The image classification models currently supported in models are listed in the table,and the top-1/top-5 accuracy on the imagenet-2012 validation set of the models and the inference time of Paddle Fluid and Paddle TensorRT based on dynamic link library(test GPU model: Tesla P4) are given. As the activation function swish used by ShuffleNetV2 and the activation function relu6 used by MobileNetV2 are not supported by Paddle TensorRT, inference acceleration is not obvious. Paddle TensorRT will support both op soon. The inference method based on dynamic link library will be also released soon,The inference speed indicator may be updated with the official released tool. Pretrained models can be downloaded by clicking related model names.
Available top-1/top-5 validation accuracy on ImageNet 2012 are listed in table. Pretrained models can be downloaded by clicking related model names.Among them, ResNet50_vd_v2 is the distilled version of ResNet50_vd.
- Note1: ResNet50_vd_v2 is the distilled version of ResNet50_vd.
- Released models: specify parameter names
- Note2:In addition to the input image resolution 299x299 adopted by InceptionV4, the resolution used by other models is 224x224.
- Note3: Calling dynamic link library to infer requires converting the train model to a binary model. The conversion method is as follows: a. Set the save_inference parameter in infer.py to True; b. Execute infer.py