@@ -9,7 +9,7 @@ PaddleClas is an image recognition toolset for industry and academia, helping us
**Recent updates**
- 2021.06.29 Add Swin-transformer series model，Highest top1 acc on ImageNet1k dataset reaches 87.2%, training, evaluation and inference are all supported. Pretrained models can be downloaded [here](docs/en/models/models_intro_en.md).
- 2021.06.16 PaddleClas release/2.2. Add metric learning and vector search modules. Add product recognition, animation character recognition, vehicle recognition and logo recognition. Added 24 pretrained models of LeViT, TNT, DLA, HarDNet, and RedNet, and the accuracy is roughly the same as that of the paper.
- 2021.06.16 PaddleClas release/2.2. Add metric learning and vector search modules. Add product recognition, animation character recognition, vehicle recognition and logo recognition. Added 30 pretrained models of LeViT, Twins, TNT, DLA, HarDNet, and RedNet, and the accuracy is roughly the same as that of the paper.
-[more](./docs/en/update_history_en.md)
## Features
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@@ -17,7 +17,7 @@ PaddleClas is an image recognition toolset for industry and academia, helping us
- 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.
- Rich library of pre-trained models: Provide a total of 164 ImageNet pre-trained models in 35 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.
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@@ -51,7 +51,7 @@ Quick experience of image recognition：[Link](./docs/en/tutorials/quick_start_r
-[Introduction to Image Recognition Systems](#Introduction_to_Image_Recognition_Systems)
-[Demo images](#Demo_images)
- Algorithms Introduction
-[Backbone Network and Pre-trained Model Library](./docs/en/ImageNet_models.md)
-[Backbone Network and Pre-trained Model Library](./docs/en/ImageNet_models_en.md)
Based on the ImageNet-1k classification dataset, the 35 classification network structures supported by PaddleClas and the corresponding 164 image classification pretrained models are shown below. Training trick, a brief introduction to each series of network structures, and performance evaluation will be shown in the corresponding chapters. The evaluation environment is as follows.
* CPU evaluation environment is based on Snapdragon 855 (SD855).
* The GPU evaluation speed is measured by running 500 times under the FP32+TensorRT configuration (excluding the warmup time of the first 10 times).
Curves of accuracy to the inference time of common server-side models are shown as follows.
Curves of accuracy to the inference time and storage size of common mobile-side models are shown as follows.
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### SSLD pretrained models
Accuracy and inference time of the prtrained models based on SSLD distillation are as follows. More detailed information can be refered to [SSLD distillation tutorial](../en/advanced_tutorials/distillation/distillation_en.md).
* Note: `Reference Top-1 Acc` means accuracy of pretrained models which are trained on ImageNet1k dataset.
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### ResNet and Vd series
Accuracy and inference time metrics of ResNet and Vd series models are shown as follows. More detailed information can be refered to [ResNet and Vd series tutorial](../en/models/ResNet_and_vd_en.md).
Accuracy and inference time metrics of Mobile series models are shown as follows. More detailed information can be refered to [Mobile series tutorial](../en/models/Mobile_en.md).
Accuracy and inference time metrics of SEResNeXt and Res2Net series models are shown as follows. More detailed information can be refered to [SEResNext and_Res2Net series tutorial](../en/models/SEResNext_and_Res2Net_en.md).
Accuracy and inference time metrics of DPN and DenseNet series models are shown as follows. More detailed information can be refered to [DPN and DenseNet series tutorial](../en/models/DPN_DenseNet_en.md).
Accuracy and inference time metrics of HRNet series models are shown as follows. More detailed information can be refered to [Mobile series tutorial](../en/models/HRNet_en.md).
Accuracy and inference time metrics of Inception series models are shown as follows. More detailed information can be refered to [Inception series tutorial](../en/models/Inception_en.md).
Accuracy and inference time metrics of EfficientNet and ResNeXt101_wsl series models are shown as follows. More detailed information can be refered to [EfficientNet and ResNeXt101_wsl series tutorial](../en/models/EfficientNet_and_ResNeXt101_wsl_en.md).
Accuracy and inference time metrics of ResNeSt and RegNet series models are shown as follows. More detailed information can be refered to [ResNeSt and RegNet series tutorial](../en/models/ResNeSt_RegNet_en.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](../en/models/ViT_and_DeiT_en.md).
Accuracy and inference time metrics of RepVGG series models are shown as follows. More detailed information can be refered to [RepVGG series tutorial](../en/models/RepVGG_en.md).
Accuracy and inference time metrics of MixNet series models are shown as follows. More detailed information can be refered to [MixNet series tutorial](../en/models/MixNet_en.md).
Accuracy and inference time metrics of ReXNet series models are shown as follows. More detailed information can be refered to [ReXNet series tutorial](../en/models/ReXNet_en.md).
Accuracy and inference time metrics of SwinTransformer series models are shown as follows. More detailed information can be refered to[SwinTransformer series tutorial](../en/models/SwinTransformer_en.md).
[1] Based on the pre-trained model of the ImageNet22k dataset, it is obtained by finetuning from the ImageNet1k data set.
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### LeViT
Accuracy and inference time metrics of LeViT series models are shown as follows. More detailed information can be refered to[LeViT series tutorial](../en/models/LeViT_en.md).
**Note**：The difference in accuracy from Reference is due to the difference in data preprocessing and the absence of distilled head as output.
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### Twins
Accuracy and inference time metrics of Twins series models are shown as follows. More detailed information can be refered to[Twins series tutorial](../en/models/Twins_en.md).
**Note**：The difference in accuracy from Reference is due to the difference in data preprocessing.
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### HarDNet
Accuracy and inference time metrics of HarDNet series models are shown as follows. More detailed information can be refered to[HarDNet series tutorial](../en/models/HarDNet_en.md).
Accuracy and inference time metrics of DLA series models are shown as follows. More detailed information can be refered to[DLA series tutorial](../en/models/DLA_en.md).
Accuracy and inference time metrics of RedNet series models are shown as follows. More detailed information can be refered to[RedNet series tutorial](../en/models/RedNet_en.md).
Accuracy and inference time metrics of TNT series models are shown as follows. More detailed information can be refered to[TNT series tutorial](../en/models/TNT_en.md).
**Note**：The `mean` and `std` in `NormalizeImage` in the data preprocessing part of the TNT model are both 0.5.
### Others
Accuracy and inference time metrics of AlexNet, SqueezeNet series, VGG series and DarkNet53 models are shown as follows. More detailed information can be refered to [Others](../en/models/Others_en.md).
