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.
**Recent update**
- 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%.
@@ -68,6 +69,7 @@ PaddleClas is a toolset for image classification tasks prepared for the industry
-[EfficientNet and ResNeXt101_wsl series](#EfficientNet_and_ResNeXt101_wsl_series)
-[ResNeSt and RegNet series](#ResNeSt_and_RegNet_series)
-[Transformer series](#Transformer)
-[RepVGG series](#RepVGG)
-[Others](#Others)
- 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
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@@ -356,7 +358,7 @@ Accuracy and inference time metrics of ResNeSt and RegNet series models are show
<aname="Transformer"></a>
### Transformer series
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).
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).
@@ -384,6 +386,26 @@ Accuracy and inference time metrics of ViT and DeiT series models are shown as f
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<aname="RepVGG_series"></a>
### RepVGG
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).
