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# 模型库概览
## 概述
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基于ImageNet1k分类数据集, PaddleClas支持的36种系列分类网络结构以及对应的175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。
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## 评估环境
* CPU的评估环境基于骁龙855( SD855) 。
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* Intel CPU的评估环境基于Intel(R) Xeon(R) Gold 6148。
* GPU评估环境基于V100和TensorRT。
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> 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](https://github.com/PaddlePaddle/PaddleClas)
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## 预训练模型列表及下载地址
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- ResNet及其Vd系列
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- ResNet系列< sup > [[1](#ref1)]</ sup > ([论文地址](http://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html))
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- [ResNet18 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet18_pretrained.pdparams )
- [ResNet34 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet34_pretrained.pdparams )
- [ResNet50 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_pretrained.pdparams )
- [ResNet101 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet101_pretrained.pdparams )
- [ResNet152 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet152_pretrained.pdparams )
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- ResNet_vc、ResNet_vd系列< sup > [[2](#ref2)]</ sup > ([论文地址](https://arxiv.org/abs/1812.01187))
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- [ResNet50_vc ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vc_pretrained.pdparams )
- [ResNet18_vd ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet18_vd_pretrained.pdparams )
- [ResNet34_vd ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet34_vd_pretrained.pdparams )
- [ResNet34_vd_ssld ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet34_vd_ssld_pretrained.pdparams )
- [ResNet50_vd ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_pretrained.pdparams )
- [ResNet50_vd_v2 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_v2_pretrained.pdparams )
- [ResNet101_vd ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet101_vd_pretrained.pdparams )
- [ResNet152_vd ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet152_vd_pretrained.pdparams )
- [ResNet200_vd ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet200_vd_pretrained.pdparams )
- [ResNet50_vd_ssld ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_pretrained.pdparams )
- [Fix_ResNet50_vd_ssld_v2 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Fix_ResNet50_vd_ssld_v2_pretrained.pdparams )
- [ResNet101_vd_ssld ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet101_vd_ssld_pretrained.pdparams )
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- 轻量级模型系列
- PP-LCNet系列< sup > [[28](#28)]</ sup > ([论文地址](https://arxiv.org/pdf/2109.15099.pdf))
- [PPLCNet_x0_25 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_25_pretrained.pdparams )
- [PPLCNet_x0_35 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_35_pretrained.pdparams )
- [PPLCNet_x0_5 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_5_pretrained.pdparams )
- [PPLCNet_x0_75 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_75_pretrained.pdparams )
- [PPLCNet_x1_0 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_0_pretrained.pdparams )
- [PPLCNet_x1_5 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_5_pretrained.pdparams )
- [PPLCNet_x2_0 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_0_pretrained.pdparams )
- [PPLCNet_x2_5 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_5_pretrained.pdparams )
- [PPLCNet_x0_5_ssld ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_5__ssld_pretrained.pdparams )
- [PPLCNet_x1_0_ssld ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_0_ssld_pretrained.pdparams )
- [PPLCNet_x2_5_ssld ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_5__ssld_pretrained.pdparams )
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- MobileNetV3系列< sup > [[3](#ref3)]</ sup > ([论文地址](https://arxiv.org/abs/1905.02244))
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- [MobileNetV3_large_x0_35 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_35_pretrained.pdparams )
- [MobileNetV3_large_x0_5 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams )
- [MobileNetV3_large_x0_75 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_75_pretrained.pdparams )
- [MobileNetV3_large_x1_0 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x1_0_pretrained.pdparams )
- [MobileNetV3_large_x1_25 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x1_25_pretrained.pdparams )
- [MobileNetV3_small_x0_35 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_small_x0_35_pretrained.pdparams )
- [MobileNetV3_small_x0_5 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_small_x0_5_pretrained.pdparams )
- [MobileNetV3_small_x0_75 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_small_x0_75_pretrained.pdparams )
- [MobileNetV3_small_x1_0 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_small_x1_0_pretrained.pdparams )
- [MobileNetV3_small_x1_25 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_small_x1_25_pretrained.pdparams )
- [MobileNetV3_large_x1_0_ssld ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x1_0_ssld_pretrained.pdparams )
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- [MobileNetV3_large_x1_0_ssld_int8]()(coming soon)
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- [MobileNetV3_small_x1_0_ssld ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_small_x1_0_ssld_pretrained.pdparams )
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- MobileNetV2系列< sup > [[4](#ref4)]</ sup > ([论文地址](https://arxiv.org/abs/1801.04381))
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- [MobileNetV2_x0_25 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x0_25_pretrained.pdparams )
- [MobileNetV2_x0_5 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x0_5_pretrained.pdparams )
- [MobileNetV2_x0_75 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x0_75_pretrained.pdparams )
- [MobileNetV2 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_pretrained.pdparams )
- [MobileNetV2_x1_5 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x1_5_pretrained.pdparams )
- [MobileNetV2_x2_0 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x2_0_pretrained.pdparams )
- [MobileNetV2_ssld ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_ssld_pretrained.pdparams )
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- MobileNetV1系列< sup > [[5](#ref5)]</ sup > ([论文地址](https://arxiv.org/abs/1704.04861))
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- [MobileNetV1_x0_25 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV1_x0_25_pretrained.pdparams )
- [MobileNetV1_x0_5 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV1_x0_5_pretrained.pdparams )
- [MobileNetV1_x0_75 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV1_x0_75_pretrained.pdparams )
- [MobileNetV1 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV1_pretrained.pdparams )
- [MobileNetV1_ssld ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV1_ssld_pretrained.pdparams )
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- ShuffleNetV2系列< sup > [[6](#ref6)]</ sup > ([论文地址](https://arxiv.org/abs/1807.11164))
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- [ShuffleNetV2_x0_25 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_25_pretrained.pdparams )
- [ShuffleNetV2_x0_33 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_33_pretrained.pdparams )
- [ShuffleNetV2_x0_5 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_5_pretrained.pdparams )
- [ShuffleNetV2 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x1_0_pretrained.pdparams )
- [ShuffleNetV2_x1_5 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x1_5_pretrained.pdparams )
- [ShuffleNetV2_x2_0 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x2_0_pretrained.pdparams )
- [ShuffleNetV2_swish ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_swish_pretrained.pdparams )
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- GhostNet系列< sup > [[23](#ref23)]</ sup > ([论文地址](https://arxiv.org/pdf/1911.11907.pdf))
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- [GhostNet_x0_5 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x0_5_pretrained.pdparams )
- [GhostNet_x1_0 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_0_pretrained.pdparams )
- [GhostNet_x1_3 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_3_pretrained.pdparams )
- [GhostNet_x1_3_ssld ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_3_ssld_pretrained.pdparams )
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- MixNet系列< sup > [[29](#ref29)]</ sup > ([论文地址](https://arxiv.org/pdf/1907.09595.pdf))
- [MixNet_S ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MixNet_S_pretrained.pdparams )
- [MixNet_M ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MixNet_M_pretrained.pdparams )
- [MixNet_L ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MixNet_L_pretrained.pdparams )
- ReXNet系列< sup > [[30](#ref30)]</ sup > ([论文地址](https://arxiv.org/pdf/2007.00992.pdf))
- [ReXNet_1_0 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_1_0_pretrained.pdparams )
- [ReXNet_1_3 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_1_3_pretrained.pdparams )
- [ReXNet_1_5 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_1_5_pretrained.pdparams )
- [ReXNet_2_0 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_2_0_pretrained.pdparams )
- [ReXNet_3_0 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_3_0_pretrained.pdparams )
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- SEResNeXt与Res2Net系列
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- ResNeXt系列< sup > [[7](#ref7)]</ sup > ([论文地址](https://arxiv.org/abs/1611.05431))
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- [ResNeXt50_32x4d ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_32x4d_pretrained.pdparams )
- [ResNeXt50_64x4d ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_64x4d_pretrained.pdparams )
- [ResNeXt101_32x4d ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x4d_pretrained.pdparams )
- [ResNeXt101_64x4d ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_64x4d_pretrained.pdparams )
- [ResNeXt152_32x4d ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_32x4d_pretrained.pdparams )
- [ResNeXt152_64x4d ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_64x4d_pretrained.pdparams )
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- ResNeXt_vd系列
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- [ResNeXt50_vd_32x4d ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_vd_32x4d_pretrained.pdparams )
- [ResNeXt50_vd_64x4d ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_vd_64x4d_pretrained.pdparams )
- [ResNeXt101_vd_32x4d ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_vd_32x4d_pretrained.pdparams )
- [ResNeXt101_vd_64x4d ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_vd_64x4d_pretrained.pdparams )
- [ResNeXt152_vd_32x4d ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_vd_32x4d_pretrained.pdparams )
- [ResNeXt152_vd_64x4d ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_vd_64x4d_pretrained.pdparams )
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- SE_ResNet_vd系列< sup > [[8](#ref8)]</ sup > ([论文地址](https://arxiv.org/abs/1709.01507))
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- [SE_ResNet18_vd ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNet18_vd_pretrained.pdparams )
- [SE_ResNet34_vd ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNet34_vd_pretrained.pdparams )
- [SE_ResNet50_vd ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNet50_vd_pretrained.pdparams )
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- SE_ResNeXt系列
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- [SE_ResNeXt50_32x4d ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt50_32x4d_pretrained.pdparams )
- [SE_ResNeXt101_32x4d ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt101_32x4d_pretrained.pdparams )
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- SE_ResNeXt_vd系列
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- [SE_ResNeXt50_vd_32x4d ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt50_vd_32x4d_pretrained.pdparams )
- [SENet154_vd ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SENet154_vd_pretrained.pdparams )
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- Res2Net系列< sup > [[9](#ref9)]</ sup > ([论文地址](https://arxiv.org/abs/1904.01169))
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- [Res2Net50_26w_4s ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_26w_4s_pretrained.pdparams )
- [Res2Net50_vd_26w_4s ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_vd_26w_4s_pretrained.pdparams )
- [Res2Net50_vd_26w_4s_ssld ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_vd_26w_4s_ssld_pretrained.pdparams )
- [Res2Net50_14w_8s ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_14w_8s_pretrained.pdparams )
- [Res2Net101_vd_26w_4s ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net101_vd_26w_4s_pretrained.pdparams )
- [Res2Net101_vd_26w_4s_ssld ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net101_vd_26w_4s_ssld_pretrained.pdparams )
- [Res2Net200_vd_26w_4s ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net200_vd_26w_4s_pretrained.pdparams )
- [Res2Net200_vd_26w_4s_ssld ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net200_vd_26w_4s_ssld_pretrained.pdparams )
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- Inception系列
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- GoogLeNet系列< sup > [[10](#ref10)]</ sup > ([论文地址](https://arxiv.org/pdf/1409.4842.pdf))
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- [GoogLeNet ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GoogLeNet_pretrained.pdparams )
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- InceptionV3系列< sup > [[26](#ref26)]</ sup > ([论文地址](https://arxiv.org/abs/1512.00567))
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- [InceptionV3 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/InceptionV3_pretrained.pdparams )
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- InceptionV4系列< sup > [[11](#ref11)]</ sup > ([论文地址](https://arxiv.org/abs/1602.07261))
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- [InceptionV4 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/InceptionV4_pretrained.pdparams )
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- Xception系列< sup > [[12](#ref12)]</ sup > ([论文地址](http://openaccess.thecvf.com/content_cvpr_2017/html/Chollet_Xception_Deep_Learning_CVPR_2017_paper.html))
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- [Xception41 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception41_pretrained.pdparams )
- [Xception41_deeplab ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception41_deeplab_pretrained.pdparams )
- [Xception65 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception65_pretrained.pdparams )
- [Xception65_deeplab ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception65_deeplab_pretrained.pdparams )
- [Xception71 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception71_pretrained.pdparams )
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- HRNet系列
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- HRNet系列< sup > [[13](#ref13)]</ sup > ([论文地址](https://arxiv.org/abs/1908.07919))
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- [HRNet_W18_C ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W18_C_pretrained.pdparams )
- [HRNet_W18_C_ssld ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W18_C_ssld_pretrained.pdparams )
- [HRNet_W30_C ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W30_C_pretrained.pdparams )
- [HRNet_W32_C ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W32_C_pretrained.pdparams )
- [HRNet_W40_C ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W40_C_pretrained.pdparams )
- [HRNet_W44_C ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W44_C_pretrained.pdparams )
- [HRNet_W48_C ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_pretrained.pdparams )
- [HRNet_W48_C_ssld ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_ssld_pretrained.pdparams )
- [HRNet_W64_C ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W64_C_pretrained.pdparams )
2020-12-06 17:52:34 +08:00
- [SE_HRNet_W64_C_ssld ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_HRNet_W64_C_ssld_pretrained.pdparams )
2020-04-10 00:45:02 +08:00
- DPN与DenseNet系列
2020-04-10 18:36:01 +08:00
- DPN系列< sup > [[14](#ref14)]</ sup > ([论文地址](https://arxiv.org/abs/1707.01629))
2020-11-30 12:43:05 +08:00
- [DPN68 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN68_pretrained.pdparams )
- [DPN92 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN92_pretrained.pdparams )
- [DPN98 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN98_pretrained.pdparams )
- [DPN107 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN107_pretrained.pdparams )
- [DPN131 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN131_pretrained.pdparams )
2020-04-10 18:36:01 +08:00
- DenseNet系列< sup > [[15](#ref15)]</ sup > ([论文地址](https://arxiv.org/abs/1608.06993))
2020-11-30 12:43:05 +08:00
- [DenseNet121 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet121_pretrained.pdparams )
- [DenseNet161 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet161_pretrained.pdparams )
- [DenseNet169 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet169_pretrained.pdparams )
- [DenseNet201 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet201_pretrained.pdparams )
- [DenseNet264 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet264_pretrained.pdparams )
2020-04-10 00:45:02 +08:00
- EfficientNet与ResNeXt101_wsl系列
2020-04-10 18:36:01 +08:00
- EfficientNet系列< sup > [[16](#ref16)]</ sup > ([论文地址](https://arxiv.org/abs/1905.11946))
2020-11-30 12:43:05 +08:00
- [EfficientNetB0_small ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB0_small_pretrained.pdparams )
- [EfficientNetB0 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB0_pretrained.pdparams )
- [EfficientNetB1 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB1_pretrained.pdparams )
- [EfficientNetB2 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB2_pretrained.pdparams )
- [EfficientNetB3 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB3_pretrained.pdparams )
- [EfficientNetB4 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB4_pretrained.pdparams )
- [EfficientNetB5 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB5_pretrained.pdparams )
- [EfficientNetB6 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB6_pretrained.pdparams )
- [EfficientNetB7 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB7_pretrained.pdparams )
2020-04-10 18:36:01 +08:00
- ResNeXt101_wsl系列< sup > [[17](#ref17)]</ sup > ([论文地址](https://arxiv.org/abs/1805.00932))
2020-11-30 12:43:05 +08:00
- [ResNeXt101_32x8d_wsl ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x8d_wsl_pretrained.pdparams )
- [ResNeXt101_32x16d_wsl ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x16d_wsl_pretrained.pdparams )
- [ResNeXt101_32x32d_wsl ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x32d_wsl_pretrained.pdparams )
- [ResNeXt101_32x48d_wsl ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x48d_wsl_pretrained.pdparams )
- [Fix_ResNeXt101_32x48d_wsl ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Fix_ResNeXt101_32x48d_wsl_pretrained.pdparams )
2020-04-10 00:45:02 +08:00
2020-10-19 13:05:34 +08:00
- ResNeSt与RegNet系列
- ResNeSt系列< sup > [[24](#ref24)]</ sup > ([论文地址](https://arxiv.org/abs/2004.08955))
2020-11-30 12:43:05 +08:00
- [ResNeSt50_fast_1s1x64d ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeSt50_fast_1s1x64d_pretrained.pdparams )
- [ResNeSt50 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeSt50_pretrained.pdparams )
2020-10-19 13:05:34 +08:00
- RegNet系列< sup > [[25](#ref25)]</ sup > ([paper link](https://arxiv.org/abs/2003.13678))
2020-11-30 12:43:05 +08:00
- [RegNetX_4GF ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetX_4GF_pretrained.pdparams )
2020-10-19 13:05:34 +08:00
2021-06-29 12:27:57 +08:00
- Transformer系列
- Swin-transformer系列< sup > [[27](#ref27)]</ sup > ([论文地址](https://arxiv.org/pdf/2103.14030.pdf))
- [SwinTransformer_tiny_patch4_window7_224 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_tiny_patch4_window7_224_pretrained.pdparams )
- [SwinTransformer_small_patch4_window7_224 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_small_patch4_window7_224_pretrained.pdparams )
- [SwinTransformer_base_patch4_window7_224 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window7_224_pretrained.pdparams )
- [SwinTransformer_base_patch4_window12_384 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window12_384_pretrained.pdparams )
- [SwinTransformer_base_patch4_window7_224_22k ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window7_224_22k_pretrained.pdparams )
- [SwinTransformer_base_patch4_window7_224_22kto1k ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window7_224_22kto1k_pretrained.pdparams )
- [SwinTransformer_large_patch4_window12_384_22k ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window12_384_22k_pretrained.pdparams )
- [SwinTransformer_large_patch4_window12_384_22kto1k ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window12_384_22kto1k_pretrained.pdparams )
- [SwinTransformer_large_patch4_window7_224_22k ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window7_224_22k_pretrained.pdparams )
- [SwinTransformer_large_patch4_window7_224_22kto1k ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window7_224_22kto1k_pretrained.pdparams )
2021-11-01 00:09:58 +08:00
- ViT系列< sup > [[31](#ref31)]</ sup > ([论文地址](https://arxiv.org/pdf/2010.11929.pdf))
- [ViT_small_patch16_224 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_small_patch16_224_pretrained.pdparams )
- [ViT_base_patch16_224 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch16_224_pretrained.pdparams )
- [ViT_base_patch16_384 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch16_384_pretrained.pdparams )
- [ViT_base_patch32_384 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch32_384_pretrained.pdparams )
- [ViT_large_patch16_224 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch16_224_pretrained.pdparams )
- [ViT_large_patch16_384 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch16_384_pretrained.pdparams )
- [ViT_large_patch32_384 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch32_384_pretrained.pdparams )
- DeiT系列< sup > [[32](#ref32)]</ sup > ([论文地址](https://arxiv.org/pdf/2012.12877.pdf))
- [DeiT_tiny_patch16_224 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_tiny_patch16_224_pretrained.pdparams )
- [DeiT_small_patch16_224 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_small_patch16_224_pretrained.pdparams )
- [DeiT_base_patch16_224 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_patch16_224_pretrained.pdparams )
- [DeiT_base_patch16_384 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_patch16_384_pretrained.pdparams )
- [DeiT_tiny_distilled_patch16_224 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_tiny_distilled_patch16_224_pretrained.pdparams )
- [DeiT_small_distilled_patch16_224 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_small_distilled_patch16_224_pretrained.pdparams )
- [DeiT_base_distilled_patch16_224 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_distilled_patch16_224_pretrained.pdparams )
- [DeiT_base_distilled_patch16_384 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_distilled_patch16_384_pretrained.pdparams )
- LeViT系列< sup > [[33](#ref33)]</ sup > ([论文地址](https://arxiv.org/pdf/2104.01136.pdf))
- [LeViT_128S ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_128S_pretrained.pdparams )
- [LeViT_128 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_128_pretrained.pdparams )
- [LeViT_192 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_192_pretrained.pdparams )
- [LeViT_256 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_256_pretrained.pdparams )
- [LeViT_384 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_384_pretrained.pdparams )
- Twins系列< sup > [[34](#ref43)]</ sup > ([论文地址](https://arxiv.org/pdf/2104.13840.pdf))
- [pcpvt_small ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/pcpvt_small_pretrained.pdparams )
- [pcpvt_base ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/pcpvt_base_pretrained.pdparams )
- [pcpvt_large ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/pcpvt_large_pretrained.pdparams )
- [alt_gvt_small ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/alt_gvt_small_pretrained.pdparams )
- [alt_gvt_base ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/alt_gvt_base_pretrained.pdparams )
- [alt_gvt_large ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/alt_gvt_large_pretrained.pdparams )
- TNT系列< sup > [[35](#ref35)]</ sup > ([论文地址](https://arxiv.org/pdf/2103.00112.pdf))
- [TNT_small ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/TNT_small_pretrained.pdparams )
2020-04-10 00:45:02 +08:00
- 其他模型
2020-04-10 18:36:01 +08:00
- AlexNet系列< sup > [[18](#ref18)]</ sup > ([论文地址](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf))
2020-11-30 12:43:05 +08:00
- [AlexNet ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/AlexNet_pretrained.pdparams )
2020-04-10 18:36:01 +08:00
- SqueezeNet系列< sup > [[19](#ref19)]</ sup > ([论文地址](https://arxiv.org/abs/1602.07360))
2020-11-30 12:43:05 +08:00
- [SqueezeNet1_0 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_0_pretrained.pdparams )
- [SqueezeNet1_1 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_1_pretrained.pdparams )
2020-04-10 18:36:01 +08:00
- VGG系列< sup > [[20](#ref20)]</ sup > ([论文地址](https://arxiv.org/abs/1409.1556))
2020-11-30 12:43:05 +08:00
- [VGG11 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VGG11_pretrained.pdparams )
- [VGG13 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VGG13_pretrained.pdparams )
- [VGG16 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VGG16_pretrained.pdparams )
- [VGG19 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VGG19_pretrained.pdparams )
2020-04-10 18:36:01 +08:00
- DarkNet系列< sup > [[21](#ref21)]</ sup > ([论文地址](https://arxiv.org/abs/1506.02640))
2020-12-12 11:56:36 +08:00
- [DarkNet53 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DarkNet53_pretrained.pdparams )
2021-11-01 00:09:58 +08:00
- RepVGG系列< sup > [[36](#ref36)]</ sup > ([论文地址](https://arxiv.org/pdf/2101.03697.pdf))
- [RepVGG_A0 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A0_pretrained.pdparams )
- [RepVGG_A1 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A1_pretrained.pdparams )
- [RepVGG_A2 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A2_pretrained.pdparams )
- [RepVGG_B0 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B0_pretrained.pdparams )
- [RepVGG_B1s ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1_pretrained.pdparams )
- [RepVGG_B2 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B2_pretrained.pdparams )
- [RepVGG_B1g2 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1g2_pretrained.pdparams )
- [RepVGG_B1g4 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1g4_pretrained.pdparams )
- [RepVGG_B2g4 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B2g4_pretrained.pdparams )
- [RepVGG_B3g4 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B3g4_pretrained.pdparams )
- HarDNet系列< sup > [[37](#ref37)]</ sup > ([论文地址](https://arxiv.org/pdf/1909.00948.pdf))
- [HarDNet39_ds ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet39_ds_pretrained.pdparams )
- [HarDNet68_ds ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet68_ds_pretrained.pdparams )
- [HarDNet68 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet68_pretrained.pdparams )
- [HarDNet85 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet85_pretrained.pdparams )
- DLA系列< sup > [[38](#ref38)]</ sup > ([论文地址](https://arxiv.org/pdf/1707.06484.pdf))
- [DLA102 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA102_pretrained.pdparams )
- [DLA102x2 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA102x2_pretrained.pdparams )
- [DLA102x ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA102x_pretrained.pdparams )
- [DLA169 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA169_pretrained.pdparams )
- [DLA34 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA34_pretrained.pdparams )
- [DLA46_c ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA46_c_pretrained.pdparams )
- [DLA60 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA60_pretrained.pdparams )
- [DLA60x_c ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA60x_c_pretrained.pdparams )
- [DLA60x ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA60x_pretrained.pdparams )
- RedNet系列< sup > [[39](#ref39)]</ sup > ([论文地址](https://arxiv.org/pdf/2103.06255.pdf))
- [RedNet26 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet26_pretrained.pdparams )
- [RedNet38 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet38_pretrained.pdparams )
- [RedNet50 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet50_pretrained.pdparams )
- [RedNet101 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet101_pretrained.pdparams )
- [RedNet152 ](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet152_pretrained.pdparams )
2020-04-10 00:45:02 +08:00
2021-01-12 11:14:01 +08:00
**注意**: 以上模型中EfficientNetB1-B7的预训练模型转自[pytorch版EfficientNet](https://github.com/lukemelas/EfficientNet-PyTorch), ResNeXt101_wsl系列预训练模型转自[官方repo](https://github.com/facebookresearch/WSL-Images), 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。
2020-04-10 00:45:02 +08:00
## 参考文献
2020-04-10 18:36:01 +08:00
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< a name = "ref3" > [3]< / a > Howard A, Sandler M, Chu G, et al. Searching for mobilenetv3[C]//Proceedings of the IEEE International Conference on Computer Vision. 2019: 1314-1324.
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< a name = "ref5" > [5]< / a > Howard A G, Zhu M, Chen B, et al. Mobilenets: Efficient convolutional neural networks for mobile vision applications[J]. arXiv preprint arXiv:1704.04861, 2017.
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< a name = "ref6" > [6]< / a > Ma N, Zhang X, Zheng H T, et al. Shufflenet v2: Practical guidelines for efficient cnn architecture design[C]//Proceedings of the European Conference on Computer Vision (ECCV). 2018: 116-131.
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< a name = "ref7" > [7]< / a > Xie S, Girshick R, Dollár P, et al. Aggregated residual transformations for deep neural networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 1492-1500.
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< a name = "ref8" > [8]< / a > Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 7132-7141.
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< a name = "ref9" > [9]< / a > Gao S, Cheng M M, Zhao K, et al. Res2net: A new multi-scale backbone architecture[J]. IEEE transactions on pattern analysis and machine intelligence, 2019.
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< a name = "ref10" > [10]< / a > Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 1-9.
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< a name = "ref11" > [11]< / a > Szegedy C, Ioffe S, Vanhoucke V, et al. Inception-v4, inception-resnet and the impact of residual connections on learning[C]//Thirty-first AAAI conference on artificial intelligence. 2017.
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< a name = "ref12" > [12]< / a > Chollet F. Xception: Deep learning with depthwise separable convolutions[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 1251-1258.
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