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基于ImageNet1k分类数据集,PaddleClas提供ResNet、ResNet_vd、Res2Net、HRNet、MobileNetV3等23种系列的分类网络结构的简单介绍、论文指标复现配置,以及在复现过程中的训练技巧。与此同时,也提供了对应的117个图像分类预训练模型,并且基于TensorRT评估了服务器端模型的GPU预测时间,以及在骁龙855(SD855)上评估了移动端模型的CPU预测时间和存储大小。支持的***预训练模型列表、下载地址以及更多信息***请见文档教程中的[**模型库章节**](https://paddleclas.readthedocs.io/zh_CN/latest/models/models_intro.html)。 基于ImageNet1k分类数据集,PaddleClas提供ResNet、ResNet_vd、Res2Net、HRNet、MobileNetV3等23种系列的分类网络结构的简单介绍、论文指标复现配置,以及在复现过程中的训练技巧。与此同时,也提供了对应的117个图像分类预训练模型,并且基于TensorRT评估了服务器端模型的GPU预测时间,以及在骁龙855(SD855)上评估了移动端模型的CPU预测时间和存储大小。支持的***预训练模型列表、下载地址以及更多信息***请见文档教程中的[**模型库章节**](https://paddleclas.readthedocs.io/zh_CN/latest/models/models_intro.html)。
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<img src="./docs/images/models/T4_benchmark/t4.fp32.bs4.main_fps_top1.png" width="700"> <img src="./docs/images/models/T4_benchmark/V100_benchmark/v100.fp32.bs1.main_fps_top1_s.jpg" width="700">
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上图对比了一些最新的面向服务器端应用场景的模型,在使用V100,FP32和TensorRT,batch size为1时的预测时间及其准确率,图中准确率83.0%的ResNet50_vd_ssld_v2和83.7%的ResNet101_vd_ssld,是采用PaddleClas提供的SSLD知识蒸馏方案训练的模型,其中v2表示在训练时添加了AutoAugment数据增广策略。图中相同颜色和符号的点代表同一系列不同规模的模型。不同模型的简介、FLOPS、Parameters以及详细的GPU预测时间(包括不同batchsize的T4卡预测速度)请参考文档教程中的[**模型库章节**](https://paddleclas.readthedocs.io/zh_CN/latest/models/models_intro.html) 上图对比了一些最新的面向服务器端应用场景的模型,在使用V100,FP32和TensorRT,batch size为1时的预测时间及其准确率,图中准确率83.0%的ResNet50_vd_ssld_v2和83.7%的ResNet101_vd_ssld,是采用PaddleClas提供的SSLD知识蒸馏方案训练的模型,其中v2表示在训练时添加了AutoAugment数据增广策略。图中相同颜色和符号的点代表同一系列不同规模的模型。不同模型的简介、FLOPS、Parameters以及详细的GPU预测时间(包括不同batchsize的T4卡预测速度)请参考文档教程中的[**模型库章节**](https://paddleclas.readthedocs.io/zh_CN/latest/models/models_intro.html)
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...@@ -20,7 +20,7 @@ Based on ImageNet1k dataset, PaddleClas provides 23 series of image classificati ...@@ -20,7 +20,7 @@ Based on ImageNet1k dataset, PaddleClas provides 23 series of image classificati
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<img src="./docs/images/models/T4_benchmark/t4.fp32.bs4.main_fps_top1.png" width="700"> <img src="./docs/images/models/V100_benchmark/v100.fp32.bs1.main_fps_top1_s.jpg" width="700">
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The above figure shows some of the latest server-side pretrained models. It can be seen from the figure that when using V100 GPU with FP32 and TensorRT, the `Top1` accuracy of the ResNet50_vd_ssld pretrained model on ImageNet1k-val dataset is **83.0%** and that of ResNet101_vd_ssld pretrained model is 83.7%. These pretained models are obtained from SSLD knowledge distillation solution provided by PaddleClas. The marks of the same color and symbol in the figure represent models of different model sizes in the same series. For the introduction of different models, FLOPS, Params and detailed GPU inference time (including the inference speed of T4 GPU with different batch size), please refer to the documentation tutorial for more details: [https://paddleclas-en.readthedocs.io/en/latest/models/models_intro_en.html](https://paddleclas-en.readthedocs.io/en/latest/models/models_intro_en.html) The above figure shows some of the latest server-side pretrained models. It can be seen from the figure that when using V100 GPU with FP32 and TensorRT, the `Top1` accuracy of the ResNet50_vd_ssld pretrained model on ImageNet1k-val dataset is **83.0%** and that of ResNet101_vd_ssld pretrained model is 83.7%. These pretained models are obtained from SSLD knowledge distillation solution provided by PaddleClas. The marks of the same color and symbol in the figure represent models of different model sizes in the same series. For the introduction of different models, FLOPS, Params and detailed GPU inference time (including the inference speed of T4 GPU with different batch size), please refer to the documentation tutorial for more details: [https://paddleclas-en.readthedocs.io/en/latest/models/models_intro_en.html](https://paddleclas-en.readthedocs.io/en/latest/models/models_intro_en.html)
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