PaddleClas is a toolset for image classification tasks prepared for the industry and academia. It helps users train better computer vision models and apply them in real scenarios.
PaddleClas is an image recognition toolset for industry and academia, helping users train better computer vision models and apply them in real scenarios.
**Recent update**
**Recent updates**
- 2021.06.16 PaddleClas release/2.2.
- Add metric learning and vector search module.
- Add product recognition, cartoon character recognition, car recognition and logo recognition.
- Add metric learning and vector search modules.
- Add product recognition, animation character recognition, vehicle recognition and logo recognition.
- Added 30 pretrained models of LeViT, Twins, TNT, DLA, HarDNet, and RedNet, and the accuracy is roughly the same as that of the paper.
- 2021.05.14
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@@ -21,467 +21,97 @@ PaddleClas is a toolset for image classification tasks prepared for the industry
-[more](./docs/en/update_history_en.md)
## Features
- Rich model zoo. Based on the ImageNet-1k classification dataset, PaddleClas provides 29 series of classification network structures and training configurations, 134 models' pretrained weights and their evaluation metrics.
- A practical image recognition system consist of detection, feature learning and retrieval modules, widely applicable to all types of image recognition tasks.
Four sample solutions are provided, including product recognition, vehicle recognition, logo recognition and animation character recognition.
-SSLD Knowledge Distillation. Based on this SSLD distillation strategy, the top-1 acc of the distilled model is generally increased by more than 3%.
-Rich library of pre-trained models: Provide a total of 164 ImageNet pre-trained models in 34 series, among which 6 selected series of models support fast structural modification.
-Data augmentation: PaddleClas provides detailed introduction of 8 data augmentation algorithms such as AutoAugment, Cutout, Cutmix, code reproduction and effect evaluation in a unified experimental environment.
-Comprehensive and easy-to-use feature learning components: 12 metric learning methods are integrated and can be combined and switched at will through configuration files.
-Pretrained model with 100,000 categories: Based on `ResNet50_vd` model, Baidu open sourced the `ResNet50_vd` pretrained model trained on a 100,000-category dataset. In some practical scenarios, the accuracy based on the pretrained weights can be increased by up to 30%.
-SSLD knowledge distillation: The 14 classification pre-training models generally improved their accuracy by more than 3%; among them, the ResNet50_vd model achieved a Top-1 accuracy of 84.0% on the Image-Net-1k dataset and the Res2Net200_vd pre-training model achieved a Top-1 accuracy of 85.1%.
-A variety of training modes, including multi-machine training, mixed precision training, etc.
-Data augmentation: Provide 8 data augmentation algorithms such as AutoAugment, Cutout, Cutmix, etc. with detailed introduction, code replication and evaluation of effectiveness in a unified experimental environment.
- A variety of inference and deployment solutions, including TensorRT inference, Paddle-Lite inference, model service deployment, model quantification, Paddle Hub, etc.
- Support Linux, Windows, macOS and other systems.
*Scan the QR code below with your Wechat and send the message `分类` out, then you will be invited into the official technical exchange group.
*You can also scan the QR code below to join the PaddleClas WeChat group to get more efficient answers to your questions and to communicate with developers from all walks of life. We look forward to hearing from you.
Based on the ImageNet-1k classification dataset, the 24 classification network structures supported by PaddleClas and the corresponding 122 image classification pretrained models are shown below. Training trick, a brief introduction to each series of network structures, and performance evaluation will be shown in the corresponding chapters. The evaluation environment is as follows.
* CPU evaluation environment is based on Snapdragon 855 (SD855).
* The GPU evaluation speed is measured by running 500 times under the FP32+TensorRT configuration (excluding the warmup time of the first 10 times).
Curves of accuracy to the inference time of common server-side models are shown as follows.
Curves of accuracy to the inference time and storage size of common mobile-side models are shown as follows.
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### SSLD pretrained models
Accuracy and inference time of the prtrained models based on SSLD distillation are as follows. More detailed information can be refered to [SSLD distillation tutorial](./docs/en/advanced_tutorials/distillation/distillation_en.md).
* Note: `Reference Top-1 Acc` means accuracy of pretrained models which are trained on ImageNet1k dataset.
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### ResNet and Vd series
Accuracy and inference time metrics of ResNet and Vd series models are shown as follows. More detailed information can be refered to [ResNet and Vd series tutorial](./docs/en/models/ResNet_and_vd_en.md).
Accuracy and inference time metrics of Mobile series models are shown as follows. More detailed information can be refered to [Mobile series tutorial](./docs/en/models/Mobile_en.md).
Accuracy and inference time metrics of SEResNeXt and Res2Net series models are shown as follows. More detailed information can be refered to [SEResNext and_Res2Net series tutorial](./docs/en/models/SEResNext_and_Res2Net_en.md).
Accuracy and inference time metrics of DPN and DenseNet series models are shown as follows. More detailed information can be refered to [DPN and DenseNet series tutorial](./docs/en/models/DPN_DenseNet_en.md).
Accuracy and inference time metrics of HRNet series models are shown as follows. More detailed information can be refered to [Mobile series tutorial](./docs/en/models/HRNet_en.md).
Accuracy and inference time metrics of Inception series models are shown as follows. More detailed information can be refered to [Inception series tutorial](./docs/en/models/Inception_en.md).
Accuracy and inference time metrics of EfficientNet and ResNeXt101_wsl series models are shown as follows. More detailed information can be refered to [EfficientNet and ResNeXt101_wsl series tutorial](./docs/en/models/EfficientNet_and_ResNeXt101_wsl_en.md).
Accuracy and inference time metrics of ResNeSt and RegNet series models are shown as follows. More detailed information can be refered to [ResNeSt and RegNet series tutorial](./docs/en/models/ResNeSt_RegNet_en.md).
Accuracy and inference time metrics of ViT and DeiT series models are shown as follows. More detailed information can be refered to [ViT and DeiT series tutorial](./docs/en/models/ViT_and_DeiT_en.md).
Accuracy and inference time metrics of RepVGG series models are shown as follows. More detailed information can be refered to [RepVGG series tutorial](./docs/en/models/RepVGG_en.md).
Accuracy and inference time metrics of MixNet series models are shown as follows. More detailed information can be refered to [MixNet series tutorial](./docs/en/models/MixNet_en.md).
Accuracy and inference time metrics of ReXNet series models are shown as follows. More detailed information can be refered to [ReXNet series tutorial](./docs/en/models/ReXNet_en.md).
Accuracy and inference time metrics of SwinTransformer series models are shown as follows. More detailed information can be refered to [SwinTransformer series tutorial](./docs/en/models/SwinTransformer_en.md).
[1]:Based on imagenet22k dataset pre-training, and then in imagenet1k dataset transfer learning.
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### Others
Accuracy and inference time metrics of AlexNet, SqueezeNet series, VGG series and DarkNet53 models are shown as follows. More detailed information can be refered to [Others](./docs/en/models/Others_en.md).
## Introduction to Image Recognition Systems
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Image recognition can be divided into three steps:
- （1）Identify region proposal for target objects through a detection model；
- （2）Extract features for each region proposal;
- （3）Search features in the retrieval database and output results;
For a new unknown category, there is no need to retrain the model, just prepare images of new category, extract features and update retrieval database and the category can be recognised.
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## License
PaddleClas is released under the <ahref="https://github.com/PaddlePaddle/PaddleClas/blob/master/LICENSE">Apache 2.0 license</a>
## License
PaddleClas is released under the Apache 2.0 license <ahref="https://github.com/PaddlePaddle/PaddleCLS/blob/master/LICENSE">Apache 2.0 license</a>
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## Contribution
Contributions are highly welcomed and we would really appreciate your feedback!!
- Thank [nblib](https://github.com/nblib) to fix bug of RandErasing.
- Thank [chenpy228](https://github.com/chenpy228) to fix some typos PaddleClas.
- Thank [jm12138](https://github.com/jm12138) to add ViT, DeiT models and RepVGG models into PaddleClas.
- Thank [FutureSI](https://aistudio.baidu.com/aistudio/personalcenter/thirdview/76563) to parse and summarize the PaddleClas code.
