LabelLab is an image analysis and classification platform for Machine Learning researchers. Originally built to classify images of elephants, it has been developed over the years and is now a full-fledged ML utility tool that is able to classify all kinds of images and carry out a number of ML-related tasks in a project-based manner while teaming up with other researchers.
- The user can create a project and add other users as collaborators.
- Inside a project, multiple actions can be performed such as -
- Uploading images in batches
- Creating new labels
- Labelling the uploaded images
- Creating and training different kinds of models
- Using the image path tracking feature on labeled images
- Each collaborator can be assigned one or more roles such as images, labels, models, admin, etc which will allow that collaborator to perform tasks specific to that role.
- On basis of their roles, the collaborators are divided into various teams.
- Each team has a chat system to discuss the assigned tasks and progress on the project.
- The project also has an activity tracking system recording all progress in it, which can be filtered by team, project member, category, and entity.
- Besides that, the user can also upload images outside a project and run classifications against a trained model.
See the LICENSE file for details.
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To learn more about the project, check out the Project Wiki
Please go though our Code of Conduct first.
Follow these instructions to setup the project in your local system.
- Clone the repository.
git clone https://github.com/scorelab/Labellab.git
-
Follow the instructions provided below for setting up each section of the project
NOTE: The Node.js API in
labellab-server
directory is now deprecated and is no longer being used in the project. Instead, use the Flask API inlabellab-flask
directory.
- Join the LabelLab Gitter Channel for any queries or discussions regarding the project.
- For learning more about SCoRE Lab and its various other projects, check out our Website