X-AnyLabeling and autodistill
These are complements: X-AnyLabeling manually annotates datasets with AI assistance, while Autodistill automatically generates training data from foundation models, addressing different stages of the labeling pipeline.
About X-AnyLabeling
CVHub520/X-AnyLabeling
Effortless data labeling with AI support from Segment Anything and other awesome models.
This tool helps data professionals quickly and accurately label images and videos for various computer vision tasks. You input raw visual data, and it assists you in marking objects, segments, or text, outputting structured annotations that can be used to train AI models. It's designed for data engineers and researchers who need to prepare large datasets for machine learning applications.
About autodistill
autodistill/autodistill
Images to inference with no labeling (use foundation models to train supervised models).
Autodistill helps you train custom computer vision models, like those for detecting specific objects in images, without having to manually label a single image. You provide raw, unlabeled images, and the system uses advanced AI to automatically label them and then train a specialized model. This is for machine learning engineers, data scientists, or researchers who need to rapidly deploy AI for image analysis.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work