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.
image-annotation computer-vision annotations deep-learning semantic-segmentation instance-segmentation video-annotation classificationShareX is a free and open source program that lets you capture or record any area of your screen and share it with a single press of a key. It also allows uploading images, text or other types of files to over 50 supported destinations you can choose from.
screen-capture screen-recorder file-sharing file-upload url-shortener color-picker region-capture imgur dropbox gif gif-recorder ocr image-annotation ftp share screenshotTry 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-labelingLabelbox 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-annotationCVAT is completely re-designed and re-implemented version of Video Annotation Tool from Irvine, California tool. It is free, online, interactive video and image annotation tool for computer vision. It is being used by our team to annotate million of objects with different properties. Many UI and UX decisions are based on feedbacks from professional data annotation team. Code released under the MIT License.
video-annotation computer-vision computer-vision-annotation deep-learning image-annotation annotation-tool annotation labeling labeling-tool image-labeling image-labelling-tool bounding-boxes boundingbox image-classification annotations imagenet detection recognition tensorflowThis project satisfies the requirements for both the Advanced Lane Finding project and the Vehicle Detection project for Udacity's Self-Driving Car Engineer nanodegree. Primary goals include detecting the lane lines, determining the curvature of the lane as well as the car's position within the lane, and detecting other vehicles. I chose to use convolutional neural networks to detect lane lines and cars, rather than the gradient and SVM-based approaches recommended for these projects. I annotated training images with the correct answers by adding extra layers to indicate which parts of the picture were part of lane lines or cars, then trained convolutional neural networks to produce such image masks for other images from the video. The process of curating training data and training convolutional neural networks will be discussed further later in this document.
udacity-self-driving-car lane-detection convolutional-neural-networks image-annotation image-maskSemi Automatic Image Annotation Toolbox with RetinaNet as the suggesting algorithm. The toolbox suggests 80 class objects from the MS COCO dataset using a pretrained RetinaNet model. Clone this repository.
deep-learning tensorflow keras image-annotation image-labeling automationVIAME is a computer vision application designed for do-it-yourself artificial intelligence including object detection, object tracking, image/video annotation, image/video search, image mosaicing, size measurement, rapid model generation, and tools for the evaluation of different algorithms. Originally targetting marine species analytics, it now contains many common algorithms and libraries, and is also useful as a generic computer vision toolkit. It contains a number of standalone tools for accomplishing the above, a pipeline framework which can connect C/C++, python, and matlab nodes together in a multi-threaded fashion, and, lastly, multiple algorithms resting on top of the pipeline infrastructure. Both a desktop and web version exist for deployments in different types of environments. For a full installation guide and description of the various flavors of VIAME, see the quick-start guide, above. The desktop version is provided as either a .msi, .zip or .tar file. Alternatively, docker files are available for both VIAME Desktop and Web (below). A sample instance of VIAME Web is also online, hosted at viame.kitware.com. For desktop installs, extract the binaries (or use the msi Windows installation wizard) and place them in a directory of your choosing, for example /opt/noaa/viame on Linux or C:\Program Files\VIAME on Windows. If using packages built with GPU support, make sure to have sufficient video drivers installed, version 451.82 or higher. The best way to install drivers depends on your operating system, see below. Lastly, run through some of the examples to validate the installation. The binaries are quite large, in terms of disk space, due to the inclusion of multiple default model files and programs, but if just building your desired features from source (e.g. for embedded apps) they are much smaller.
open-source machine-learning computer-vision image-annotation video-annotation image-processing artificial-intelligence oceanography annotation-framework video-search marine-biology video-analyticsData Annotator for Machine Learning (DAML) is an application that helps machine learning teams facilitating the creation and management of annotations. DAML project team welcomes contributions from the community. For more detailed information, see CONTRIBUTING.md.
deep-learning image-annotation annotation named-entity-recognition annotation-tool text-annotation tabular labeling-toolAdditionally, it is possible to connect a pre-trained or a custom-trained model to the LabelTool lite. This functionality allows one to accelerate the labeling process whereby the connected model can be actively used to suggest appropriate labels for each image. We provide a sample dataset in case you don't have your own custom dataset.
docker computer-vision deep-learning image-annotation neural-network tensorflow label inference object-detection boundingbox voc synthetic-data labeling-tool annotaion bounding-box labeltool auto-label yolov4 autolabeling smart-labeling
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