# autodistill/autodistill

Images to inference with no labeling (use foundation models to train supervised models).

Repository: https://github.com/autodistill/autodistill
Canonical: https://ross.abutalabs.com/products/autodistill
Homepage: https://docs.autodistill.com
Language: Python
License: Apache-2.0
License Family: permissive
Topics: computer-vision, model-distillation, auto-labeling, deep-learning, foundation-models, grounding-dino, image-annotation, image-classification, instance-segmentation, labeling-tool, machine-learning, multimodal, object-detection, pytorch, segment-anything, yolov5, yolov8
Last push: 2025-05-14T07:53:55+00:00

## Health v2 (maintenance only)
Score: 29/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 21, release rhythm 8, longevity 84
- inputs: {"age_days": 1184, "days_push": 476, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2763, forks 221 (observed 2026-08-28T04:07:18.560156+00:00)

## What it is
Autodistill is a Python library that uses large foundation vision models (like Grounding DINO, Grounded SAM, and CLIP) to automatically label unlabeled image datasets, then trains small supervised models (like YOLOv5, YOLOv8, and DETR) on those auto-labeled datasets. It provides a pluggable pipeline that goes from raw images to a fine-tuned, deployable model with no human labeling in between.

## Use cases
- auto-label images for object detection without manual annotation
- train a custom YOLOv8 model from unlabeled photos
- distill a large vision foundation model into a small fast model for edge deployment
- generate labeled datasets using Grounding DINO or Grounded SAM
- build an end-to-end image labeling and model training pipeline with no human intervention
- create instance segmentation training data automatically
- label a dataset with text-prompted classes using an ontology

## When to choose
- you have many unlabeled images and want to avoid manual annotation
- you need a small, fast supervised vision model distilled from a foundation model
- you want to deploy custom object detection or segmentation models to edge devices
- you want a pluggable pipeline connecting base models like Grounded SAM to target models like YOLOv8

## When to avoid
- you need highly accurate labels that require human review and verification
- your task is not object detection or instance segmentation (classification support is still limited)
- you lack GPU hardware or cannot run large foundation models locally
- you need a fully managed labeling service rather than a self-run pipeline

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, computer-vision, image-processing, data-generation, deep-learning
- domain: machine-learning, computer-vision, image-processing, deep-learning, artificial-intelligence
- platform: python, cross-platform
- tags: model-distillation, auto-labeling, foundation-models, object-detection, instance-segmentation, image-annotation, grounding-dino, segment-anything, yolov8, pytorch, dataset-labeling, edge-deployment, gpu

## Member repositories
- autodistill/autodistill (main) score 29

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:18.560156+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T08:17:26.306746+00:00, confidence not recorded.
  - readme: https://github.com/autodistill/autodistill (fetched 2026-08-28T04:07:18.560156+00:00, sha d694637a9180)
  - homepage: https://docs.autodistill.com (fetched 2026-08-29T09:56:28.816410+00:00, sha 5d20bf6376bb)
  - site_page: https://docs.autodistill.com/quickstart (fetched 2026-08-29T09:56:28.825829+00:00, sha 331304018e91)
  - registry_pypi: https://pypi.org/pypi/autodistill/json (fetched 2026-08-29T09:56:28.827859+00:00, sha 46362ed931ee)
- Data as of 2026-08-30T08:39:29.467469+00:00.