EasyCV

EasyCV is Alibaba’s all-in-one computer vision toolkit, built on PyTorch to cover the full workflow from data preparation and model training to deployment. It bundles production-grade implementations for image classification, object detection, segmentation, self-supervised learning and vision transformers behind a unified, configuration-driven API. It is an open-source project on GitHub with around 1,900 stars, maintained into 2025.

Overview

EasyCV comes out of Alibaba’s PAI (Platform for AI) ecosystem and aims to lower the engineering cost of landing vision models in real products. A model zoo of pretrained backbones and task models ships with the toolkit, and common training recipes (mixed precision, distributed training, knowledge distillation, model compression) are built in rather than bolted on. The same configuration style works across tasks, so a team can move from a classification prototype to a detection system without learning a new framework, and export paths target common deployment formats.

Key Features

• Unified PyTorch toolkit for classification, detection, segmentation and more
• Pretrained model zoo with vision transformer and self-supervised models
• Configuration-driven training with distributed and mixed-precision support
• Built-in distillation, pruning and quantization for deployment
• Data augmentation and preprocessing pipelines for common vision datasets
• Export support for serving models outside the training environment

💳 Pricing

Free. EasyCV is open source under the Apache-2.0 license and can be installed from PyPI as pai-easycv at no cost.

EasyCV’s model zoo covers the architectures teams most often reach for — ResNet and vision transformer backbones, YOLOX and DETR-style detectors, segmentation networks and self-supervised pretraining methods such as MAE and MoCo — each with documented training recipes and reported metrics. It also connects to Alibaba’s ModelScope model hub for grabbing pretrained weights, and its export tooling targets formats used by common inference servers, so a model trained with EasyCV can move into an existing serving stack without a rewrite.

Who Is It For

Computer vision engineers and research teams who want one maintained toolkit for training and shipping vision models, especially teams already working in the PyTorch and Alibaba Cloud ecosystem.