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# EasyCV
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.
### 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.
- **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).
- **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.
- **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)
## What's New
[🔥 Latest News] We have released our YOLOX-PAI that achieves SOTA results within 40~50 mAP (less than 1ms). And we also provide a convenient and fast export/predictor api for end2end object detection. To get a quick start of YOLOX-PAI, click [here](docs/source/tutorials/yolox.md)!
* 31/08/2022 EasyCV v0.6.0 was released.
- Release YOLOX-PAI which achieves SOTA results within 40~50 mAP (less than 1ms)
- Add detection algo DINO which achieves 58.5 mAP on COCO
- Add mask2former algo
- Releases imagenet1k, imagenet22k, coco, lvis, voc2012 data with BaiduDisk to accelerate downloading
Please refer to [change_log.md](docs/source/change_log.md) for more details and history.
## Technical Articles
We have a series of technical articles on the functionalities of EasyCV.
* [EasyCV开源|开箱即用的视觉自监督+Transformer算法库](https://zhuanlan.zhihu.com/p/505219993)
* [MAE自监督算法介绍和基于EasyCV的复现](https://zhuanlan.zhihu.com/p/515859470)
* [基于EasyCV复现ViTDet:单层特征超越FPN](https://zhuanlan.zhihu.com/p/528733299)
* [基于EasyCV复现DETR和DAB-DETR,Object Query的正确打开方式](https://zhuanlan.zhihu.com/p/543129581)
## Installation
Please refer to the installation section in [quick_start.md](docs/source/quick_start.md) for installation.
## Get Started
Please refer to [quick_start.md](docs/source/quick_start.md) for quick start. We also provides tutorials for more usages.
* [self-supervised learning](docs/source/tutorials/ssl.md)
* [image classification](docs/source/tutorials/cls.md)
* [object detection with yolox-pai](docs/source/tutorials/yolox.md)
* [model compression with yolox](docs/source/tutorials/compression.md)
* [metric learning](docs/source/tutorials/metric_learning.md)
* [torchacc](docs/source/tutorials/torchacc.md)
notebook
* [self-supervised learning](docs/source/tutorials/EasyCV图像自监督训练-MAE.ipynb)
* [image classification](docs/source/tutorials/EasyCV图像分类resnet50.ipynb)
* [object detection with yolox-pai](docs/source/tutorials/EasyCV图像检测YoloX.ipynb)
* [metric learning](docs/source/tutorials/EasyCV度量学习resnet50.ipynb)
## Model Zoo
<div align="center">
<b>Architectures</b>
</div>
<table align="center">
<tbody>
<tr align="center">
<td>
<b>Self-Supervised Learning</b>
</td>
<td>
<b>Image Classification</b>
</td>
<td>
<b>Object Detection</b>
</td>
<td>
<b>Segmentation</b>
</td>
</tr>
<tr valign="top">
<td>
<ul>
<li><a href="configs/selfsup/byol">BYOL (NeurIPS'2020)</a></li>
<li><a href="configs/selfsup/dino">DINO (ICCV'2021)</a></li>
<li><a href="configs/selfsup/mixco">MiXCo (NeurIPS'2020)</a></li>
<li><a href="configs/selfsup/moby">MoBY (ArXiv'2021)</a></li>
<li><a href="configs/selfsup/mocov2">MoCov2 (ArXiv'2020)</a></li>
<li><a href="configs/selfsup/simclr">SimCLR (ICML'2020)</a></li>
<li><a href="configs/selfsup/swav">SwAV (NeurIPS'2020)</a></li>
<li><a href="configs/selfsup/mae">MAE (CVPR'2022)</a></li>
<li><a href="configs/selfsup/fast_convmae">FastConvMAE (ArXiv'2022)</a></li>
</ul>
</td>
<td>
<ul>
<li><a href="configs/classification/imagenet/resnet">ResNet (CVPR'2016)</a></li>
<li><a href="configs/classification/imagenet/resnext">ResNeXt (CVPR'2017)</a></li>
<li><a href="configs/classification/imagenet/hrnet">HRNet (CVPR'2019)</a></li>
<li><a href="configs/classification/imagenet/vit">ViT (ICLR'2021)</a></li>
<li><a href="configs/classification/imagenet/swint">SwinT (ICCV'2021)</a></li>
<li><a href="configs/classification/imagenet/efficientformer">EfficientFormer (ArXiv'2022)</a></li>
<li><a href="configs/classification/imagenet/timm/deit">DeiT (ICML'2021)</a></li>
<li><a href="configs/classification/imagenet/timm/xcit">XCiT (ArXiv'2021)</a></li>
<li><a href="configs/classification/imagenet/timm/tnt">TNT (NeurIPS'2021)</a></li>
<li><a href="configs/classification/imagenet/timm/convit">ConViT (ArXiv'2021)</a></li>
<li><a href="configs/classification/imagenet/timm/cait">CaiT (ICCV'2021)</a></li>
<li><a href="configs/classification/imagenet/timm/levit">LeViT (ICCV'2021)</a></li>
<li><a href="configs/classification/imagenet/timm/convnext">ConvNeXt (CVPR'2022)</a></li>
<li><a href="configs/classification/imagenet/timm/resmlp">ResMLP (ArXiv'2021)</a></li>
<li><a href="configs/classification/imagenet/timm/coat">CoaT (ICCV'2021)</a></li>
<li><a href="configs/classificati
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An_all-in-one_toolkit_for_computer_vision_EasyCV.zip (863个子文件)
make.bat 759B
setup.cfg 925B
ms_deform_attn_cpu.cpp 1KB
vision.cpp 942B
ms_deform_attn_cuda.cu 7KB
ms_deform_im2col_cuda.cuh 54KB
.gitignore 2KB
ms_deform_attn.h 2KB
ms_deform_attn_cuda.h 1KB
ms_deform_attn_cpu.h 1KB
MANIFEST.in 115B
EasyCV图像检测YoloX.ipynb 12KB
EasyCV自监督训练-moco.ipynb 10KB
EasyCV自监督训练-dino.ipynb 10KB
EasyCV图像自监督训练-MAE.ipynb 10KB
EasyCV图像分类swinTransformer.ipynb 9KB
EasyCV图像分类resnet50.ipynb 8KB
EasyCV度量学习resnet50.ipynb 8KB
result.jpg 227KB
result.jpg 186KB
dingding_qrcode.jpg 97KB
LICENSE 11KB
LICENSE 11KB
LICENSE 10KB
LICENSE 1KB
Makefile 638B
data_hub.md 31KB
model_zoo_cls.md 24KB
model_zoo_ssl.md 17KB
model_zoo_det.md 14KB
prepare_data.md 12KB
README.md 11KB
README_zh-CN.md 10KB
file.md 8KB
change_log.md 8KB
metric_learning.md 6KB
yolox.md 5KB
CODE_OF_CONDUCT.md 5KB
cls.md 5KB
README.md 5KB
compression.md 4KB
visualization.md 4KB
export.md 4KB
model_zoo_seg.md 4KB
detr.md 4KB
README.md 3KB
README.md 3KB
quick_start.md 3KB
mmdet_models_usage_guide.md 3KB
ssl.md 3KB
torchacc.md 2KB
CONTRIBUTING.md 2KB
develop.md 2KB
README.md 2KB
pull_request_template.md 1KB
bug_report.md 1004B
feature_request.md 723B
NOTICE 1KB
onet.npy 2.24MB
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genet.py 56KB
coco_evaluation.py 51KB
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cocoeval.py 41KB
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fcos_head.py 36KB
lighthrnet.py 35KB
swin_transformer_dynamic.py 35KB
transforms.py 29KB
file_io.py 29KB
hrnet.py 29KB
vitdet.py 27KB
swin_transformer.py 26KB
yolo_head_template.py 25KB
detector.py 24KB
set_criterion.py 22KB
xcit_transformer.py 22KB
feature_extractor.py 22KB
export.py 21KB
attention.py 20KB
necks.py 20KB
dino_head.py 20KB
resnest.py 20KB
test_arcface.py 20KB
dab_detr_transformer.py 19KB
top_down_eval.py 19KB
resnet.py 19KB
pose_predictor.py 19KB
transformer_decoder.py 18KB
train.py 18KB
test_coco_evaluation.py 17KB
sampler.py 17KB
shuffle_transformer.py 17KB
boxes.py 17KB
modelzoo.py 17KB
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