# alibaba/EasyCV

An all-in-one toolkit for computer vision

Repository: https://github.com/alibaba/EasyCV
Canonical: https://ross.abutalabs.com/products/easycv
Language: Python
License: Apache-2.0
License Family: permissive
Topics: self-supervised-learning, transformers, classification, computer-vision, object-detection, pytorch, vision-transformer
Last push: 2025-05-09T10:40:23+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 20, release rhythm 8, longevity 100
- inputs: {"age_days": 1615, "days_push": 481, "days_rel": 481, "gap_med": null, "n_releases_24m": 1}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1954, forks 227 (observed 2026-08-28T04:05:58.875704+00:00)

## What it is
EasyCV is an all-in-one PyTorch-based computer vision toolkit from Alibaba covering self-supervised learning, vision transformers, and major CV tasks like classification, object detection, metric learning, and pose estimation. It includes SOTA SSL algorithms (SimCLR, MoCo, Swav, DINO, MAE), pretrained transformer models, efficient multi-GPU training with DALI acceleration, and deployment support on PAI-EAS.

## Use cases
- train self-supervised vision models with DINO or MAE
- run image classification with pretrained vision transformers
- fine-tune object detection models like DETR
- benchmark SSL model representations
- deploy CV models as online inference services
- accelerate data preprocessing for vision training

## When to choose
- you need a unified PyTorch toolbox for SSL and transformer-based vision tasks
- you want off-the-shelf SOTA vision transformer models and pretrained weights
- you need efficient multi-GPU training with accelerated data loading
- you plan to deploy vision models as scalable online services

## When to avoid
- you need tasks outside its supported areas like segmentation or video
- you prefer lightweight single-purpose libraries
- you work outside the PyTorch ecosystem
- you need heavy customization of the training framework

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, image-processing, computer-vision, benchmarking
- domain: computer-vision, machine-learning, deep-learning, image-processing
- platform: python
- tags: self-supervised-learning, vision-transformer, object-detection, image-classification, pytorch, metric-learning, pose-estimation, model-deployment, linux, gpu, docker

## Member repositories
- alibaba/EasyCV (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:58.875704+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 2026-08-30T03:06:32.084618+00:00, confidence not recorded.
  - readme: https://github.com/alibaba/EasyCV (fetched 2026-08-28T04:05:58.875704+00:00, sha e2cc76e0b77d)
- Data as of 2026-08-30T08:39:29.467469+00:00.