Package: pai-easycv
EasyCVEnglish |简体中文IntroductionEasyCV 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 featuresSOTA SSL AlgorithmsEasyCV 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 TransformersEasyCV 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 fromtimm.Functionality & ExtensibilityIn 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 onPAI-EAS, which can be easily deployed as online service and support automatic scaling and service monitoring.EfficiencyEasyCV supports multi-gpu and multi-worker training. EasyCV usesDALIto accelerate data io and preprocessing process, and usesTorchAcceleratorand fp16 to accelerate training process. For inference optimization, EasyCV exports model using jit script, which can be optimized byPAI-BladeWhat's New[🔥 2023.05.09]09/05/2023 EasyCV v0.11.0 was released.Support EasyCV as a plug-in for [modelscope](https://github.com/modelscope/modelscope.[🔥 2023.03.06]06/03/2023 EasyCV v0.10.0 was released.Add segmentation model STDCAdd skeleton based video recognition model STGCNSupport ReID and Multi-len MOT[🔥 2023.01.17]17/01/2023 EasyCV v0.9.0 was released.Support Single-lens MOTSupport video recognition (X3D, SWIN-video)[🔥 2022.12.02]02/12/2022 EasyCV v0.8.0 was released.bevformer-base NDS increased by 0.8 on nuscenes val, training speed increased by 10%, and inference speed increased by 40%.Support Objects365 pretrain and Adding the DINO++ model can achieve an accuracy of 63.4mAP at a model scale of 200M(Under the same scale, the accuracy is the best).[🔥 2022.08.31] 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, clickhere!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 COCOAdd mask2former algoReleases imagenet1k, imagenet22k, coco, lvis, voc2012 data with BaiduDisk to accelerate downloadingPlease refer tochange_log.mdfor more details and history.Technical ArticlesWe 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MAE自监督算法介绍和基于EasyCV的复现基于EasyCV复现ViTDet：单层特征超越FPN基于EasyCV复现DETR和DAB-DETR，Object Query的正确打开方式YOLOX-PAI: 加速YOLOX, 比YOLOv6更快更强EasyCV带你复现更好更快的自监督算法-FastConvMAEEasyCV DataHub 提供多领域视觉数据集下载，助力模型生产使用EasyCV Mask2Former轻松实现图像分割InstallationPlease refer to the installation section inquick_start.mdfor installation.Get StartedPlease refer toquick_start.mdfor quick start. We also provides tutorials for more usages.self-supervised learningimage classificationmetric learningobject detection with yolox-paimodel compression with yoloxusing torchaccfile io for local and oss filesusing mmdetection model in EasyCVbatch prediction toolsnotebookself-supervised learningimage classificationobject detection with yolox-paimetric learningModel ZooArchitecturesSelf-Supervised LearningImage ClassificationObject DetectionSegmentationObject Detection 3DBYOL (NeurIPS'2020)DINO (ICCV'2021)MiXCo (NeurIPS'2020)MoBY (ArXiv'2021)MoCov2 (ArXiv'2020)SimCLR (ICML'2020)SwAV (NeurIPS'2020)MAE (CVPR'2022)FastConvMAE (ArXiv'2022)ResNet (CVPR'2016)ResNeXt (CVPR'2017)HRNet (CVPR'2019)ViT (ICLR'2021)SwinT (ICCV'2021)EfficientFormer (ArXiv'2022)DeiT (ICML'2021)XCiT (ArXiv'2021)TNT (NeurIPS'2021)ConViT (ArXiv'2021)CaiT (ICCV'2021)LeViT (ICCV'2021)ConvNeXt (CVPR'2022)ResMLP (ArXiv'2021)CoaT (ICCV'2021)ConvMixer (ICLR'2022)MLP-Mixer (ArXiv'2021)NesT (AAAI'2022)PiT (ArXiv'2021)Twins (NeurIPS'2021)Shuffle Transformer (ArXiv'2021)DeiT III (ECCV'2022)Hydra Attention (2022)FCOS (ICCV'2019)YOLOX (ArXiv'2021)YOLOX-PAI (ArXiv'2022)DETR (ECCV'2020)DAB-DETR (ICLR'2022)DN-DETR (CVPR'2022)DINO (ArXiv'2022)Instance SegmentationMask R-CNN (ICCV'2017)ViTDet (ArXiv'2022)Mask2Former (CVPR'2022)Semantic SegmentationFCN (CVPR'2015)UperNet (ECCV'2018)Panoptic SegmentationMask2Former (CVPR'2022)BEVFormer (ECCV'2022)Please refer to the following model zoo for more details.self-supervised learning model zooclassification model zoodetection model zoodetection3d model zoosegmentation model zoopose model zooData HubEasyCV have collected dataset info for different senarios, making it easy for users to finetune or evaluate models in EasyCV model zoo.Please refer todata_hub.md.LicenseThis project is licensed under theApache License (Version 2.0). This toolkit also contains various third-party components and some code modified from other repos under other open source licenses. See theNOTICEfile for more information.ContactThis repo is currently maintained by PAI-CV team, you can contact us byDingding group number: 41783266Email:easycv@list.alibaba-inc.comEnterprise ServiceIf you need EasyCV enterprise service support, or purchase cloud product services, you can contact us by DingDing Group.