large number of training images in a folder. Instructions to use:- 1. If the training images are in JPEG or any other format, then convert them to PNG format using some other tool or program before using these images for annotation. 2. All the training images must contain the object of interest which is to be annotated. 3. Currently the application only supports 2D bounding box annotation for single object detection per image, but in the future semantic segmentation based annotation features will be added which will allow precise boundary segmentation masks of an object in an image. 4. If some or all of the training images have varying dimensions(shapes/resolutions), then resize them to the same dimensions using this tool by providing the height and width to which all the training images need to be resized to. The height and width are inputed separately in two different dialog boxes which pop up once the program is executed. If the training images need not be resized then press the cancel button in the dialog boxes requesting the height and width. 5. Select the folder containing the training images by navigating to the folder containing the training images through a dialog box which pops up after the program is executed. If the images need to be resized then two dialog boxes pop up. The first dialog box is to navigate to the destination folder containing the unresized raw training images and after resizing another dialog box pops up to navigate to the folder containing the saved resized training images named as "resized_data". If the images need not be resized then only one dialog box pops up so that the user can navigate to the raw training images folder directly. 6. The images in the folder pop up one by one. After drawing the correct 2D bounding box over the ROI (region of Interest), press the 'y' key. Except the first image, the rest of the images will have a 2D bounding box drawn over them. If the predicted box is accurate, then continue by pressing the 'y' key. If the prediction is incorrect, then draw the accurate bounding box and press the 'y' key. If any mistake occured while drawing the 2D box, then reset the image by removing the incorrect drawing by pressing the 'n' key and then draw the correct box and press the 'y' key. 7. The output images are stored in four different folders in the same directory containing the training images folder. among the four folders, one contains the cropped templates of the bounding boxes, black and white mask images, training images and the images with 2D box detection markings.
cuixing158/imageLabeler-API
Convenient image annotation tool API