EasyCV is an all-in-one computer vision toolbox based on PyTorch, mainly focus on self-supervised learning, transformer based models, and SOTA CV tasks including image classification, metric-learning, object detection, pose estimation and so on.
EasyCV provides state-of-the-art algorithms in self-supervised learning based on contrastive learning such as SimCLR, MoCO V2, Swav, DINO and also MAE based on masked image modeling. We also provide standard benchmark tools for ssl model evaluation.
EasyCV aims to provide an easy way to use the off-the-shelf SOTA transformer models trained either using supervised learning or self-supervised learning, such as ViT, Swin-Transformer and Shuffle Transformer. More models will be added in the future. In addition, we support all the pretrained models from [timm](https://github.com/rwightman/pytorch-image-models).
In addition to SSL, EasyCV also support image classification, object detection, metric learning, and more area will be supported in the future. Although convering different area,
EasyCV decompose the framework into different componets such as dataset, model, running hook, making it easy to add new compoenets and combining it with existing modules.
EasyCV provide simple and comprehensive interface for inference. Additionaly, all models are supported on [PAI-EAS](https://help.aliyun.com/document_detail/113696.html), which can be easily deployed as online service and support automatic scaling and service monitoring.
EasyCV support multi-gpu and multi worker training. EasyCV use [DALI](https://github.com/NVIDIA/DALI) to accelerate data io and preprocessing process, and use [TorchAccelerator](https://github.com/alibaba/EasyCV/tree/master/docs/source/tutorials/torchacc.md) and fp16 to accelerate training process. For inference optimization, EasyCV export model using jit script, which can be optimized by [PAI-Blade](https://help.aliyun.com/document_detail/205134.html)
This project licensed under the [Apache License (Version 2.0)](LICENSE). This toolkit also contains various third-party components and some code modified from other repos under other open source licenses. See the [NOTICE](NOTICE) file for more information.
