Based on the ImageNet-1k classification dataset, the 24 classification network structures supported by PaddleClas and the corresponding 122 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/Transformer_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 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).
