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模型库概览

概述

基于ImageNet1k分类数据集，PaddleClas支持的29种系列分类网络结构以及对应的134个图像分类预训练模型如下所示，训练技巧、每个系列网络结构的简单介绍和性能评估将在相应章节展现。

评估环境

  • CPU的评估环境基于骁龙855（SD855）。
  • GPU评估环境基于V100和TensorRT，评估脚本如下。
#!/usr/bin/env bash

export PYTHONPATH=$PWD:$PYTHONPATH

python tools/infer/predict.py \
    --model_file='pretrained/infer/model' \
    --params_file='pretrained/infer/params' \
    --enable_benchmark=True \
    --model_name=ResNet50_vd \
    --use_tensorrt=True \
    --use_fp16=False \
    --batch_size=1

如果您觉得此文档对您有帮助，欢迎star我们的项目：https://github.com/PaddlePaddle/PaddleClas

预训练模型列表及下载地址

注意：以上模型中EfficientNetB1-B7的预训练模型转自pytorch版EfficientNet，ResNeXt101_wsl系列预训练模型转自官方repo，剩余预训练模型均基于飞桨训练得到的，并在configs里给出了相应的训练超参数。

参考文献

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