How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
Images to inference with no labeling (use foundation models to train supervised models).
An open source online platform for collaborative image labeling
Collaborative Annotation Toolkit for Massive Amounts of Image Data
A collaborative tool for labeling image data for yolo
Leverage machine learning algorithms to easily segment, classify, track and count your cells or other experimental data. Most operations are interactive, even on large datasets: you just draw the labels and immediately see the result. No machine learning expertise required.
The main features of ilastik/ilastik are: Cell Segmentation, Image Annotation Tools.
Open-source alternatives to ilastik/ilastik include: autodistill/autodistill — Images to inference with no labeling (use foundation models to train supervised models). bit-bots/imagetagger — An open source online platform for collaborative image labeling. catmaid/catmaid — Collaborative Annotation Toolkit for Massive Amounts of Image Data. ch-sa/labelcloud — A lightweight tool for labeling 3D bounding boxes in point clouds. computational-cell-analytics/micro-sam. alturosdestinations/alturos.imageannotation — A collaborative tool for labeling image data for yolo.