Autodistill Repo Skill

Use this skill when a task mentions Autodistill, automatic image labeling from foundation models, prompt-to-class ontologies, distilling labels into smaller vision target models, autodistill CLI commands, DetectionBaseModel/CaptionOntology, model plugin aliases, or Autodistill utility/debugging workflows.

Autodistill's core package defines interfaces, dataset writers, a CLI, a plugin registry, and utilities. Concrete base/target model implementations live in separate autodistill-* plugin packages and may require model downloads, GPU, credentials, long training, or separate licenses.

First Steps

1. For stale-skill checks, read repository provenance.
2. For package concepts and snapshot caveats, read package overview.
3. Run the safe root smoke script when checking an environment:

python scripts/check_autodistill_install.py --check-cli

4. Route to the most specific sub-skill below.

Sub-skill Routes

User task	Read
Auto-label an image folder, validate a generated YOLO/classification dataset, debug .label(), use SAHI/NMS, or run a safe dummy dataset-writer check	dataset-labeling
Build an autodistill ... command, inspect base/target aliases, understand plugin packages, run a safe CLI dry run, or debug CLI/model registry errors	cli-and-model-registry
Design an ontology, implement a custom base/target model, validate abstract interface conformance, compose detector+classifier models, or use embedding ontologies	ontologies-and-model-interfaces
Convert image inputs, use plotting/comparison helpers, split video frames, understand split_data, or handle Roboflow sync utility boundaries	utilities

Install and Minimal Import Check

Core install:

pip install autodistill
python - <<'PY'
import autodistill
from autodistill.detection import CaptionOntology
print(autodistill.__version__)
print(CaptionOntology({"milk bottle": "bottle"}).classes())
PY

Plugin example for a full detection pipeline:

pip install autodistill autodistill-grounding-dino autodistill-yolov8

Install only the selected plugin packages. Do not install every supported model just to use the core package.

Safe Core Workflow Skeleton

from autodistill.detection import CaptionOntology
from autodistill_grounding_dino import GroundingDINO

ontology = CaptionOntology({"shipping container": "container"})
base_model = GroundingDINO(ontology=ontology)
base_model.label(input_folder="images", extension=".jpg", output_folder="dataset")

This shows the core call shape. The selected plugin's environment and runtime behavior must be verified separately.

Cross-cutting Troubleshooting

Read troubleshooting when failures involve install/import, missing plugins, GPU/backend crashes, CLI side effects, Roboflow credentials, dataset output, stale docs names, or utility boundaries.

Important snapshot warnings:

• Source-verified core version is 0.1.29.
• In this snapshot the source method is label(), not the stale docs name label_folder().
• In this snapshot the source method is sahi_predict(), not the stale docs name predict_sahi().
• In this snapshot composed detection uses ComposedDetectionModel; some docs use stale names.
• The CLI SUPPORTED_MODEL_TYPES source constant has a missing comma, so classification/segmentation CLI paths may not match docs.

Stop Conditions

Before continuing, ask for approval when a task would install plugin packages, download model weights, run large labeling/training, use GPU unexpectedly, contact Roboflow/cloud APIs, or overwrite user output directories.