# facebookresearch/CutLER

Code release for "Cut and Learn for Unsupervised Object Detection and Instance Segmentation" and "VideoCutLER: Surprisingly Simple Unsupervised Video Instance Segmentation"

Repository: https://github.com/facebookresearch/CutLER
Canonical: https://ross.abutalabs.com/products/cutler
Language: Python
License: NOASSERTION
License Family: other
Last push: 2026-04-14T01:20:10+00:00

## Health v2 (maintenance only)
Score: 66/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 77, release rhythm 35, longevity 94
- inputs: {"age_days": 1324, "days_push": 142, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1072, forks 109 (observed 2026-08-28T04:03:28.441216+00:00)

## What it is
CutLER is a research codebase from Meta FAIR for training object detection and instance segmentation models without human annotations, using the MaskCut pseudo-mask approach on ImageNet-1K. It also includes VideoCutLER for unsupervised video instance segmentation without optical flow.

## Use cases
- train an object detector without labeled data
- generate pseudo-masks for objects in images
- unsupervised instance segmentation
- unsupervised video instance segmentation
- pretrain a detector for semi-supervised fine-tuning
- evaluate detection robustness across domain shifts

## When to choose
- you lack annotated detection/segmentation data
- you need a pretrained backbone for semi-supervised detection
- you want to reproduce CVPR research on unsupervised segmentation

## When to avoid
- you need a production-ready, supported detection framework
- you have abundant labeled data and just need standard supervised training
- you need a no-GPU or lightweight inference tool

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, computer-vision, image-processing, video-processing
- domain: computer-vision, machine-learning, deep-learning, artificial-intelligence
- platform: python
- tags: object-detection, instance-segmentation, unsupervised-learning, pseudo-labeling, research-code, pytorch, cvpr, linux, gpu

## Member repositories
- facebookresearch/CutLER (main) score 66

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:28.441216+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 2026-08-30T06:53:41.324608+00:00, confidence not recorded.
  - readme: https://github.com/facebookresearch/CutLER (fetched 2026-08-28T04:03:28.441216+00:00, sha 93301b54b509)
- Data as of 2026-08-30T08:39:29.467469+00:00.