# facebookresearch/Detic

Code release for "Detecting Twenty-thousand Classes using Image-level Supervision".

Repository: https://github.com/facebookresearch/Detic
Canonical: https://ross.abutalabs.com/products/detic
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2024-03-21T20:23:43+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 1726, "days_push": 895, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2008, forks 227 (observed 2026-08-28T04:06:04.816501+00:00)

## What it is
Detic is the official code release for the ECCV 2022 paper 'Detecting Twenty-thousand Classes using Image-level Supervision'. It is an open-vocabulary object detector built on Detectron2 that uses CLIP embeddings to detect arbitrary classes given only class names, trained on ImageNet-21K with image-level labels.

## Use cases
- detect arbitrary objects in images given class names
- run open-vocabulary object detection without finetuning
- train an object detector using only image-level labels
- evaluate zero-shot detection on LVIS, COCO, OpenImages, or Objects365
- detect objects from a custom vocabulary list
- run a detection demo in Colab or Hugging Face Spaces

## When to choose
- you need to detect classes not covered by fixed-vocabulary detectors
- you want state-of-the-art open-vocabulary LVIS/COCO results
- you only have image-level labels rather than bounding box annotations
- you want a DETR-style detector with flexible vocabularies

## When to avoid
- you need a lightweight detector for CPU-only or edge deployment
- you need a production system with active maintenance and support
- you only need standard fixed-vocabulary COCO detection
- you need a non-PyTorch or non-Detectron2 stack

## Facets
- artifact type: library
- maturity: maintenance
- function: computer-vision, image-processing, machine-learning, deep-learning
- domain: computer-vision, machine-learning, deep-learning, artificial-intelligence
- platform: python, cross-platform
- tags: object-detection, open-vocabulary-detection, clip, detectron2, zero-shot-detection, eccv-2022, research-code, gpu, linux

## Member repositories
- facebookresearch/Detic (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:04.816501+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 2026-08-30T03:01:18.299866+00:00, confidence not recorded.
  - readme: https://github.com/facebookresearch/Detic (fetched 2026-08-28T04:06:04.816501+00:00, sha 2952cb8c33ae)
- Data as of 2026-08-30T08:39:29.467469+00:00.