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PaddleClas is an image recognition toolset for industry and academia, helping users train better computer vision models and apply them in real scenarios.
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.
Rich library of pre-trained models: Provide a total of 150 ImageNet pre-trained models in 33 series, among which 6 selected series of models support fast structural modification.
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.
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%.
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.
Quick experience of image recognition：Link
Image recognition can be divided into three steps:
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.
PaddleClas is released under the Apache 2.0 license Apache 2.0 license
Contributions are highly welcomed and we would really appreciate your feedback!!
：Code submit frequency
：React/respond to issue & PR etc.
：Well-balanced team members and collaboration
：Recent popularity of project
：Star counts, download counts etc.