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@ -20,29 +20,29 @@ English | [简体中文](README_zh-CN.md)
## Introduction
EasyCV is an all-in-one computer vision toolbox based on PyTorch, mainly focuses on self-supervised learning, transformer based models, and major CV tasks including image classification, metric-learning, object detection, pose estimation and so on.
EasyCV is an all-in-one computer vision toolbox based on PyTorch, mainly focuses on self-supervised learning, transformer based models, and major CV tasks including image classification, metric-learning, object detection, pose estimation, and so on.
### Major features
- **SOTA SSL Algorithms**
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 benchmarking tools for ssl model evaluation.
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 benchmarking tools for ssl model evaluation.
- **Vision Transformers**
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 DETR Series. 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).
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 DETR Series. 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).
- **Functionality & Extensibility**
In addition to SSL, EasyCV also supports image classification, object detection, metric learning, and more areas will be supported in the future. Although covering different areas,
EasyCV decomposes the framework into different components such as dataset, model and running hook, making it easy to add new components and combining it with existing modules.
EasyCV provides simple and comprehensive interface for inference. Additionally, 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 provides simple and comprehensive interface for inference. Additionally, 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.
- **Efficiency**
EasyCV supports multi-gpu and multi worker training. EasyCV uses [DALI](https://github.com/NVIDIA/DALI) to accelerate data io and preprocessing process, and uses [TorchAccelerator](https://github.com/alibaba/EasyCV/tree/master/docs/source/tutorials/torchacc.md) and fp16 to accelerate training process. For inference optimization, EasyCV exports model using jit script, which can be optimized by [PAI-Blade](https://help.aliyun.com/document_detail/205134.html)
EasyCV supports multi-gpu and multi-worker training. EasyCV uses [DALI](https://github.com/NVIDIA/DALI) to accelerate data io and preprocessing process, and uses [TorchAccelerator](https://github.com/alibaba/EasyCV/tree/master/docs/source/tutorials/torchacc.md) and fp16 to accelerate training process. For inference optimization, EasyCV exports model using jit script, which can be optimized by [PAI-Blade](https://help.aliyun.com/document_detail/205134.html)
## What's New
@ -172,7 +172,7 @@ notebook
</ul>
</ul>
</ul>
<li><b>Sementic Segmentation</b></li>
<li><b>Semantic Segmentation</b></li>
<ul>
<ul>
<li><a href="configs/segmentation/fcn">FCN (CVPR'2015)</a></li>
@ -204,14 +204,14 @@ Please refer to the following model zoo for more details.
## Data Hub
EasyCV have collected dataset info for different senarios, making it easy for users to fintune or evaluate models in EasyCV modelzoo.
EasyCV have collected dataset info for different senarios, making it easy for users to finetune or evaluate models in EasyCV model zoo.
Please refer to [data_hub.md](docs/source/data_hub.md).
## License
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.
This project is 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.
## Contact