Displaying 1 to 10 from 10 results

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.

ShareX - Screen capture, file sharing and productivity tool

  •    CSharp

ShareX 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.

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.

cvat - Computer Vision Annotation Tool (CVAT) is a web-based tool which helps to annotate video and images for Computer Vision algorithms

  •    Javascript

CVAT 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.

CarND-Detect-Lane-Lines-And-Vehicles - Use segmentation networks to recognize lane lines and vehicles

  •    Python

This 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.

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

  •    Python

Semi 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.

VIAME - Video and Image Analytics for Marine Environments

  •    C++

VIAME 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.

data-annotator-for-machine-learning - Data annotator for machine learning allows you to centrally create, manage and administer annotation projects for machine learning

  •    TypeScript

Data 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.

BMW-Labeltool-Lite - This repository provides you with an easy-to-use labeling tool for State-of-the-art Deep Learning training purposes

  •    CSharp

Additionally, 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.

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