diff --git a/docs/en/algorithm_introduction/ImageNet_models_en.md b/docs/en/algorithm_introduction/ImageNet_models_en.md index ccc1affbfb401c3ce957de59709e77e3587d7410..c9d0a7270ad74d9586bc087dea87a69502f64fa1 100644 --- a/docs/en/algorithm_introduction/ImageNet_models_en.md +++ b/docs/en/algorithm_introduction/ImageNet_models_en.md @@ -28,6 +28,7 @@ - [21. RedNet series](#21) - [22. TNT series](#22) - [23. Other models](#23) +- [Reference](#reference) @@ -101,7 +102,7 @@ Accuracy and inference time of the prtrained models based on SSLD distillation a -## 3. PP-LCNet series +## 3. PP-LCNet series [[28](#ref28)] The accuracy and speed indicators of the PP-LCNet series models are shown in the following table. For more information about this series of models, please refer to: [PP-LCNet series model documents](../models/PP-LCNet_en.md)。 @@ -118,7 +119,7 @@ The accuracy and speed indicators of the PP-LCNet series models are shown in the -## 4. ResNet series +## 4. ResNet series [[1](#ref1)] The accuracy and speed indicators of ResNet and ResNet_vd series models are shown in the following table. For more information about this series of models, please refer to: [ResNet and ResNet_vd series model documents](../models/ResNet_and_vd_en.md)。 @@ -142,7 +143,7 @@ The accuracy and speed indicators of ResNet and ResNet_vd series models are show -## 5. Mobile series +## 5. Mobile series [[3](#ref3)][[4](#ref4)][[5](#ref5)][[6](#ref6)][[23](#ref23)] The accuracy and speed indicators of the mobile series models are shown in the following table. For more information about this series, please refer to: [Mobile series model documents](../models/Mobile_en.md)。 @@ -191,7 +192,7 @@ The accuracy and speed indicators of the mobile series models are shown in the f -## 6. SEResNeXt and Res2Net series +## 6. SEResNeXt and Res2Net series [[7](#ref7)][[8](#ref8)][[9](#ref9)] The accuracy and speed indicators of the SEResNeXt and Res2Net series models are shown in the following table. For more information about the models of this series, please refer to: [SEResNeXt and Res2Net series model documents](../models/SEResNext_and_Res2Net_en.md). @@ -226,7 +227,7 @@ The accuracy and speed indicators of the SEResNeXt and Res2Net series models are -## 7. DPN and DenseNet series +## 7. DPN and DenseNet series [[14](#ref14)][[15](#ref15)] The accuracy and speed indicators of the DPN and DenseNet series models are shown in the following table. For more information about the models of this series, please refer to: [DPN and DenseNet series model documents](../models/DPN_DenseNet_en.md). @@ -244,11 +245,9 @@ The accuracy and speed indicators of the DPN and DenseNet series models are show | DPN107 | 0.8089 | 0.9532 | 19.46 | 35.62 | 50.22 | 18.38 | 87.13 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN107_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DPN107_infer.tar) | | DPN131 | 0.8070 | 0.9514 | 19.64 | 34.60 | 47.42 | 16.09 | 79.48 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN131_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DPN131_infer.tar) | - - -## 8. HRNet series +## 8. HRNet series [[13](#ref13)] The accuracy and speed indicators of the HRNet series models are shown in the following table. For more information about the models of this series, please refer to: [HRNet series model documents](../models/HRNet_en.md). @@ -268,7 +267,7 @@ The accuracy and speed indicators of the HRNet series models are shown in the fo -## 9. Inception series +## 9. Inception series [[10](#ref10)][[11](#ref11)][[12](#ref12)][[26](#ref26)] The accuracy and speed indicators of the Inception series models are shown in the following table. For more information about this series of models, please refer to: [Inception series model documents](../models/Inception_en.md). @@ -285,7 +284,7 @@ The accuracy and speed indicators of the Inception series models are shown in th -## 10. EfficientNet and ResNeXt101_wsl series +## 10. EfficientNet and ResNeXt101_wsl series [[16](#ref16)][[17](#ref17)] The accuracy and speed indicators of the EfficientNet and ResNeXt101_wsl series models are shown in the following table. For more information about this series of models, please refer to: [EfficientNet and ResNeXt101_wsl series model documents](../models/EfficientNet_and_ResNeXt101_wsl_en.md). @@ -308,7 +307,7 @@ The accuracy and speed indicators of the EfficientNet and ResNeXt101_wsl series -## 11. ResNeSt and RegNet series +## 11. ResNeSt and RegNet series [[24](#ref24)][[25](#ref25)] The accuracy and speed indicators of the ResNeSt and RegNet series models are shown in the following table. For more information about the models of this series, please refer to: [ResNeSt and RegNet series model documents](../models/ResNeSt_RegNet_en.md). @@ -320,11 +319,10 @@ The accuracy and speed indicators of the ResNeSt and RegNet series models are sh -## 12. ViT and DeiT series +## 12. ViT and DeiT series [[31](#ref31)][[32](#ref32)] The accuracy and speed indicators of ViT (Vision Transformer) and DeiT (Data-efficient Image Transformers) series models are shown in the following table. For more information about this series of models, please refer to: [ViT_and_DeiT series model documents](../models/ViT_and_DeiT_en.md). - | Model | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | time(ms)
bs=8 | FLOPs(G) | Params(M) | Pretrained Model Download Address | Inference Model Download Address | |------------------------|-----------|-----------|------------------|------------------|----------|------------------------|------------------------|------------------------|------------------------| | ViT_small_
patch16_224 | 0.7769 | 0.9342 | 3.71 | 9.05 | 16.72 | 9.41 | 48.60 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_small_patch16_224_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_small_patch16_224_infer.tar) | @@ -335,8 +333,6 @@ The accuracy and speed indicators of ViT (Vision Transformer) and DeiT (Data-eff |ViT_large_
patch16_384| 0.8513 | 0.9736 | 39.51 | 152.46 | 304.06 | 174.70 | 304.12 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch16_384_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_large_patch16_384_infer.tar) | |ViT_large_
patch32_384| 0.8153 | 0.9608 | 11.44 | 36.09 | 70.63 | 44.24 | 306.48 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch32_384_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_large_patch32_384_infer.tar) | - - | Model | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | time(ms)
bs=8 | FLOPs(G) | Params(M) | Pretrained Model Download Address | Inference Model Download Address | |------------------------|-----------|-----------|------------------|------------------|----------|------------------------|------------------------|------------------------|------------------------| | DeiT_tiny_
patch16_224 | 0.718 | 0.910 | 3.61 | 3.94 | 6.10 | 1.07 | 5.68 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_tiny_patch16_224_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_tiny_patch16_224_infer.tar) | @@ -350,11 +346,10 @@ The accuracy and speed indicators of ViT (Vision Transformer) and DeiT (Data-eff -## 13. RepVGG series +## 13. RepVGG series [[36](#ref36)] The accuracy and speed indicators of RepVGG series models are shown in the following table. For more introduction, please refer to: [RepVGG series model documents](../models/RepVGG_en.md). - | Model | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | time(ms)
bs=8 | FLOPs(G) | Params(M) | Pretrained Model Download Address | Inference Model Download Address | |------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------| | RepVGG_A0 | 0.7131 | 0.9016 | | | | 1.36 | 8.31 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A0_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_A0_infer.tar) | @@ -370,7 +365,7 @@ The accuracy and speed indicators of RepVGG series models are shown in the follo -## 14. MixNet series +## 14. MixNet series [[29](#ref29)] The accuracy and speed indicators of the MixNet series models are shown in the following table. For more introduction, please refer to: [MixNet series model documents](../models/MixNet_en.md). @@ -382,7 +377,7 @@ The accuracy and speed indicators of the MixNet series models are shown in the f -## 15. ReXNet series +## 15. ReXNet series [[30](#ref30)] The accuracy and speed indicators of ReXNet series models are shown in the following table. For more introduction, please refer to: [ReXNet series model documents](../models/ReXNet_en.md). @@ -396,7 +391,7 @@ The accuracy and speed indicators of ReXNet series models are shown in the follo -## 16. SwinTransformer series +## 16. SwinTransformer series [[27](#ref27)] The accuracy and speed indicators of SwinTransformer series models are shown in the following table. For more introduction, please refer to: [SwinTransformer series model documents](../models/SwinTransformer_en.md). @@ -415,7 +410,7 @@ The accuracy and speed indicators of SwinTransformer series models are shown in -## 17. LeViT series +## 17. LeViT series [[33](#ref33)] The accuracy and speed indicators of LeViT series models are shown in the following table. For more introduction, please refer to: [LeViT series model documents](../models/LeViT_en.md). @@ -431,7 +426,7 @@ The accuracy and speed indicators of LeViT series models are shown in the follow -## 18. Twins series +## 18. Twins series [[34](#ref34)] The accuracy and speed indicators of Twins series models are shown in the following table. For more introduction, please refer to: [Twins series model documents](../models/Twins_en.md). @@ -448,7 +443,7 @@ The accuracy and speed indicators of Twins series models are shown in the follow -## 19. HarDNet series +## 19. HarDNet series [[37](#ref37)] The accuracy and speed indicators of HarDNet series models are shown in the following table. For more introduction, please refer to: [HarDNet series model documents](../models/HarDNet_en.md). @@ -461,7 +456,7 @@ The accuracy and speed indicators of HarDNet series models are shown in the foll -## 20. DLA series +## 20. DLA series [[38](#ref38)] The accuracy and speed indicators of DLA series models are shown in the following table. For more introduction, please refer to: [DLA series model documents](../models/DLA_en.md). @@ -479,7 +474,7 @@ The accuracy and speed indicators of DLA series models are shown in the followin -## 21. RedNet series +## 21. RedNet series [[39](#ref39)] The accuracy and speed indicators of RedNet series models are shown in the following table. For more introduction, please refer to: [RedNet series model documents](../models/RedNet_en.md). @@ -493,7 +488,7 @@ The accuracy and speed indicators of RedNet series models are shown in the follo -## 22. TNT series +## 22. TNT series [[35](#ref35)] The accuracy and speed indicators of TNT series models are shown in the following table. For more introduction, please refer to: [TNT series model documents](../models/TNT_en.md). @@ -507,7 +502,7 @@ The accuracy and speed indicators of TNT series models are shown in the followin ## 23. Other models -The accuracy and speed indicators of AlexNet, SqueezeNet series, VGG series, DarkNet53 and other models are shown in the following table. For more information, please refer to: [Other model documents](../models/Others_en.md). +The accuracy and speed indicators of AlexNet [[18](#ref18)], SqueezeNet series [[19](#ref19)], VGG series [[20](#ref20)], DarkNet53 [[21](#ref21)] and other models are shown in the following table. For more information, please refer to: [Other model documents](../models/Others_en.md). | Model | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | time(ms)
bs=8 | FLOPs(G) | Params(M) | Pretrained Model Download Address | Inference Model Download Address | |------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------| @@ -519,3 +514,86 @@ The accuracy and speed indicators of AlexNet, SqueezeNet series, VGG series, Dar | VGG16 | 0.720 | 0.907 | 2.48 | 6.79 | 12.33 | 15.470 | 138.35 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG16_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/VGG16_infer.tar) | | VGG19 | 0.726 | 0.909 | 2.93 | 8.28 | 15.21 | 19.63 | 143.66 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG19_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/VGG19_infer.tar) | | DarkNet53 | 0.780 | 0.941 | 2.79 | 6.42 | 10.89 | 9.31 | 41.65 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DarkNet53_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DarkNet53_infer.tar) | + + + +## Reference + +[1] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778. + +[2] He T, Zhang Z, Zhang H, et al. Bag of tricks for image classification with convolutional neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019: 558-567. + +[3] Howard A, Sandler M, Chu G, et al. Searching for mobilenetv3[C]//Proceedings of the IEEE International Conference on Computer Vision. 2019: 1314-1324. + +[4] Sandler M, Howard A, Zhu M, et al. Mobilenetv2: Inverted residuals and linear bottlenecks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 4510-4520. + +[5] 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. + +[6] 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. + +[7] 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. + +[8] 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. + +[9] 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. + +[10] 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. + +[11] 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. + +[12] Chollet F. Xception: Deep learning with depthwise separable convolutions[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 1251-1258. + +[13] Wang J, Sun K, Cheng T, et al. Deep high-resolution representation learning for visual recognition[J]. arXiv preprint arXiv:1908.07919, 2019. + +[14] Chen Y, Li J, Xiao H, et al. Dual path networks[C]//Advances in neural information processing systems. 2017: 4467-4475. + +[15] Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 4700-4708. + +[16] Tan M, Le Q V. Efficientnet: Rethinking model scaling for convolutional neural networks[J]. arXiv preprint arXiv:1905.11946, 2019. + +[17] Mahajan D, Girshick R, Ramanathan V, et al. Exploring the limits of weakly supervised pretraining[C]//Proceedings of the European Conference on Computer Vision (ECCV). 2018: 181-196. + +[18] Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[C]//Advances in neural information processing systems. 2012: 1097-1105. + +[19] Iandola F N, Han S, Moskewicz M W, et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size[J]. arXiv preprint arXiv:1602.07360, 2016. + +[20] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv:1409.1556, 2014. + +[21] Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 779-788. + +[22] Ding X, Guo Y, Ding G, et al. Acnet: Strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks[C]//Proceedings of the IEEE International Conference on Computer Vision. 2019: 1911-1920. + +[23] Han K, Wang Y, Tian Q, et al. GhostNet: More features from cheap operations[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020: 1580-1589. + +[24] Zhang H, Wu C, Zhang Z, et al. Resnest: Split-attention networks[J]. arXiv preprint arXiv:2004.08955, 2020. + +[25] Radosavovic I, Kosaraju R P, Girshick R, et al. Designing network design spaces[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020: 10428-10436. + +[26] C.Szegedy, V.Vanhoucke, S.Ioffe, J.Shlens, and Z.Wojna. Rethinking the inception architecture for computer vision. arXiv preprint arXiv:1512.00567, 2015. + +[27] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin and Baining Guo. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. + +[28]Cheng Cui, Tingquan Gao, Shengyu Wei, Yuning Du, Ruoyu Guo, Shuilong Dong, Bin Lu, Ying Zhou, Xueying Lv, Qiwen Liu, Xiaoguang Hu, Dianhai Yu, Yanjun Ma. PP-LCNet: A Lightweight CPU Convolutional Neural Network. + +[29]Mingxing Tan, Quoc V. Le. MixConv: Mixed Depthwise Convolutional Kernels. + +[30]Dongyoon Han, Sangdoo Yun, Byeongho Heo, YoungJoon Yoo. Rethinking Channel Dimensions for Efficient Model Design. + +[31]Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby. AN IMAGE IS WORTH 16X16 WORDS: +TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE. + +[32]Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Herve Jegou. Training data-efficient image transformers & distillation through attention. + +[33]Benjamin Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Herve Jegou, Matthijs Douze. LeViT: a Vision Transformer in ConvNet’s Clothing for Faster Inference. + +[34]Xiangxiang Chu, Zhi Tian, Yuqing Wang, Bo Zhang, Haibing Ren, Xiaolin Wei, Huaxia Xia, Chunhua Shen. Twins: Revisiting the Design of Spatial Attention in Vision Transformers. + +[35]Kai Han, An Xiao, Enhua Wu, Jianyuan Guo, Chunjing Xu, Yunhe Wang. Transformer in Transformer. + +[36]Xiaohan Ding, Xiangyu Zhang, Ningning Ma, Jungong Han, Guiguang Ding, Jian Sun. RepVGG: Making VGG-style ConvNets Great Again. + +[37]Ping Chao, Chao-Yang Kao, Yu-Shan Ruan, Chien-Hsiang Huang, Youn-Long Lin. HarDNet: A Low Memory Traffic Network. + +[38]Fisher Yu, Dequan Wang, Evan Shelhamer, Trevor Darrell. Deep Layer Aggregation. + +[39]Duo Lim Jie Hu, Changhu Wang, Xiangtai Li, Qi She, Lei Zhu, Tong Zhang, Qifeng Chen. Involution: Inverting the Inherence of Convolution for Visual Recognition. diff --git a/docs/en/models/models_intro_en.md b/docs/en/models/models_intro_en.md deleted file mode 100644 index 6a256f82f017d02ab31f01d6d77f5902a9303dd6..0000000000000000000000000000000000000000 --- a/docs/en/models/models_intro_en.md +++ /dev/null @@ -1,304 +0,0 @@ -# Model Library Overview - -## Overview - -Based on the ImageNet1k classification dataset, the 29 classification network structures supported by PaddleClas and the corresponding 134 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. - -## Evaluation environment -* Arm evaluation environment is based on Snapdragon 855 (SD855). -* The GPU evaluation environment is based on V100 and TensorRT, and the evaluation script is as follows. - -```shell -#!/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 -``` - - -![](../../images/models/V100_benchmark/v100.fp32.bs1.main_fps_top1_s.png) - - -![](../../images/models/mobile_arm_top1.png) - - -![](../../images/models/V100_benchmark/v100.fp32.bs1.visiontransformer.png) - - -> If you think this document is helpful to you, welcome to give a star to our project:[https://github.com/PaddlePaddle/PaddleClas](https://github.com/PaddlePaddle/PaddleClas) - - -## Pretrained model list and download address -- ResNet and ResNet_vd series - - ResNet series[[1](#ref1)]([paper link](http://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html)) - - [ResNet18](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet18_pretrained.pdparams) - - [ResNet34](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_pretrained.pdparams) - - [ResNet50](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_pretrained.pdparams) - - [ResNet101](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_pretrained.pdparams) - - [ResNet152](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet152_pretrained.pdparams) - - ResNet_vc、ResNet_vd series[[2](#ref2)]([paper link](https://arxiv.org/abs/1812.01187)) - - [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/legendary_models/ResNet18_vd_pretrained.pdparams) - - [ResNet34_vd](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_vd_pretrained.pdparams) - - [ResNet34_vd_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_vd_ssld_pretrained.pdparams) - - [ResNet50_vd](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_pretrained.pdparams) - - [ResNet101_vd](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_vd_pretrained.pdparams) - - [ResNet152_vd](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet152_vd_pretrained.pdparams) - - [ResNet200_vd](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet200_vd_pretrained.pdparams) - - [ResNet50_vd_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_ssld_pretrained.pdparams) - - [ResNet50_vd_ssld_v2](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_v2_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/legendary_models/ResNet101_vd_ssld_pretrained.pdparams) - - -- Mobile and Embedded Vision Applications Network series - - MobileNetV3 series[[3](#ref3)]([paper link](https://arxiv.org/abs/1905.02244)) - - [MobileNetV3_large_x0_35](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x0_35_pretrained.pdparams) - - [MobileNetV3_large_x0_5](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x0_5_pretrained.pdparams) - - [MobileNetV3_large_x0_75](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x0_75_pretrained.pdparams) - - [MobileNetV3_large_x1_0](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_0_pretrained.pdparams) - - [MobileNetV3_large_x1_25](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_25_pretrained.pdparams) - - [MobileNetV3_small_x0_35](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_35_pretrained.pdparams) - - [MobileNetV3_small_x0_5](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_5_pretrained.pdparams) - - [MobileNetV3_small_x0_75](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_75_pretrained.pdparams) - - [MobileNetV3_small_x1_0](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_0_pretrained.pdparams) - - [MobileNetV3_small_x1_25](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_25_pretrained.pdparams) - - [MobileNetV3_large_x1_0_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_0_ssld_pretrained.pdparams) - - [MobileNetV3_large_x1_0_ssld_int8]()(coming soon) - - [MobileNetV3_small_x1_0_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_0_ssld_pretrained.pdparams) - - MobileNetV2 series[[4](#ref4)]([paper link](https://arxiv.org/abs/1801.04381)) - - [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) - - MobileNetV1 series[[5](#ref5)]([paper link](https://arxiv.org/abs/1704.04861)) - - [MobileNetV1_x0_25](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_25_pretrained.pdparams) - - [MobileNetV1_x0_5](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_5_pretrained.pdparams) - - [MobileNetV1_x0_75](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_75_pretrained.pdparams) - - [MobileNetV1](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_pretrained.pdparams) - - [MobileNetV1_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_ssld_pretrained.pdparams) - - ShuffleNetV2 series[[6](#ref6)]([paper link](https://arxiv.org/abs/1807.11164)) - - [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) - - GhostNet series[[23](#ref23)]([paper link](https://arxiv.org/pdf/1911.11907.pdf)) - - [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) - - -- SEResNeXt and Res2Net series - - ResNeXt series[[7](#ref7)]([paper link](https://arxiv.org/abs/1611.05431)) - - [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) - - ResNeXt_vd series - - [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) - - SE_ResNet_vd series[[8](#ref8)]([paper link](https://arxiv.org/abs/1709.01507)) - - [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) - - SE_ResNeXt series - - [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) - - SE_ResNeXt_vd series - - [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) - - Res2Net series[[9](#ref9)]([paper link](https://arxiv.org/abs/1904.01169)) - - [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) - - -- Inception series - - GoogLeNet series[[10](#ref10)]([paper link](https://arxiv.org/pdf/1409.4842.pdf)) - - [GoogLeNet](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GoogLeNet_pretrained.pdparams) - - InceptionV3 series[[26](#ref26)]([paper link](https://arxiv.org/abs/1512.00567)) - - [InceptionV3](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/InceptionV3_pretrained.pdparams) - - InceptionV4 series[[11](#ref11)]([paper link](https://arxiv.org/abs/1602.07261)) - - [InceptionV4](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/InceptionV4_pretrained.pdparams) - - Xception series[[12](#ref12)]([paper link](http://openaccess.thecvf.com/content_cvpr_2017/html/Chollet_Xception_Deep_Learning_CVPR_2017_paper.html)) - - [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) - - -- HRNet series - - HRNet series[[13](#ref13)]([paper link](https://arxiv.org/abs/1908.07919)) - - [HRNet_W18_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W18_C_pretrained.pdparams) - - [HRNet_W18_C_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W18_C_ssld_pretrained.pdparams) - - [HRNet_W30_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W30_C_pretrained.pdparams) - - [HRNet_W32_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W32_C_pretrained.pdparams) - - [HRNet_W40_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W40_C_pretrained.pdparams) - - [HRNet_W44_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W44_C_pretrained.pdparams) - - [HRNet_W48_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W48_C_pretrained.pdparams) - - [HRNet_W48_C_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W48_C_ssld_pretrained.pdparams) - - [HRNet_W64_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W64_C_pretrained.pdparams) - - [SE_HRNet_W64_C_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SE_HRNet_W64_C_ssld_pretrained.pdparams) - - -- DPN and DenseNet series - - DPN series[[14](#ref14)]([paper link](https://arxiv.org/abs/1707.01629)) - - [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) - - DenseNet series[[15](#ref15)]([paper link](https://arxiv.org/abs/1608.06993)) - - [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) - - -- EfficientNet and ResNeXt101_wsl series - - EfficientNet series[[16](#ref16)]([paper link](https://arxiv.org/abs/1905.11946)) - - [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) - - ResNeXt101_wsl series[[17](#ref17)]([paper link](https://arxiv.org/abs/1805.00932)) - - [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) - - - -- ResNeSt and RegNet series - - ResNeSt series[[24](#ref24)]([paper link](https://arxiv.org/abs/2004.08955)) - - [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) - - RegNet series[[25](#ref25)]([paper link](https://arxiv.org/abs/2003.13678)) - - [RegNetX_4GF](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetX_4GF_pretrained.pdparams) - - -- Transformer series - - Swin-transformer series[[27](#ref27)]([paper link](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_22kto1k_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_22kto1k_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_22kto1k_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) - - - -- Other models - - AlexNet series[[18](#ref18)]([paper link](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)) - - [AlexNet](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/AlexNet_pretrained.pdparams) - - SqueezeNet series[[19](#ref19)]([paper link](https://arxiv.org/abs/1602.07360)) - - [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) - - VGG series[[20](#ref20)]([paper link](https://arxiv.org/abs/1409.1556)) - - [VGG11](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG11_pretrained.pdparams) - - [VGG13](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG13_pretrained.pdparams) - - [VGG16](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG16_pretrained.pdparams) - - [VGG19](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG19_pretrained.pdparams) - - DarkNet series[[21](#ref21)]([paper link](https://arxiv.org/abs/1506.02640)) - - [DarkNet53](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DarkNet53_pretrained.pdparams) - -**Note**: The pretrained models of EfficientNetB1-B7 in the above models are transferred from [pytorch version of EfficientNet](https://github.com/lukemelas/EfficientNet-PyTorch), and the ResNeXt101_wsl series of pretrained models are transferred from [Official repo](https://github.com/facebookresearch/WSL-Images), the remaining pretrained models are obtained by training with the PaddlePaddle framework, and the corresponding training hyperparameters are given in configs. - -## References - - -[1] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778. - -[2] He T, Zhang Z, Zhang H, et al. Bag of tricks for image classification with convolutional neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019: 558-567. - -[3] Howard A, Sandler M, Chu G, et al. Searching for mobilenetv3[C]//Proceedings of the IEEE International Conference on Computer Vision. 2019: 1314-1324. - -[4] Sandler M, Howard A, Zhu M, et al. Mobilenetv2: Inverted residuals and linear bottlenecks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 4510-4520. - -[5] 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. - -[6] 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. - -[7] 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. - - -[8] 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. - - -[9] 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. - -[10] 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. - - -[11] 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. - -[12] Chollet F. Xception: Deep learning with depthwise separable convolutions[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 1251-1258. - -[13] Wang J, Sun K, Cheng T, et al. Deep high-resolution representation learning for visual recognition[J]. arXiv preprint arXiv:1908.07919, 2019. - -[14] Chen Y, Li J, Xiao H, et al. Dual path networks[C]//Advances in neural information processing systems. 2017: 4467-4475. - -[15] Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 4700-4708. - - -[16] Tan M, Le Q V. Efficientnet: Rethinking model scaling for convolutional neural networks[J]. arXiv preprint arXiv:1905.11946, 2019. - -[17] Mahajan D, Girshick R, Ramanathan V, et al. Exploring the limits of weakly supervised pretraining[C]//Proceedings of the European Conference on Computer Vision (ECCV). 2018: 181-196. - -[18] Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[C]//Advances in neural information processing systems. 2012: 1097-1105. - -[19] Iandola F N, Han S, Moskewicz M W, et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size[J]. arXiv preprint arXiv:1602.07360, 2016. - -[20] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv:1409.1556, 2014. - -[21] Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 779-788. - -[22] Ding X, Guo Y, Ding G, et al. Acnet: Strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks[C]//Proceedings of the IEEE International Conference on Computer Vision. 2019: 1911-1920. - -[23] Han K, Wang Y, Tian Q, et al. GhostNet: More features from cheap operations[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020: 1580-1589. - -[24] Zhang H, Wu C, Zhang Z, et al. Resnest: Split-attention networks[J]. arXiv preprint arXiv:2004.08955, 2020. - -[25] Radosavovic I, Kosaraju R P, Girshick R, et al. Designing network design spaces[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020: 10428-10436. - -[26] C.Szegedy, V.Vanhoucke, S.Ioffe, J.Shlens, and Z.Wojna. Rethinking the inception architecture for computer vision. arXiv preprint arXiv:1512.00567, 2015. - -[27] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin and Baining Guo. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. diff --git a/docs/zh_CN/algorithm_introduction/ImageNet_models.md b/docs/zh_CN/algorithm_introduction/ImageNet_models.md index 5ec85d3187d2d3dc23233b6062535d37616ca802..a61d882e70aa2dc54adef23c9ec125024233889b 100644 --- a/docs/zh_CN/algorithm_introduction/ImageNet_models.md +++ b/docs/zh_CN/algorithm_introduction/ImageNet_models.md @@ -31,6 +31,7 @@ - [21. RedNet 系列](#21) - [22. TNT 系列](#22) - [23. 其他模型](#23) +- [参考文献](#reference) @@ -98,14 +99,11 @@ | PPLCNet_x1_0_ssld | 0.744 | 0.713 | 0.033 | 2.46 | 160.81 | 2.96 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_0_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x1_0_ssld_infer.tar) | | PPLCNet_x2_5_ssld | 0.808 | 0.766 | 0.042 | 5.39 | 906.49 | 9.04 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_5_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x2_5_ssld_infer.tar) | - - - * 注: `Reference Top-1 Acc` 表示 PaddleClas 基于 ImageNet1k 数据集训练得到的预训练模型精度。 -## 3. PP-LCNet 系列 +## 3. PP-LCNet 系列 [[28](#ref28)] PP-LCNet 系列模型的精度、速度指标如下表所示，更多关于该系列的模型介绍可以参考：[PP-LCNet 系列模型文档](../models/PP-LCNet.md)。 @@ -122,7 +120,7 @@ PP-LCNet 系列模型的精度、速度指标如下表所示，更多关于该 -## 4. ResNet 系列 +## 4. ResNet 系列 [[1](#ref1)] ResNet 及其 Vd 系列模型的精度、速度指标如下表所示，更多关于该系列的模型介绍可以参考：[ResNet 及其 Vd 系列模型文档](../models/ResNet_and_vd.md)。 @@ -146,7 +144,7 @@ ResNet 及其 Vd 系列模型的精度、速度指标如下表所示，更多关 -## 5. 移动端系列 +## 5. 移动端系列 [[3](#ref3)][[4](#ref4)][[5](#ref5)][[6](#ref6)][[23](#ref23)] 移动端系列模型的精度、速度指标如下表所示，更多关于该系列的模型介绍可以参考：[移动端系列模型文档](../models/Mobile.md)。 @@ -195,7 +193,7 @@ ResNet 及其 Vd 系列模型的精度、速度指标如下表所示，更多关 -## 6. SEResNeXt 与 Res2Net 系列 +## 6. SEResNeXt 与 Res2Net 系列 [[7](#ref7)][[8](#ref8)][[9](#ref9)] SEResNeXt 与 Res2Net 系列模型的精度、速度指标如下表所示，更多关于该系列的模型介绍可以参考：[SEResNeXt 与 Res2Net 系列模型文档](../models/SEResNext_and_Res2Net.md)。 @@ -230,7 +228,7 @@ SEResNeXt 与 Res2Net 系列模型的精度、速度指标如下表所示，更 -## 7. DPN 与 DenseNet 系列 +## 7. DPN 与 DenseNet 系列 [[14](#ref14)][[15](#ref15)] DPN 与 DenseNet 系列模型的精度、速度指标如下表所示，更多关于该系列的模型介绍可以参考：[DPN 与 DenseNet 系列模型文档](../models/DPN_DenseNet.md)。 @@ -248,15 +246,12 @@ DPN 与 DenseNet 系列模型的精度、速度指标如下表所示，更多关 | DPN107 | 0.8089 | 0.9532 | 19.46 | 35.62 | 50.22 | 18.38 | 87.13 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN107_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DPN107_infer.tar) | | DPN131 | 0.8070 | 0.9514 | 19.64 | 34.60 | 47.42 | 16.09 | 79.48 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN131_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DPN131_infer.tar) | - - -## 8. HRNet 系列 +## 8. HRNet 系列 [[13](#ref13)] HRNet 系列模型的精度、速度指标如下表所示，更多关于该系列的模型介绍可以参考：[HRNet 系列模型文档](../models/HRNet.md)。 - | 模型 | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | time(ms)
bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 | |-------------|-----------|-----------|------------------|------------------|----------|-----------|--------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------| | HRNet_W18_C | 0.7692 | 0.9339 | 6.66 | 8.94 | 11.95 | 4.32 | 21.35 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W18_C_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HRNet_W18_C_infer.tar) | @@ -272,7 +267,7 @@ HRNet 系列模型的精度、速度指标如下表所示，更多关于该系 -## 9. Inception 系列 +## 9. Inception 系列 [[10](#ref10)][[11](#ref11)][[12](#ref12)][[26](#ref26)] Inception 系列模型的精度、速度指标如下表所示，更多关于该系列的模型介绍可以参考：[Inception 系列模型文档](../models/Inception.md)。 @@ -289,11 +284,10 @@ Inception 系列模型的精度、速度指标如下表所示，更多关于该 -## 10. EfficientNet 与 ResNeXt101_wsl 系列 +## 10. EfficientNet 与 ResNeXt101_wsl 系列 [[16](#ref16)][[17](#ref17)] EfficientNet 与 ResNeXt101_wsl 系列模型的精度、速度指标如下表所示，更多关于该系列的模型介绍可以参考：[EfficientNet 与 ResNeXt101_wsl 系列模型文档](../models/EfficientNet_and_ResNeXt101_wsl.md)。 - | 模型 | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | time(ms)
bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 | |---------------------------|-----------|-----------|------------------|------------------|----------|-----------|----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------| | ResNeXt101_
32x8d_wsl | 0.8255 | 0.9674 | 13.55 | 23.39 | 36.18 | 16.48 | 88.99 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x8d_wsl_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt101_32x8d_wsl_infer.tar) | @@ -313,11 +307,10 @@ EfficientNet 与 ResNeXt101_wsl 系列模型的精度、速度指标如下表所 -## 11. ResNeSt 与 RegNet 系列 +## 11. ResNeSt 与 RegNet 系列 [[24](#ref24)][[25](#ref25)] ResNeSt 与 RegNet 系列模型的精度、速度指标如下表所示，更多关于该系列的模型介绍可以参考：[ResNeSt 与 RegNet 系列模型文档](../models/ResNeSt_RegNet.md)。 - | 模型 | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | time(ms)
bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 | |------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------| | ResNeSt50_
fast_1s1x64d | 0.8035 | 0.9528 | 2.73 | 5.33 | 8.24 | 4.36 | 26.27 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeSt50_fast_1s1x64d_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeSt50_fast_1s1x64d_infer.tar) | @@ -326,11 +319,10 @@ ResNeSt 与 RegNet 系列模型的精度、速度指标如下表所示，更多 -## 12. ViT_and_DeiT 系列 +## 12. ViT_and_DeiT 系列 [[31](#ref31)][[32](#ref32)] ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模型的精度、速度指标如下表所示. 更多关于该系列模型的介绍可以参考： [ViT_and_DeiT 系列模型文档](../models/ViT_and_DeiT.md)。 - | 模型 | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | time(ms)
bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 | |------------------------|-----------|-----------|------------------|------------------|----------|------------------------|------------------------|------------------------|------------------------| | ViT_small_
patch16_224 | 0.7769 | 0.9342 | 3.71 | 9.05 | 16.72 | 9.41 | 48.60 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_small_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_small_patch16_224_infer.tar) | @@ -341,8 +333,6 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模 |ViT_large_
patch16_384| 0.8513 | 0.9736 | 39.51 | 152.46 | 304.06 | 174.70 | 304.12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch16_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_large_patch16_384_infer.tar) | |ViT_large_
patch32_384| 0.8153 | 0.9608 | 11.44 | 36.09 | 70.63 | 44.24 | 306.48 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch32_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_large_patch32_384_infer.tar) | - - | 模型 | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | time(ms)
bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 | |------------------------|-----------|-----------|------------------|------------------|----------|------------------------|------------------------|------------------------|------------------------| | DeiT_tiny_
patch16_224 | 0.718 | 0.910 | 3.61 | 3.94 | 6.10 | 1.07 | 5.68 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_tiny_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_tiny_patch16_224_infer.tar) | @@ -356,11 +346,10 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模 -## 13. RepVGG 系列 +## 13. RepVGG 系列 [[36](#ref36)] 关于 RepVGG 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[RepVGG 系列模型文档](../models/RepVGG.md)。 - | 模型 | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | time(ms)
bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 | |------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------| | RepVGG_A0 | 0.7131 | 0.9016 | | | | 1.36 | 8.31 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_A0_infer.tar) | @@ -376,7 +365,7 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模 -## 14. MixNet 系列 +## 14. MixNet 系列 [[29](#ref29)] 关于 MixNet 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[MixNet 系列模型文档](../models/MixNet.md)。 @@ -388,7 +377,7 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模 -## 15. ReXNet 系列 +## 15. ReXNet 系列 [[30](#ref30)] 关于 ReXNet 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[ReXNet 系列模型文档](../models/ReXNet.md)。 @@ -402,7 +391,7 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模 -## 16. SwinTransformer 系列 +## 16. SwinTransformer 系列 [[27](#ref27)] 关于 SwinTransformer 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[SwinTransformer 系列模型文档](../models/SwinTransformer.md)。 @@ -421,7 +410,7 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模 -## 17. LeViT 系列 +## 17. LeViT 系列 [[33](#ref33)] 关于 LeViT 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[LeViT 系列模型文档](../models/LeViT.md)。 @@ -437,7 +426,7 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模 -## 18. Twins 系列 +## 18. Twins 系列 [[34](#ref34)] 关于 Twins 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[Twins 系列模型文档](../models/Twins.md)。 @@ -454,7 +443,7 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模 -## 19. HarDNet 系列 +## 19. HarDNet 系列 [[37](#ref37)] 关于 HarDNet 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[HarDNet 系列模型文档](../models/HarDNet.md)。 @@ -467,7 +456,7 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模 -## 20. DLA 系列 +## 20. DLA 系列 [[38](#ref38)] 关于 DLA 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[DLA 系列模型文档](../models/DLA.md)。 @@ -485,7 +474,7 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模 -## 21. RedNet 系列 +## 21. RedNet 系列 [[39](#ref39)] 关于 RedNet 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[RedNet 系列模型文档](../models/RedNet.md)。 @@ -499,7 +488,7 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模 -## 22. TNT 系列 +## 22. TNT 系列 [[35](#ref35)] 关于 TNT 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[TNT 系列模型文档](../models/TNT.md)。 @@ -513,8 +502,7 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模 ## 23. 其他模型 -关于 AlexNet、SqueezeNet 系列、VGG 系列、DarkNet53 等模型的精度、速度指标如下表所示，更多介绍可以参考：[其他模型文档](../models/Others.md)。 - +关于 AlexNet [[18](#ref18)]、SqueezeNet 系列 [[19](#ref19)]、VGG 系列 [[20](#ref20)]、DarkNet53 [[21](#ref21)] 等模型的精度、速度指标如下表所示，更多介绍可以参考：[其他模型文档](../models/Others.md)。 | 模型 | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | time(ms)
bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 | |------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------| @@ -526,3 +514,86 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模 | VGG16 | 0.720 | 0.907 | 2.48 | 6.79 | 12.33 | 15.470 | 138.35 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG16_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/VGG16_infer.tar) | | VGG19 | 0.726 | 0.909 | 2.93 | 8.28 | 15.21 | 19.63 | 143.66 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG19_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/VGG19_infer.tar) | | DarkNet53 | 0.780 | 0.941 | 2.79 | 6.42 | 10.89 | 9.31 | 41.65 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DarkNet53_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DarkNet53_infer.tar) | + + + +## 参考文献 + +[1] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778. + +[2] He T, Zhang Z, Zhang H, et al. Bag of tricks for image classification with convolutional neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019: 558-567. + +[3] Howard A, Sandler M, Chu G, et al. Searching for mobilenetv3[C]//Proceedings of the IEEE International Conference on Computer Vision. 2019: 1314-1324. + +[4] Sandler M, Howard A, Zhu M, et al. Mobilenetv2: Inverted residuals and linear bottlenecks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 4510-4520. + +[5] 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. + +[6] 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. + +[7] 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. + +[8] 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. + +[9] 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. + +[10] 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. + +[11] 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. + +[12] Chollet F. Xception: Deep learning with depthwise separable convolutions[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 1251-1258. + +[13] Wang J, Sun K, Cheng T, et al. Deep high-resolution representation learning for visual recognition[J]. arXiv preprint arXiv:1908.07919, 2019. + +[14] Chen Y, Li J, Xiao H, et al. Dual path networks[C]//Advances in neural information processing systems. 2017: 4467-4475. + +[15] Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 4700-4708. + +[16] Tan M, Le Q V. Efficientnet: Rethinking model scaling for convolutional neural networks[J]. arXiv preprint arXiv:1905.11946, 2019. + +[17] Mahajan D, Girshick R, Ramanathan V, et al. Exploring the limits of weakly supervised pretraining[C]//Proceedings of the European Conference on Computer Vision (ECCV). 2018: 181-196. + +[18] Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[C]//Advances in neural information processing systems. 2012: 1097-1105. + +[19] Iandola F N, Han S, Moskewicz M W, et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size[J]. arXiv preprint arXiv:1602.07360, 2016. + +[20] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv:1409.1556, 2014. + +[21] Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 779-788. + +[22] Ding X, Guo Y, Ding G, et al. Acnet: Strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks[C]//Proceedings of the IEEE International Conference on Computer Vision. 2019: 1911-1920. + +[23] Han K, Wang Y, Tian Q, et al. GhostNet: More features from cheap operations[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020: 1580-1589. + +[24] Zhang H, Wu C, Zhang Z, et al. Resnest: Split-attention networks[J]. arXiv preprint arXiv:2004.08955, 2020. + +[25] Radosavovic I, Kosaraju R P, Girshick R, et al. Designing network design spaces[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020: 10428-10436. + +[26] C.Szegedy, V.Vanhoucke, S.Ioffe, J.Shlens, and Z.Wojna. Rethinking the inception architecture for computer vision. arXiv preprint arXiv:1512.00567, 2015. + +[27] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin and Baining Guo. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. + +[28]Cheng Cui, Tingquan Gao, Shengyu Wei, Yuning Du, Ruoyu Guo, Shuilong Dong, Bin Lu, Ying Zhou, Xueying Lv, Qiwen Liu, Xiaoguang Hu, Dianhai Yu, Yanjun Ma. PP-LCNet: A Lightweight CPU Convolutional Neural Network. + +[29]Mingxing Tan, Quoc V. Le. MixConv: Mixed Depthwise Convolutional Kernels. + +[30]Dongyoon Han, Sangdoo Yun, Byeongho Heo, YoungJoon Yoo. Rethinking Channel Dimensions for Efficient Model Design. + +[31]Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby. AN IMAGE IS WORTH 16X16 WORDS: +TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE. + +[32]Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Herve Jegou. Training data-efficient image transformers & distillation through attention. + +[33]Benjamin Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Herve Jegou, Matthijs Douze. LeViT: a Vision Transformer in ConvNet’s Clothing for Faster Inference. + +[34]Xiangxiang Chu, Zhi Tian, Yuqing Wang, Bo Zhang, Haibing Ren, Xiaolin Wei, Huaxia Xia, Chunhua Shen. Twins: Revisiting the Design of Spatial Attention in Vision Transformers. + +[35]Kai Han, An Xiao, Enhua Wu, Jianyuan Guo, Chunjing Xu, Yunhe Wang. Transformer in Transformer. + +[36]Xiaohan Ding, Xiangyu Zhang, Ningning Ma, Jungong Han, Guiguang Ding, Jian Sun. RepVGG: Making VGG-style ConvNets Great Again. + +[37]Ping Chao, Chao-Yang Kao, Yu-Shan Ruan, Chien-Hsiang Huang, Youn-Long Lin. HarDNet: A Low Memory Traffic Network. + +[38]Fisher Yu, Dequan Wang, Evan Shelhamer, Trevor Darrell. Deep Layer Aggregation. + +[39]Duo Lim Jie Hu, Changhu Wang, Xiangtai Li, Qi She, Lei Zhu, Tong Zhang, Qifeng Chen. Involution: Inverting the Inherence of Convolution for Visual Recognition. diff --git a/docs/zh_CN/models/models_intro.md b/docs/zh_CN/models/models_intro.md deleted file mode 100644 index eeb3b271dc7236d6e2730a9ea58af1d90f29c803..0000000000000000000000000000000000000000 --- a/docs/zh_CN/models/models_intro.md +++ /dev/null @@ -1,405 +0,0 @@ -# 模型库概览 ---- -## 目录 - -* [1. 概述](#1) -* [2. 评估环境](#2) -* [3. 预训练模型列表及下载地址](#3) -* [4. 参考文献](#4) - - - -## 1. 概述 - -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。 - - - -## 2. 评估环境 -* Arm 的评估环境基于骁龙 855（SD855）。 -* 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。 - -![](../../images/models/V100_benchmark/v100.fp32.bs1.main_fps_top1_s.png) - -![](../../images/models/V100_benchmark/v100.fp32.bs1.visiontransformer.png) - -> 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) - - - -## 3. 预训练模型列表及下载地址 - -- ResNet 及其 Vd 系列 - - ResNet 系列[[1](#ref1)]([论文地址](http://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html)) - - [ResNet18](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet18_pretrained.pdparams) - - [ResNet34](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_pretrained.pdparams) - - [ResNet50](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_pretrained.pdparams) - - [ResNet101](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_pretrained.pdparams) - - [ResNet152](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet152_pretrained.pdparams) - - ResNet_vc、ResNet_vd 系列[[2](#ref2)]([论文地址](https://arxiv.org/abs/1812.01187)) - - [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/legendary_models/ResNet18_vd_pretrained.pdparams) - - [ResNet34_vd](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_vd_pretrained.pdparams) - - [ResNet34_vd_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_vd_ssld_pretrained.pdparams) - - [ResNet50_vd](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_pretrained.pdparams) - - [ResNet101_vd](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_vd_pretrained.pdparams) - - [ResNet152_vd](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet152_vd_pretrained.pdparams) - - [ResNet200_vd](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet200_vd_pretrained.pdparams) - - [ResNet50_vd_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/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/legendary_models/ResNet101_vd_ssld_pretrained.pdparams) - - -- 轻量级模型系列 - - PP-LCNet 系列[[28](#ref28)]([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) - - MobileNetV3 系列[[3](#ref3)]([论文地址](https://arxiv.org/abs/1905.02244)) - - [MobileNetV3_large_x0_35](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x0_35_pretrained.pdparams) - - [MobileNetV3_large_x0_5](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x0_5_pretrained.pdparams) - - [MobileNetV3_large_x0_75](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x0_75_pretrained.pdparams) - - [MobileNetV3_large_x1_0](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_0_pretrained.pdparams) - - [MobileNetV3_large_x1_25](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_25_pretrained.pdparams) - - [MobileNetV3_small_x0_35](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_35_pretrained.pdparams) - - [MobileNetV3_small_x0_5](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_5_pretrained.pdparams) - - [MobileNetV3_small_x0_75](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_75_pretrained.pdparams) - - [MobileNetV3_small_x1_0](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_0_pretrained.pdparams) - - [MobileNetV3_small_x1_25](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_25_pretrained.pdparams) - - [MobileNetV3_large_x1_0_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_0_ssld_pretrained.pdparams) - - [MobileNetV3_large_x1_0_ssld_int8]()(coming soon) - - [MobileNetV3_small_x1_0_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_0_ssld_pretrained.pdparams) - - MobileNetV2 系列[[4](#ref4)]([论文地址](https://arxiv.org/abs/1801.04381)) - - [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) - - MobileNetV1 系列[[5](#ref5)]([论文地址](https://arxiv.org/abs/1704.04861)) - - [MobileNetV1_x0_25](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_25_pretrained.pdparams) - - [MobileNetV1_x0_5](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_5_pretrained.pdparams) - - [MobileNetV1_x0_75](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_75_pretrained.pdparams) - - [MobileNetV1](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_pretrained.pdparams) - - [MobileNetV1_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_ssld_pretrained.pdparams) - - ShuffleNetV2 系列[[6](#ref6)]([论文地址](https://arxiv.org/abs/1807.11164)) - - [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) - - GhostNet 系列[[23](#ref23)]([论文地址](https://arxiv.org/pdf/1911.11907.pdf)) - - [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) - - MixNet 系列[[29](#ref29)]([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[[30](#ref30)]([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) - - -- SEResNeXt 与 Res2Net 系列 - - ResNeXt 系列[[7](#ref7)]([论文地址](https://arxiv.org/abs/1611.05431)) - - [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) - - ResNeXt_vd 系列 - - [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) - - SE_ResNet_vd 系列[[8](#ref8)]([论文地址](https://arxiv.org/abs/1709.01507)) - - [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) - - SE_ResNeXt 系列 - - [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) - - SE_ResNeXt_vd 系列 - - [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) - - Res2Net 系列[[9](#ref9)]([论文地址](https://arxiv.org/abs/1904.01169)) - - [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) - - -- Inception 系列 - - GoogLeNet 系列[[10](#ref10)]([论文地址](https://arxiv.org/pdf/1409.4842.pdf)) - - [GoogLeNet](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GoogLeNet_pretrained.pdparams) - - InceptionV3 系列[[26](#ref26)]([论文地址](https://arxiv.org/abs/1512.00567)) - - [InceptionV3](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/InceptionV3_pretrained.pdparams) - - InceptionV4 系列[[11](#ref11)]([论文地址](https://arxiv.org/abs/1602.07261)) - - [InceptionV4](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/InceptionV4_pretrained.pdparams) - - Xception 系列[[12](#ref12)]([论文地址](http://openaccess.thecvf.com/content_cvpr_2017/html/Chollet_Xception_Deep_Learning_CVPR_2017_paper.html)) - - [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) - - -- HRNet 系列 - - HRNet 系列[[13](#ref13)]([论文地址](https://arxiv.org/abs/1908.07919)) - - [HRNet_W18_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W18_C_pretrained.pdparams) - - [HRNet_W18_C_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W18_C_ssld_pretrained.pdparams) - - [HRNet_W30_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W30_C_pretrained.pdparams) - - [HRNet_W32_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W32_C_pretrained.pdparams) - - [HRNet_W40_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W40_C_pretrained.pdparams) - - [HRNet_W44_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W44_C_pretrained.pdparams) - - [HRNet_W48_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W48_C_pretrained.pdparams) - - [HRNet_W48_C_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W48_C_ssld_pretrained.pdparams) - - [HRNet_W64_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W64_C_pretrained.pdparams) - - [SE_HRNet_W64_C_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SE_HRNet_W64_C_ssld_pretrained.pdparams) - -- DPN 与 DenseNet 系列 - - DPN 系列[[14](#ref14)]([论文地址](https://arxiv.org/abs/1707.01629)) - - [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) - - DenseNet 系列[[15](#ref15)]([论文地址](https://arxiv.org/abs/1608.06993)) - - [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) - - -- EfficientNet 与 ResNeXt101_wsl 系列 - - EfficientNet 系列[[16](#ref16)]([论文地址](https://arxiv.org/abs/1905.11946)) - - [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) - - ResNeXt101_wsl 系列[[17](#ref17)]([论文地址](https://arxiv.org/abs/1805.00932)) - - [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) - -- ResNeSt 与 RegNet 系列 - - ResNeSt 系列[[24](#ref24)]([论文地址](https://arxiv.org/abs/2004.08955)) - - [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) - - RegNet 系列[[25](#ref25)]([paper link](https://arxiv.org/abs/2003.13678)) - - [RegNetX_4GF](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetX_4GF_pretrained.pdparams) - -- Transformer 系列 - - Swin-transformer 系列[[27](#ref27)]([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_22kto1k_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_22kto1k_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_22kto1k_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) - - ViT 系列[[31](#ref31)]([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[[32](#ref32)]([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[[33](#ref33)]([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[[34](#ref34)]([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[[35](#ref35)]([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) - -- 其他模型 - - AlexNet 系列[[18](#ref18)]([论文地址](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)) - - [AlexNet](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/AlexNet_pretrained.pdparams) - - SqueezeNet 系列[[19](#ref19)]([论文地址](https://arxiv.org/abs/1602.07360)) - - [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) - - VGG 系列[[20](#ref20)]([论文地址](https://arxiv.org/abs/1409.1556)) - - [VGG11](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG11_pretrained.pdparams) - - [VGG13](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG13_pretrained.pdparams) - - [VGG16](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG16_pretrained.pdparams) - - [VGG19](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG19_pretrained.pdparams) - - DarkNet 系列[[21](#ref21)]([论文地址](https://arxiv.org/abs/1506.02640)) - - [DarkNet53](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DarkNet53_pretrained.pdparams) - - RepVGG 系列[[36](#ref36)]([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[[37](#ref37)]([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[[38](#ref38)]([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[[39](#ref39)]([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) - - - -**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。 - - - -## 4. 参考文献 - - -[1] He K, Zhang X, Ren S, et al. 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