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Add the discription of RepVGG (#595)
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Tingquan Gao
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README.md
28
README.md
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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.
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**Recent update**
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- 2021.01.27 Add `ViT` and `DeiT` pretrained model, `ViT`'s Top-1 Acc on ImageNet-1k dataset reaches 85.13%, and `DeiT` reaches 85.1%.
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- 2021.02.01 Add `RepVGG` pretrained models, whose Top-1 Acc on ImageNet-1k dataset reaches 79.65%.
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- 2021.01.27 Add `ViT` and `DeiT` pretrained models, `ViT`'s Top-1 Acc on ImageNet-1k dataset reaches 85.13%, and `DeiT` reaches 85.1%.
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- 2021.01.08 Add support for whl package and its usage, Model inference can be done by simply install paddleclas using pip.
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- 2020.12.16 Add support for TensorRT when using cpp inference to obain more obvious acceleration.
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- 2020.12.06 Add `SE_HRNet_W64_C_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 84.75%.
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@ -68,6 +69,7 @@ PaddleClas is a toolset for image classification tasks prepared for the industry
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- [EfficientNet and ResNeXt101_wsl series](#EfficientNet_and_ResNeXt101_wsl_series)
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- [ResNeSt and RegNet series](#ResNeSt_and_RegNet_series)
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- [Transformer series](#Transformer)
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- [RepVGG series](#RepVGG)
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- [Others](#Others)
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- HS-ResNet: arxiv link: [https://arxiv.org/pdf/2010.07621.pdf](https://arxiv.org/pdf/2010.07621.pdf). Code and models are coming soon!
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- Model training/evaluation
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@ -356,7 +358,7 @@ Accuracy and inference time metrics of ResNeSt and RegNet series models are show
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<a name="Transformer"></a>
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### Transformer series
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Accuracy and inference time metrics of ViT and DeiT series models are shown as follows. More detailed information can be refered to [Transformer series tutorial](./docs/en/models/Transformer.md).
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Accuracy and inference time metrics of ViT and DeiT series models are shown as follows. More detailed information can be refered to [Transformer series tutorial](./docs/en/models/Transformer_en.md).
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| Model | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | Flops(G) | Params(M) | Download Address |
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@ -384,6 +386,26 @@ Accuracy and inference time metrics of ViT and DeiT series models are shown as f
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<a name="RepVGG_series"></a>
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### RepVGG
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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).
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| Model | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | Flops(G) | Params(M) | Download Address |
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|------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------|
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| RepVGG_A0 | 0.7131 | 0.9016 | | | | | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A0_pretrained.pdparams) |
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| RepVGG_A1 | 0.7380 | 0.9146 | | | | | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A1_pretrained.pdparams) |
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| RepVGG_A2 | 0.7571 | 0.9264 | | | | | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A2_pretrained.pdparams) |
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| RepVGG_B0 | 0.7450 | 0.9213 | | | | | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B0_pretrained.pdparams) |
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| RepVGG_B1 | 0.7773 | 0.9385 | | | | | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1_pretrained.pdparams) |
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| RepVGG_B2 | 0.7813 | 0.9410 | | | | | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B2_pretrained.pdparams) |
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| RepVGG_B1g2 | 0.7732 | 0.9359 | | | | | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1g2_pretrained.pdparams) |
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| RepVGG_B1g4 | 0.7675 | 0.9335 | | | | | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1g4_pretrained.pdparams) |
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| RepVGG_B2g4 | 0.7881 | 0.9448 | | | | | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B2g4_pretrained.pdparams) |
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| RepVGG_B3g4 | 0.7965 | 0.9485 | | | | | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B3g4_pretrained.pdparams) |
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<a name="Others"></a>
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### Others
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@ -416,4 +438,4 @@ Contributions are highly welcomed and we would really appreciate your feedback!!
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- Thank [nblib](https://github.com/nblib) to fix bug of RandErasing.
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- Thank [chenpy228](https://github.com/chenpy228) to fix some typos PaddleClas.
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- Thank [jm12138](https://github.com/jm12138) to add ViT and DeiT models into PaddleClas.
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- Thank [jm12138](https://github.com/jm12138) to add ViT, DeiT models and RepVGG models into PaddleClas.
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24
README_cn.md
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README_cn.md
@ -8,6 +8,7 @@
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**近期更新**
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- 2021.02.01 添加`RepVGG`系列模型，在ImageNet-1k上Top-1 Acc可达79.65%。
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- 2021.01.27 添加`ViT`与`DeiT`模型，在ImageNet-1k上，`ViT`模型Top-1 Acc可达85.13%，`DeiT`模型可达85.1%。
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- 2021.01.08 添加whl包及其使用说明，直接安装paddleclas whl包，即可快速完成模型预测。
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- 2020.12.16 添加对cpp预测的tensorRT支持，预测加速更明显。
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@ -68,6 +69,7 @@
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- [EfficientNet与ResNeXt101_wsl系列](#EfficientNet与ResNeXt101_wsl系列)
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- [ResNeSt与RegNet系列](#ResNeSt与RegNet系列)
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- [Transformer系列](#Transformer系列)
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- [RepVGG系列](#RepVGG系列)
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- [其他模型](#其他模型)
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- 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。
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- 模型训练/评估
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@ -386,6 +388,26 @@ ViT（Vision Transformer）与DeiT（Data-efficient Image Transformers）系列
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<a name="RepVGG系列"></a>
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### RepVGG系列
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关于RepVGG系列模型的精度、速度指标如下表所示，更多介绍可以参考：[RepVGG系列模型文档](./docs/zh_CN/models/RepVGG.md)。
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| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | Flops(G) | Params(M) | 下载地址 |
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|------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------|
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| RepVGG_A0 | 0.7131 | 0.9016 | | | | | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A0_pretrained.pdparams) |
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| RepVGG_A1 | 0.7380 | 0.9146 | | | | | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A1_pretrained.pdparams) |
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| RepVGG_A2 | 0.7571 | 0.9264 | | | | | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A2_pretrained.pdparams) |
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| RepVGG_B0 | 0.7450 | 0.9213 | | | | | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B0_pretrained.pdparams) |
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| RepVGG_B1 | 0.7773 | 0.9385 | | | | | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1_pretrained.pdparams) |
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| RepVGG_B2 | 0.7813 | 0.9410 | | | | | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B2_pretrained.pdparams) |
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| RepVGG_B1g2 | 0.7732 | 0.9359 | | | | | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1g2_pretrained.pdparams) |
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| RepVGG_B1g4 | 0.7675 | 0.9335 | | | | | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1g4_pretrained.pdparams) |
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| RepVGG_B2g4 | 0.7881 | 0.9448 | | | | | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B2g4_pretrained.pdparams) |
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| RepVGG_B3g4 | 0.7965 | 0.9485 | | | | | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B3g4_pretrained.pdparams) |
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<a name="其他模型"></a>
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### 其他模型
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@ -416,6 +438,6 @@ ViT（Vision Transformer）与DeiT（Data-efficient Image Transformers）系列
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- 非常感谢[nblib](https://github.com/nblib)修正了PaddleClas中RandErasing的数据增广配置文件。
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- 非常感谢[chenpy228](https://github.com/chenpy228)修正了PaddleClas文档中的部分错别字。
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- 非常感谢[jm12138](https://github.com/jm12138)为PaddleClas添加ViT和DeiT模型。
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- 非常感谢[jm12138](https://github.com/jm12138)为PaddleClas添加ViT，DeiT系列模型和RepVGG系列模型。
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我们非常欢迎你为PaddleClas贡献代码，也十分感谢你的反馈。
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@ -16,12 +16,7 @@ RepVGG (Making VGG-style ConvNets Great Again) series model is a simple but powe
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| RepVGG_B2 | 0.7813 | 0.9410 | 0.7878 | |
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| RepVGG_B1g2 | 0.7732 | 0.9359 | 0.7778 | |
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| RepVGG_B1g4 | 0.7675 | 0.9335 | 0.7758 | |
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| RepVGG_B2g4 | 0.7782 | 0.9380 | 0.7850 | |
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| RepVGG_B2g4 | 0.7881 | 0.9448 | 0.7938 | |
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| RepVGG_B3g4 | 0.7965 | 0.9485 | 0.8021 | |
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| Models | Top1 | Top5 | Reference<br>top1 | FLOPS<br>(G) |
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|:--:|:--:|:--:|:--:|:--:|
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| RepVGG_B3_200epochs | 0.7987 | 0.9502 | 0.8052 | |
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| RepVGG_B2g4_200epochs | 0.7881 | 0.9448 | 0.7938 | |
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| RepVGG_B3g4_200epochs | 0.7965 | 0.9485 | 0.8021 | |
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Params, FLOPs, Inference speed and other information are coming soon.
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Params, FLOPs, Inference speed and other information are coming soon.
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@ -17,12 +17,7 @@ RepVGG（Making VGG-style ConvNets Great Again）系列模型是由清华大学(
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| RepVGG_B2 | 0.7813 | 0.9410 | 0.7878 | |
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| RepVGG_B1g2 | 0.7732 | 0.9359 | 0.7778 | |
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| RepVGG_B1g4 | 0.7675 | 0.9335 | 0.7758 | |
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| RepVGG_B2g4 | 0.7782 | 0.9380 | 0.7850 | |
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| RepVGG_B2g4 | 0.7881 | 0.9448 | 0.7938 | |
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| RepVGG_B3g4 | 0.7965 | 0.9485 | 0.8021 | |
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| Models | Top1 | Top5 | Reference<br>top1 | FLOPS<br>(G) |
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|:--:|:--:|:--:|:--:|:--:|
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| RepVGG_B3_200epochs | 0.7987 | 0.9502 | 0.8052 | |
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| RepVGG_B2g4_200epochs | 0.7881 | 0.9448 | 0.7938 | |
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| RepVGG_B3g4_200epochs | 0.7965 | 0.9485 | 0.8021 | |
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关于Params、FLOPs、Inference speed等信息，敬请期待。
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关于Params、FLOPs、Inference speed等信息，敬请期待。
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