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24 lines
1.6 KiB
Markdown
24 lines
1.6 KiB
Markdown
## Features of PaddleClas
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PaddleClas is an image recognition toolset for industry and academia,
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helping users train better computer vision models and apply them in real scenarios.
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Specifically, it contains the following core features.
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- Practical image recognition system: Integrate detection, feature learning,
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and retrieval modules to be applicable to all types of image recognition tasks. Four sample solutions are provided,
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including product recognition, vehicle recognition, logo recognition, and animation character recognition.
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- Rich library of pre-trained models: Provide a total of 175 ImageNet pre-trained models of 36 series,
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among which 7 selected series of models support fast structural modification.
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- Comprehensive and easy-to-use feature learning components: 12 metric learning methods are integrated and can be
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combined and switched at will through configuration files.
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- SSLD knowledge distillation: The 14 classification pre-training models generally improved their accuracy by
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more than 3%; among them, the ResNet50_vd model achieved a Top-1 accuracy of 84.0% on the Image-Net-1k dataset
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and the Res2Net200_vd pre-training model achieved a Top-1 accuracy of 85.1%.
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- Data augmentation: Provide 8 data augmentation algorithms such as AutoAugment, Cutout, Cutmix, etc.
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with the detailed introduction, code replication, and evaluation of effectiveness in a unified experimental environment.
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For more information about the quick start of image recognition, algorithm details, model training and evaluation,
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and prediction and deployment methods, please refer to the [README Tutorial](../../../README_en.md) on home page.
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