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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 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.
**Note**: Baidu proposed a new image classification network structure **`HS-ResNet`**, which reaches 81.3% on ImageNet-1k dataset, while its `params` is almost same as `ResNet50`.The arxiv link is here: [HS-ResNet: Hierarchical-Split Block on Convolutional Neural Network](https://arxiv.org/pdf/2010.07621.pdf). The model structure and the pretrained weights are coming soon!
**Recent update** **Recent update**
- 2020.09.17 Add `Res2Net50_vd_26w_4s_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 83.1%. Add `Res2Net101_vd_26w_4s_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 83.9%. - 2020.09.17 Add `Res2Net50_vd_26w_4s_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 83.1%. Add `Res2Net101_vd_26w_4s_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 83.9%.
- 2020.10.12 Add Paddle-Lite demo。 - 2020.10.12 Add Paddle-Lite demo。
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- [Inception series](#Inception_series) - [Inception series](#Inception_series)
- [EfficientNet and ResNeXt101_wsl series](#EfficientNet_and_ResNeXt101_wsl_series) - [EfficientNet and ResNeXt101_wsl series](#EfficientNet_and_ResNeXt101_wsl_series)
- [ResNeSt and RegNet series](#ResNeSt_and_RegNet_series) - [ResNeSt and RegNet series](#ResNeSt_and_RegNet_series)
- HS-ResNet: arxiv link: [https://arxiv.org/pdf/2010.07621.pdf](https://arxiv.org/pdf/2010.07621.pdf). Code and models are coming soon!
- Model training/evaluation - Model training/evaluation
- [Data preparation](./docs/en/tutorials/data_en.md) - [Data preparation](./docs/en/tutorials/data_en.md)
- [Model training and finetuning](./docs/en/tutorials/getting_started_en.md) - [Model training and finetuning](./docs/en/tutorials/getting_started_en.md)

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- [Inception系列](#Inception系列) - [Inception系列](#Inception系列)
- [EfficientNet与ResNeXt101_wsl系列](#EfficientNet与ResNeXt101_wsl系列) - [EfficientNet与ResNeXt101_wsl系列](#EfficientNet与ResNeXt101_wsl系列)
- [ResNeSt与RegNet系列](#ResNeSt与RegNet系列) - [ResNeSt与RegNet系列](#ResNeSt与RegNet系列)
- HS-ResNet: arxiv文章链接: [https://arxiv.org/pdf/2010.07621.pdf](https://arxiv.org/pdf/2010.07621.pdf)。 代码和预训练模型即将开源,敬请期待。
- 模型训练/评估 - 模型训练/评估
- [数据准备](./docs/zh_CN/tutorials/data.md) - [数据准备](./docs/zh_CN/tutorials/data.md)
- [模型训练与微调](./docs/zh_CN/tutorials/getting_started.md) - [模型训练与微调](./docs/zh_CN/tutorials/getting_started.md)

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