diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 0000000000000000000000000000000000000000..d0c3cbf1020d5c292abdedf27627c6abe25e2293 --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = source +BUILDDIR = build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/en/algorithm_introduction/ImageNet_models_en.md b/docs/en/algorithm_introduction/ImageNet_models_en.md index 29d4f47d7144f175e531a318576eb7ccc4d24ea1..0226417f5119ea0247be4ab175b015386b071d8c 100644 --- a/docs/en/algorithm_introduction/ImageNet_models_en.md +++ b/docs/en/algorithm_introduction/ImageNet_models_en.md @@ -1,10 +1,44 @@ -### ImageNet Model zoo overview - -Based on the ImageNet-1k classification dataset, the 35 classification network structures supported by PaddleClas and the corresponding 164 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. The evaluation environment is as follows. - -* CPU evaluation environment is based on Snapdragon 855 (SD855). -* The GPU evaluation speed is measured by running 500 times under the FP32+TensorRT configuration (excluding the warmup time of the first 10 times). - +# ImageNet Model zoo overview + +## Catalogue + +- [1. Model library overview diagram](#1) +- [2. SSLD pretrained models](#2) + - [2.1 Server-side knowledge distillation model](#2.1) + - [2.2 Mobile-side knowledge distillation model](#2.2) + - [2.3 Intel-CPU-side knowledge distillation model](#2.3) +- [3. PP-LCNet series](#3) +- [4. ResNet series](#4) +- [5. Mobile series](#5) +- [6. SEResNeXt and Res2Net series](#6) +- [7. DPN and DenseNet series](#7) +- [8. HRNet series](#8) +- [9. Inception series](#9) +- [10. EfficientNet ans ResNeXt101_wsl series](#10) +- [11. ResNeSt and RegNet series](#11) +- [12. ViT and DeiT series](#12) +- [13. RepVGG series](#13) +- [14. MixNet series](#14) +- [15. ReXNet series](#15) +- [16. SwinTransformer series](#16) +- [17. LeViT series](#17) +- [18. Twins series](#18) +- [19. HarDNet series](#19) +- [20. DLA series](#20) +- [21. RedNet series](#21) +- [22. TNT series](#22) +- [23. Other models](#23) + + + +## 1. Model library overview diagram + +Based on the ImageNet-1k classification dataset, the 37 classification network structures supported by PaddleClas and the corresponding 217 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. The evaluation environment is as follows. + +* Arm CPU evaluation environment is based on Snapdragon 855 (SD855). +* Intel CPU evaluation environment is based on Intel(R) Xeon(R) Gold 6148. +* The GPU evaluation speed is measured by running 2100 times under the FP32+TensorRT configuration (excluding the warmup time of the first 100 times). +* FLOPs and Params are calculated by `paddle.flops()` (PaddlePaddle version is 2.2) Curves of accuracy to the inference time of common server-side models are shown as follows. @@ -24,457 +58,470 @@ Curves of accuracy to the inference time of some VisionTransformer models are sh - -### SSLD pretrained models + + +## 2. SSLD pretrained models Accuracy and inference time of the prtrained models based on SSLD distillation are as follows. More detailed information can be refered to [SSLD distillation tutorial](../en/advanced_tutorials/distillation/distillation_en.md). -* Server-side distillation pretrained models - -| Model | Top-1 Acc | Reference
Top-1 Acc | Acc gain | time(ms)
bs=1 | time(ms)
bs=4 | Flops(G) | Params(M) | Download Address | -|---------------------|-----------|-----------|---------------|----------------|----------------|----------|-----------|-----------------------------------| -| ResNet34_vd_ssld | 0.797 | 0.760 | 0.037 | 2.434 | 6.222 | 7.39 | 21.82 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_vd_ssld_pretrained.pdparams) | -| ResNet50_vd_ssld | 0.830 | 0.792 | 0.039 | 3.531 | 8.090 | 8.67 | 25.58 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_ssld_pretrained.pdparams) | -| ResNet101_vd_ssld | 0.837 | 0.802 | 0.035 | 6.117 | 13.762 | 16.1 | 44.57 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_vd_ssld_pretrained.pdparams) | -| Res2Net50_vd_26w_4s_ssld | 0.831 | 0.798 | 0.033 | 4.527 | 9.657 | 8.37 | 25.06 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_vd_26w_4s_ssld_pretrained.pdparams) | -| Res2Net101_vd_26w_4s_ssld | 0.839 | 0.806 | 0.033 | 8.087 | 17.312 | 16.67 | 45.22 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net101_vd_26w_4s_ssld_pretrained.pdparams) | -| Res2Net200_vd_26w_4s_ssld | 0.851 | 0.812 | 0.049 | 14.678 | 32.350 | 31.49 | 76.21 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net200_vd_26w_4s_ssld_pretrained.pdparams) | -| HRNet_W18_C_ssld | 0.812 | 0.769 | 0.043 | 7.406 | 13.297 | 4.14 | 21.29 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W18_C_ssld_pretrained.pdparams) | -| HRNet_W48_C_ssld | 0.836 | 0.790 | 0.046 | 13.707 | 34.435 | 34.58 | 77.47 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W48_C_ssld_pretrained.pdparams) | -| SE_HRNet_W64_C_ssld | 0.848 | - | - | 31.697 | 94.995 | 57.83 | 128.97 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SE_HRNet_W64_C_ssld_pretrained.pdparams) | - - -* Mobile-side distillation pretrained models - -| Model | Top-1 Acc | Reference
Top-1 Acc | Acc gain | SD855 time(ms)
bs=1 | Flops(G) | Params(M) | Storage Size(M) | Download Address | -|---------------------|-----------|-----------|---------------|----------------|-----------|----------|-----------|-----------------------------------| -| MobileNetV1_ssld | 0.779 | 0.710 | 0.069 | 32.523 | 1.11 | 4.19 | 16 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_ssld_pretrained.pdparams) | -| MobileNetV2_ssld | 0.767 | 0.722 | 0.045 | 23.318 | 0.6 | 3.44 | 14 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_ssld_pretrained.pdparams) | -| MobileNetV3_small_x0_35_ssld | 0.556 | 0.530 | 0.026 | 2.635 | 0.026 | 1.66 | 6.9 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_35_ssld_pretrained.pdparams) | -| MobileNetV3_large_x1_0_ssld | 0.790 | 0.753 | 0.036 | 19.308 | 0.45 | 5.47 | 21 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_0_ssld_pretrained.pdparams) | -| MobileNetV3_small_x1_0_ssld | 0.713 | 0.682 | 0.031 | 6.546 | 0.123 | 2.94 | 12 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_0_ssld_pretrained.pdparams) | -| GhostNet_x1_3_ssld | 0.794 | 0.757 | 0.037 | 19.983 | 0.44 | 7.3 | 29 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_3_ssld_pretrained.pdparams) - -* Intel-CPU-side distillation pretrained models - -| Model | Top-1 Acc | Reference
Top-1 Acc | Acc gain | Intel-Xeon-Gold-6148 time(ms)
bs=1 | Flops(M) | Params(M) | Download Address | -|---------------------|-----------|-----------|---------------|----------------|----------|-----------|-----------------------------------| -| PPLCNet_x0_5_ssld | 0.661 | 0.631 | 0.030 | 2.05 | 47 | 1.9 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_5_ssld_pretrained.pdparams) | -| PPLCNet_x1_0_ssld | 0.744 | 0.713 | 0.033 | 2.46 | 161 | 3.0 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_0_ssld_pretrained.pdparams) | -| PPLCNet_x2_5_ssld | 0.808 | 0.766 | 0.042 | 5.39 | 906 | 9.0 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_5_ssld_pretrained.pdparams) | - - -* Note: `Reference Top-1 Acc` means accuracy of pretrained models which are trained on ImageNet1k dataset. - - - -### PP-LCNet_series - -Accuracy and inference time metrics of PPLCNet series models are shown as follows. More detailed information can be refered to [PPLCNet series tutorial](../en/models/PP-LCNet_en.md). - -| Model | Top-1 Acc | Top-5 Acc | Intel-Xeon-Gold-6148 time(ms)
bs=1 | FLOPs(M) | Params(M) | Download Address | -|:--:|:--:|:--:|:--:|:--:|:--:|:--:| -| PPLCNet_x0_25 |0.5186 | 0.7565 | 1.74 | 18 | 1.5 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_25_pretrained.pdparams) | -| PPLCNet_x0_35 |0.5809 | 0.8083 | 1.92 | 29 | 1.6 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_35_pretrained.pdparams) | -| PPLCNet_x0_5 |0.6314 | 0.8466 | 2.05 | 47 | 1.9 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_5_pretrained.pdparams) | -| PPLCNet_x0_75 |0.6818 | 0.8830 | 2.29 | 99 | 2.4 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_75_pretrained.pdparams) | -| PPLCNet_x1_0 |0.7132 | 0.9003 | 2.46 | 161 | 3.0 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_0_pretrained.pdparams) | -| PPLCNet_x1_5 |0.7371 | 0.9153 | 3.19 | 342 | 4.5 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_5_pretrained.pdparams) | -| PPLCNet_x2_0 |0.7518 | 0.9227 | 4.27 | 590 | 6.5 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_0_pretrained.pdparams) | -| PPLCNet_x2_5 |0.7660 | 0.9300 | 5.39 | 906 | 9.0 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_5_pretrained.pdparams) | - - - -### ResNet and Vd series - -Accuracy and inference time metrics of ResNet and Vd series models are shown as follows. More detailed information can be refered to [ResNet and Vd series tutorial](../en/models/ResNet_and_vd_en.md). - -| Model | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | Flops(G) | Params(M) | Download Address | -|---------------------|-----------|-----------|-----------------------|----------------------|----------|-----------|----------------------------------------------------------------------------------------------| -| ResNet18 | 0.7098 | 0.8992 | 1.45606 | 3.56305 | 3.66 | 11.69 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet18_pretrained.pdparams) | -| ResNet18_vd | 0.7226 | 0.9080 | 1.54557 | 3.85363 | 4.14 | 11.71 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet18_vd_pretrained.pdparams) | -| ResNet34 | 0.7457 | 0.9214 | 2.34957 | 5.89821 | 7.36 | 21.8 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_pretrained.pdparams) | -| ResNet34_vd | 0.7598 | 0.9298 | 2.43427 | 6.22257 | 7.39 | 21.82 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_vd_pretrained.pdparams) | -| ResNet34_vd_ssld | 0.7972 | 0.9490 | 2.43427 | 6.22257 | 7.39 | 21.82 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_vd_ssld_pretrained.pdparams) | -| ResNet50 | 0.7650 | 0.9300 | 3.47712 | 7.84421 | 8.19 | 25.56 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_pretrained.pdparams) | -| ResNet50_vc | 0.7835 | 0.9403 | 3.52346 | 8.10725 | 8.67 | 25.58 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vc_pretrained.pdparams) | -| ResNet50_vd | 0.7912 | 0.9444 | 3.53131 | 8.09057 | 8.67 | 25.58 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_pretrained.pdparams) | -| ResNet101 | 0.7756 | 0.9364 | 6.07125 | 13.40573 | 15.52 | 44.55 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_pretrained.pdparams) | -| ResNet101_vd | 0.8017 | 0.9497 | 6.11704 | 13.76222 | 16.1 | 44.57 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_vd_pretrained.pdparams) | -| ResNet152 | 0.7826 | 0.9396 | 8.50198 | 19.17073 | 23.05 | 60.19 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet152_pretrained.pdparams) | -| ResNet152_vd | 0.8059 | 0.9530 | 8.54376 | 19.52157 | 23.53 | 60.21 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet152_vd_pretrained.pdparams) | -| ResNet200_vd | 0.8093 | 0.9533 | 10.80619 | 25.01731 | 30.53 | 74.74 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet200_vd_pretrained.pdparams) | -| ResNet50_vd_
ssld | 0.8300 | 0.9640 | 3.53131 | 8.09057 | 8.67 | 25.58 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_ssld_pretrained.pdparams) | -| ResNet101_vd_
ssld | 0.8373 | 0.9669 | 6.11704 | 13.76222 | 16.1 | 44.57 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_vd_ssld_pretrained.pdparams) | - - - -### Mobile series - -Accuracy and inference time metrics of Mobile series models are shown as follows. More detailed information can be refered to [Mobile series tutorial](../en/models/Mobile_en.md). - -| Model | Top-1 Acc | Top-5 Acc | SD855 time(ms)
bs=1 | Flops(G) | Params(M) | Model storage size(M) | Download Address | -|----------------------------------|-----------|-----------|------------------------|----------|-----------|---------|-----------------------------------------------------------------------------------------------------------| -| MobileNetV1_
x0_25 | 0.5143 | 0.7546 | 3.21985 | 0.07 | 0.46 | 1.9 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_25_pretrained.pdparams) | -| MobileNetV1_
x0_5 | 0.6352 | 0.8473 | 9.579599 | 0.28 | 1.31 | 5.2 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_5_pretrained.pdparams) | -| MobileNetV1_
x0_75 | 0.6881 | 0.8823 | 19.436399 | 0.63 | 2.55 | 10 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_75_pretrained.pdparams) | -| MobileNetV1 | 0.7099 | 0.8968 | 32.523048 | 1.11 | 4.19 | 16 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_pretrained.pdparams) | -| MobileNetV1_
ssld | 0.7789 | 0.9394 | 32.523048 | 1.11 | 4.19 | 16 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_ssld_pretrained.pdparams) | -| MobileNetV2_
x0_25 | 0.5321 | 0.7652 | 3.79925 | 0.05 | 1.5 | 6.1 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x0_25_pretrained.pdparams) | -| MobileNetV2_
x0_5 | 0.6503 | 0.8572 | 8.7021 | 0.17 | 1.93 | 7.8 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x0_5_pretrained.pdparams) | -| MobileNetV2_
x0_75 | 0.6983 | 0.8901 | 15.531351 | 0.35 | 2.58 | 10 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x0_75_pretrained.pdparams) | -| MobileNetV2 | 0.7215 | 0.9065 | 23.317699 | 0.6 | 3.44 | 14 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_pretrained.pdparams) | -| MobileNetV2_
x1_5 | 0.7412 | 0.9167 | 45.623848 | 1.32 | 6.76 | 26 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x1_5_pretrained.pdparams) | -| MobileNetV2_
x2_0 | 0.7523 | 0.9258 | 74.291649 | 2.32 | 11.13 | 43 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x2_0_pretrained.pdparams) | -| MobileNetV2_
ssld | 0.7674 | 0.9339 | 23.317699 | 0.6 | 3.44 | 14 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_ssld_pretrained.pdparams) | -MobileNetV3_
large_x1_25 | 0.7641 | 0.9295 | 28.217701 | 0.714 | 7.44 | 29 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_25_pretrained.pdparams) | -| MobileNetV3_
large_x1_0 | 0.7532 | 0.9231 | 19.30835 | 0.45 | 5.47 | 21 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_0_pretrained.pdparams) | -| MobileNetV3_
large_x0_75 | 0.7314 | 0.9108 | 13.5646 | 0.296 | 3.91 | 16 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x0_75_pretrained.pdparams) | -| MobileNetV3_
large_x0_5 | 0.6924 | 0.8852 | 7.49315 | 0.138 | 2.67 | 11 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x0_5_pretrained.pdparams) | -| MobileNetV3_
large_x0_35 | 0.6432 | 0.8546 | 5.13695 | 0.077 | 2.1 | 8.6 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x0_35_pretrained.pdparams) | -| MobileNetV3_
small_x1_25 | 0.7067 | 0.8951 | 9.2745 | 0.195 | 3.62 | 14 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_25_pretrained.pdparams) | -| MobileNetV3_
small_x1_0 | 0.6824 | 0.8806 | 6.5463 | 0.123 | 2.94 | 12 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_0_pretrained.pdparams) | -| MobileNetV3_
small_x0_75 | 0.6602 | 0.8633 | 5.28435 | 0.088 | 2.37 | 9.6 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_75_pretrained.pdparams) | -| MobileNetV3_
small_x0_5 | 0.5921 | 0.8152 | 3.35165 | 0.043 | 1.9 | 7.8 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_5_pretrained.pdparams) | -| MobileNetV3_
small_x0_35 | 0.5303 | 0.7637 | 2.6352 | 0.026 | 1.66 | 6.9 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_35_pretrained.pdparams) | -| MobileNetV3_
small_x0_35_ssld | 0.5555 | 0.7771 | 2.6352 | 0.026 | 1.66 | 6.9 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_35_ssld_pretrained.pdparams) | -| MobileNetV3_
large_x1_0_ssld | 0.7896 | 0.9448 | 19.30835 | 0.45 | 5.47 | 21 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_0_ssld_pretrained.pdparams) | -| MobileNetV3_small_
x1_0_ssld | 0.7129 | 0.9010 | 6.5463 | 0.123 | 2.94 | 12 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_0_ssld_pretrained.pdparams) | -| ShuffleNetV2 | 0.6880 | 0.8845 | 10.941 | 0.28 | 2.26 | 9 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x1_0_pretrained.pdparams) | -| ShuffleNetV2_
x0_25 | 0.4990 | 0.7379 | 2.329 | 0.03 | 0.6 | 2.7 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_25_pretrained.pdparams) | -| ShuffleNetV2_
x0_33 | 0.5373 | 0.7705 | 2.64335 | 0.04 | 0.64 | 2.8 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_33_pretrained.pdparams) | -| ShuffleNetV2_
x0_5 | 0.6032 | 0.8226 | 4.2613 | 0.08 | 1.36 | 5.6 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_5_pretrained.pdparams) | -| ShuffleNetV2_
x1_5 | 0.7163 | 0.9015 | 19.3522 | 0.58 | 3.47 | 14 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x1_5_pretrained.pdparams) | -| ShuffleNetV2_
x2_0 | 0.7315 | 0.9120 | 34.770149 | 1.12 | 7.32 | 28 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x2_0_pretrained.pdparams) | -| ShuffleNetV2_
swish | 0.7003 | 0.8917 | 16.023151 | 0.29 | 2.26 | 9.1 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_swish_pretrained.pdparams) | -| GhostNet_
x0_5 | 0.6688 | 0.8695 | 5.7143 | 0.082 | 2.6 | 10 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x0_5_pretrained.pdparams) | -| GhostNet_
x1_0 | 0.7402 | 0.9165 | 13.5587 | 0.294 | 5.2 | 20 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_0_pretrained.pdparams) | -| GhostNet_
x1_3 | 0.7579 | 0.9254 | 19.9825 | 0.44 | 7.3 | 29 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_3_pretrained.pdparams) | -| GhostNet_
x1_3_ssld | 0.7938 | 0.9449 | 19.9825 | 0.44 | 7.3 | 29 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_3_ssld_pretrained.pdparams) | - - - -### SEResNeXt and Res2Net series - -Accuracy and inference time metrics of SEResNeXt and Res2Net series models are shown as follows. More detailed information can be refered to [SEResNext and_Res2Net series tutorial](../en/models/SEResNext_and_Res2Net_en.md). - - -| Model | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | Flops(G) | Params(M) | Download Address | -|---------------------------|-----------|-----------|-----------------------|----------------------|----------|-----------|----------------------------------------------------------------------------------------------------| -| Res2Net50_
26w_4s | 0.7933 | 0.9457 | 4.47188 | 9.65722 | 8.52 | 25.7 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_26w_4s_pretrained.pdparams) | -| Res2Net50_vd_
26w_4s | 0.7975 | 0.9491 | 4.52712 | 9.93247 | 8.37 | 25.06 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_vd_26w_4s_pretrained.pdparams) | -| Res2Net50_
14w_8s | 0.7946 | 0.9470 | 5.4026 | 10.60273 | 9.01 | 25.72 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_14w_8s_pretrained.pdparams) | -| Res2Net101_vd_
26w_4s | 0.8064 | 0.9522 | 8.08729 | 17.31208 | 16.67 | 45.22 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net101_vd_26w_4s_pretrained.pdparams) | -| Res2Net200_vd_
26w_4s | 0.8121 | 0.9571 | 14.67806 | 32.35032 | 31.49 | 76.21 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net200_vd_26w_4s_pretrained.pdparams) | -| Res2Net200_vd_
26w_4s_ssld | 0.8513 | 0.9742 | 14.67806 | 32.35032 | 31.49 | 76.21 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net200_vd_26w_4s_ssld_pretrained.pdparams) | -| ResNeXt50_
32x4d | 0.7775 | 0.9382 | 7.56327 | 10.6134 | 8.02 | 23.64 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_32x4d_pretrained.pdparams) | -| ResNeXt50_vd_
32x4d | 0.7956 | 0.9462 | 7.62044 | 11.03385 | 8.5 | 23.66 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_vd_32x4d_pretrained.pdparams) | -| ResNeXt50_
64x4d | 0.7843 | 0.9413 | 13.80962 | 18.4712 | 15.06 | 42.36 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_64x4d_pretrained.pdparams) | -| ResNeXt50_vd_
64x4d | 0.8012 | 0.9486 | 13.94449 | 18.88759 | 15.54 | 42.38 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_vd_64x4d_pretrained.pdparams) | -| ResNeXt101_
32x4d | 0.7865 | 0.9419 | 16.21503 | 19.96568 | 15.01 | 41.54 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x4d_pretrained.pdparams) | -| ResNeXt101_vd_
32x4d | 0.8033 | 0.9512 | 16.28103 | 20.25611 | 15.49 | 41.56 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_vd_32x4d_pretrained.pdparams) | -| ResNeXt101_
64x4d | 0.7835 | 0.9452 | 30.4788 | 36.29801 | 29.05 | 78.12 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_64x4d_pretrained.pdparams) | -| ResNeXt101_vd_
64x4d | 0.8078 | 0.9520 | 30.40456 | 36.77324 | 29.53 | 78.14 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_vd_64x4d_pretrained.pdparams) | -| ResNeXt152_
32x4d | 0.7898 | 0.9433 | 24.86299 | 29.36764 | 22.01 | 56.28 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_32x4d_pretrained.pdparams) | -| ResNeXt152_vd_
32x4d | 0.8072 | 0.9520 | 25.03258 | 30.08987 | 22.49 | 56.3 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_vd_32x4d_pretrained.pdparams) | -| ResNeXt152_
64x4d | 0.7951 | 0.9471 | 46.7564 | 56.34108 | 43.03 | 107.57 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_64x4d_pretrained.pdparams) | -| ResNeXt152_vd_
64x4d | 0.8108 | 0.9534 | 47.18638 | 57.16257 | 43.52 | 107.59 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_vd_64x4d_pretrained.pdparams) | -| SE_ResNet18_vd | 0.7333 | 0.9138 | 1.7691 | 4.19877 | 4.14 | 11.8 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNet18_vd_pretrained.pdparams) | -| SE_ResNet34_vd | 0.7651 | 0.9320 | 2.88559 | 7.03291 | 7.84 | 21.98 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNet34_vd_pretrained.pdparams) | -| SE_ResNet50_vd | 0.7952 | 0.9475 | 4.28393 | 10.38846 | 8.67 | 28.09 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNet50_vd_pretrained.pdparams) | -| SE_ResNeXt50_
32x4d | 0.7844 | 0.9396 | 8.74121 | 13.563 | 8.02 | 26.16 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt50_32x4d_pretrained.pdparams) | -| SE_ResNeXt50_vd_
32x4d | 0.8024 | 0.9489 | 9.17134 | 14.76192 | 10.76 | 26.28 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt50_vd_32x4d_pretrained.pdparams) | -| SE_ResNeXt101_
32x4d | 0.7939 | 0.9443 | 18.82604 | 25.31814 | 15.02 | 46.28 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt101_32x4d_pretrained.pdparams) | -| SENet154_vd | 0.8140 | 0.9548 | 53.79794 | 66.31684 | 45.83 | 114.29 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SENet154_vd_pretrained.pdparams) | - - - -### DPN and DenseNet series - -Accuracy and inference time metrics of DPN and DenseNet series models are shown as follows. More detailed information can be refered to [DPN and DenseNet series tutorial](../en/models/DPN_DenseNet_en.md). - - -| Model | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | Flops(G) | Params(M) | Download Address | -|-------------|-----------|-----------|-----------------------|----------------------|----------|-----------|--------------------------------------------------------------------------------------| -| DenseNet121 | 0.7566 | 0.9258 | 4.40447 | 9.32623 | 5.69 | 7.98 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet121_pretrained.pdparams) | -| DenseNet161 | 0.7857 | 0.9414 | 10.39152 | 22.15555 | 15.49 | 28.68 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet161_pretrained.pdparams) | -| DenseNet169 | 0.7681 | 0.9331 | 6.43598 | 12.98832 | 6.74 | 14.15 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet169_pretrained.pdparams) | -| DenseNet201 | 0.7763 | 0.9366 | 8.20652 | 17.45838 | 8.61 | 20.01 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet201_pretrained.pdparams) | -| DenseNet264 | 0.7796 | 0.9385 | 12.14722 | 26.27707 | 11.54 | 33.37 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet264_pretrained.pdparams) | -| DPN68 | 0.7678 | 0.9343 | 11.64915 | 12.82807 | 4.03 | 10.78 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN68_pretrained.pdparams) | -| DPN92 | 0.7985 | 0.9480 | 18.15746 | 23.87545 | 12.54 | 36.29 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN92_pretrained.pdparams) | -| DPN98 | 0.8059 | 0.9510 | 21.18196 | 33.23925 | 22.22 | 58.46 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN98_pretrained.pdparams) | -| DPN107 | 0.8089 | 0.9532 | 27.62046 | 52.65353 | 35.06 | 82.97 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN107_pretrained.pdparams) | -| DPN131 | 0.8070 | 0.9514 | 28.33119 | 46.19439 | 30.51 | 75.36 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN131_pretrained.pdparams) | - - -### HRNet series - -Accuracy and inference time metrics of HRNet series models are shown as follows. More detailed information can be refered to [Mobile series tutorial](../en/models/HRNet_en.md). - - -| Model | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | Flops(G) | Params(M) | Download Address | -|-------------|-----------|-----------|------------------|------------------|----------|-----------|--------------------------------------------------------------------------------------| -| HRNet_W18_C | 0.7692 | 0.9339 | 7.40636 | 13.29752 | 4.14 | 21.29 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W18_C_pretrained.pdparams) | -| HRNet_W18_C_ssld | 0.81162 | 0.95804 | 7.40636 | 13.29752 | 4.14 | 21.29 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W18_C_ssld_pretrained.pdparams) | -| HRNet_W30_C | 0.7804 | 0.9402 | 9.57594 | 17.35485 | 16.23 | 37.71 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W30_C_pretrained.pdparams) | -| HRNet_W32_C | 0.7828 | 0.9424 | 9.49807 | 17.72921 | 17.86 | 41.23 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W32_C_pretrained.pdparams) | -| HRNet_W40_C | 0.7877 | 0.9447 | 12.12202 | 25.68184 | 25.41 | 57.55 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W40_C_pretrained.pdparams) | -| HRNet_W44_C | 0.7900 | 0.9451 | 13.19858 | 32.25202 | 29.79 | 67.06 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W44_C_pretrained.pdparams) | -| HRNet_W48_C | 0.7895 | 0.9442 | 13.70761 | 34.43572 | 34.58 | 77.47 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W48_C_pretrained.pdparams) | -| HRNet_W48_C_ssld | 0.8363 | 0.9682 | 13.70761 | 34.43572 | 34.58 | 77.47 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W48_C_ssld_pretrained.pdparams) | -| HRNet_W64_C | 0.7930 | 0.9461 | 17.57527 | 47.9533 | 57.83 | 128.06 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W64_C_pretrained.pdparams) | -| SE_HRNet_W64_C_ssld | 0.8475 | 0.9726 | 31.69770 | 94.99546 | 57.83 | 128.97 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SE_HRNet_W64_C_ssld_pretrained.pdparams) | - - - -### Inception series - -Accuracy and inference time metrics of Inception series models are shown as follows. More detailed information can be refered to [Inception series tutorial](../en/models/Inception_en.md). - - -| Model | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | Flops(G) | Params(M) | Download Address | -|--------------------|-----------|-----------|-----------------------|----------------------|----------|-----------|---------------------------------------------------------------------------------------------| -| GoogLeNet | 0.7070 | 0.8966 | 1.88038 | 4.48882 | 2.88 | 8.46 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GoogLeNet_pretrained.pdparams) | -| Xception41 | 0.7930 | 0.9453 | 4.96939 | 17.01361 | 16.74 | 22.69 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception41_pretrained.pdparams) | -| Xception41_deeplab | 0.7955 | 0.9438 | 5.33541 | 17.55938 | 18.16 | 26.73 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception41_deeplab_pretrained.pdparams) | -| Xception65 | 0.8100 | 0.9549 | 7.26158 | 25.88778 | 25.95 | 35.48 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception65_pretrained.pdparams) | -| Xception65_deeplab | 0.8032 | 0.9449 | 7.60208 | 26.03699 | 27.37 | 39.52 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception65_deeplab_pretrained.pdparams) | -| Xception71 | 0.8111 | 0.9545 | 8.72457 | 31.55549 | 31.77 | 37.28 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception71_pretrained.pdparams) | -| InceptionV3 | 0.7914 | 0.9459 | 6.64054 | 13.53630 | 11.46 | 23.83 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/InceptionV3_pretrained.pdparams) | -| InceptionV4 | 0.8077 | 0.9526 | 12.99342 | 25.23416 | 24.57 | 42.68 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/InceptionV4_pretrained.pdparams) | - - - -### EfficientNet and ResNeXt101_wsl series - -Accuracy and inference time metrics of EfficientNet and ResNeXt101_wsl series models are shown as follows. More detailed information can be refered to [EfficientNet and ResNeXt101_wsl series tutorial](../en/models/EfficientNet_and_ResNeXt101_wsl_en.md). - + + +### 2.1 Server-side knowledge distillation model + +| Model | Top-1 Acc | Reference
Top-1 Acc | Acc gain | time(ms)
bs=1 | time(ms)
bs=4 | time(ms)
bs=8 | FLOPs(G) | Params(M) | Pretrained Model Download Address | Inference Model Download Address | +|---------------------|-----------|-----------|---------------|----------------|-----------|----------|-----------|-----------------------------------|-----------------------------------|-----------------------------------| +| ResNet34_vd_ssld | 0.797 | 0.760 | 0.037 | 2.00 | 3.28 | 5.84 | 3.93 | 21.84 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_vd_ssld_pretrained.pdparams)   | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet34_vd_ssld.tar)   | +| ResNet50_vd_ssld | 0.830 | 0.792 | 0.039 | 2.60 | 4.86 | 7.63 | 4.35 | 25.63 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_ssld_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_vd_ssld_infer.tar) | +| ResNet101_vd_ssld | 0.837 | 0.802 | 0.035 | 4.43 | 8.25 | 12.60 | 8.08 | 44.67 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_vd_ssld_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet101_vd_ssld_infer.tar) | +| Res2Net50_vd_26w_4s_ssld | 0.831 | 0.798 | 0.033 | 3.59 | 6.35 | 9.50 | 4.28 | 25.76 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_vd_26w_4s_ssld_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Res2Net50_vd_26w_4s_ssld_infer.tar) | +| Res2Net101_vd_
26w_4s_ssld | 0.839 | 0.806 | 0.033 | 6.34 | 11.02 | 16.13 | 8.35 | 45.35 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net101_vd_26w_4s_ssld_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Res2Net101_vd_26w_4s_ssld_infer.tar) | +| Res2Net200_vd_
26w_4s_ssld | 0.851 | 0.812 | 0.049 | 11.45 | 19.77 | 28.81 | 15.77 | 76.44 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net200_vd_26w_4s_ssld_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Res2Net200_vd_26w_4s_ssld_infer.tar) | +| HRNet_W18_C_ssld | 0.812 | 0.769 | 0.043 | 6.66 | 8.94 | 11.95 | 4.32 | 21.35 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W18_C_ssld_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HRNet_W18_C_ssld_infer.tar) | +| HRNet_W48_C_ssld | 0.836 | 0.790 | 0.046 | 11.07 | 17.06 | 27.28 | 17.34 | 77.57 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W48_C_ssld_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HRNet_W48_C_ssld_infer.tar) | +| SE_HRNet_W64_C_ssld | 0.848 | - | - | 17.11 | 26.87 | 43.24 | 29.00 | 129.12 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SE_HRNet_W64_C_ssld_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SE_HRNet_W64_C_ssld_infer.tar) | + + + +### 2.2 Mobile-side knowledge distillation model + +| Model | Top-1 Acc | Reference
Top-1 Acc | Acc gain | SD855 time(ms)
bs=1, thread=1 | SD855 time(ms)
bs=1, thread=2 | SD855 time(ms)
bs=1, thread=4 | FLOPs(M) | Params(M) | Model大小(M) | Pretrained Model Download Address | Inference Model Download Address | +|---------------------|-----------|-----------|---------------|----------------|-----------|----------|-----------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------| +| MobileNetV1_ssld | 0.779 | 0.710 | 0.069 | 30.24 | 17.86 | 10.30 | 578.88 | 4.25 | 16 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_ssld_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV1_ssld_infer.tar) | +| MobileNetV2_ssld | 0.767 | 0.722 | 0.045 | 20.74 | 12.71 | 8.10 | 327.84 | 3.54 | 14 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_ssld_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV2_ssld_infer.tar) | +| MobileNetV3_small_x0_35_ssld | 0.556 | 0.530 | 0.026 | 2.23 | 1.66 | 1.43 | 14.56 | 1.67 | 6.9 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_35_ssld_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_small_x0_35_ssld_infer.tar) | +| MobileNetV3_large_x1_0_ssld | 0.790 | 0.753 | 0.036 | 16.55 | 10.09 | 6.84 | 229.66 | 5.50 | 21 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_0_ssld_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_large_x1_0_ssld_infer.tar) | +| MobileNetV3_small_x1_0_ssld | 0.713 | 0.682 | 0.031 | 5.63 | 3.65 | 2.60 | 63.67 | 2.95 | 12 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_0_ssld_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_small_x1_0_ssld_infer.tar) | +| GhostNet_x1_3_ssld | 0.794 | 0.757 | 0.037 | 19.16 | 12.25 | 9.40 | 236.89 | 7.38 | 29 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_3_ssld_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/GhostNet_x1_3_ssld_infer.tar) | + + + +### 2.3 Intel-CPU-side knowledge distillation model + +| Model | Top-1 Acc | Reference
Top-1 Acc | Acc gain | Intel-Xeon-Gold-6148 time(ms)
bs=1 | FLOPs(M) | Params(M) | Pretrained Model Download Address | Inference Model Download Address | +|---------------------|-----------|-----------|---------------|----------------|----------|-----------|-----------------------------------|-----------------------------------| +| PPLCNet_x0_5_ssld | 0.661 | 0.631 | 0.030 | 2.05 | 47.28 | 1.89 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_5_ssld_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_5_ssld_infer.tar) | +| PPLCNet_x1_0_ssld | 0.744 | 0.713 | 0.033 | 2.46 | 160.81 | 2.96 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_0_ssld_pretrained.pdparams) | [Download link](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 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_5_ssld_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x2_5_ssld_infer.tar) | + + +* Note: `Reference Top-1 Acc` means the accuracy of the pre-trained model obtained by PaddleClas based on ImageNet1k dataset training. + + + +## 3. PP-LCNet series + +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.md)。 + +| Model | Top-1 Acc | Top-5 Acc | Intel-Xeon-Gold-6148 time(ms)
bs=1 | FLOPs(M) | Params(M) | Pretrained Model Download Address | Inference Model Download Address | +|:--:|:--:|:--:|:--:|----|----|----|:--:| +| PPLCNet_x0_25 |0.5186 | 0.7565 | 1.61785 | 18.25 | 1.52 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_25_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_25_infer.tar) | +| PPLCNet_x0_35 |0.5809 | 0.8083 | 2.11344 | 29.46 | 1.65 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_35_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_35_infer.tar) | +| PPLCNet_x0_5 |0.6314 | 0.8466 | 2.72974 | 47.28 | 1.89 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_5_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_5_infer.tar) | +| PPLCNet_x0_75 |0.6818 | 0.8830 | 4.51216 | 98.82 | 2.37 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_75_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_75_infer.tar) | +| PPLCNet_x1_0 |0.7132 | 0.9003 | 6.49276 | 160.81 | 2.96 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_0_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x1_0_infer.tar) | +| PPLCNet_x1_5 |0.7371 | 0.9153 | 12.2601 | 341.86 | 4.52 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_5_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x1_5_infer.tar) | +| PPLCNet_x2_0 |0.7518 | 0.9227 | 20.1667 | 590 | 6.54 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_0_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x2_0_infer.tar) | +| PPLCNet_x2_5 |0.7660 | 0.9300 | 29.595 | 906 | 9.04 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_5_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x2_5_infer.tar) | + + + +## 4. ResNet series + +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.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 | +|---------------------|-----------|-----------|-----------------------|----------------------|----------|-----------|----------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------| +| ResNet18 | 0.7098 | 0.8992 | 1.22 | 2.19 | 3.63 | 1.83 | 11.70 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet18_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet18_infer.tar) | +| ResNet18_vd | 0.7226 | 0.9080 | 1.26 | 2.28 | 3.89 | 2.07 | 11.72 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet18_vd_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet18_vd_infer.tar) | +| ResNet34 | 0.7457 | 0.9214 | 1.97 | 3.25 | 5.70 | 3.68 | 21.81 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet34_infer.tar) | +| ResNet34_vd | 0.7598 | 0.9298 | 2.00 | 3.28 | 5.84 | 3.93 | 21.84 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_vd_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet34_vd_infer.tar) | +| ResNet34_vd_ssld | 0.7972 | 0.9490 | 2.00 | 3.28 | 5.84 | 3.93 | 21.84 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_vd_ssld_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet34_vd_ssld_infer.tar) | +| ResNet50 | 0.7650 | 0.9300 | 2.54 | 4.79 | 7.40 | 4.11 | 25.61 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_infer.tar) | +| ResNet50_vc | 0.7835 | 0.9403 | 2.57 | 4.83 | 7.52 | 4.35 | 25.63 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vc_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_vc_infer.tar) | +| ResNet50_vd | 0.7912 | 0.9444 | 2.60 | 4.86 | 7.63 | 4.35 | 25.63 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_vd_infer.tar) | +| ResNet101 | 0.7756 | 0.9364 | 4.37 | 8.18 | 12.38 | 7.83 | 44.65 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet101_infer.tar) | +| ResNet101_vd | 0.8017 | 0.9497 | 4.43 | 8.25 | 12.60 | 8.08 | 44.67 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_vd_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet101_vd_infer.tar) | +| ResNet152 | 0.7826 | 0.9396 | 6.05 | 11.41 | 17.33 | 11.56 | 60.34 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet152_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet152_infer.tar) | +| ResNet152_vd | 0.8059 | 0.9530 | 6.11 | 11.51 | 17.59 | 11.80 | 60.36 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet152_vd_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet152_vd_infer.tar) | +| ResNet200_vd | 0.8093 | 0.9533 | 7.70 | 14.57 | 22.16 | 15.30 | 74.93 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet200_vd_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet200_vd_infer.tar) | +| ResNet50_vd_
ssld | 0.8300 | 0.9640 | 2.60 | 4.86 | 7.63 | 4.35 | 25.63 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_ssld_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_vd_ssld_infer.tar) | +| ResNet101_vd_
ssld | 0.8373 | 0.9669 | 4.43 | 8.25 | 12.60 | 8.08 | 44.67 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_vd_ssld_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet101_vd_ssld_infer.tar) | + + + +## 5. Mobile series + +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.md)。 + +| Model | Top-1 Acc | Top-5 Acc | SD855 time(ms)
bs=1, thread=1 | SD855 time(ms)
bs=1, thread=2 | SD855 time(ms)
bs=1, thread=4 | FLOPs(M) | Params(M) | Model大小(M) | Pretrained Model Download Address | Inference Model Download Address | +|----------------------------------|-----------|-----------|------------------------|----------|-----------|---------|-----------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------| +| MobileNetV1_
x0_25 | 0.5143 | 0.7546 | 2.88 | 1.82 | 1.26 | 43.56 | 0.48 | 1.9 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_25_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV1_x0_25_infer.tar) | +| MobileNetV1_
x0_5 | 0.6352 | 0.8473 | 8.74 | 5.26 | 3.09 | 154.57 | 1.34 | 5.2 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_5_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV1_x0_5_infer.tar) | +| MobileNetV1_
x0_75 | 0.6881 | 0.8823 | 17.84 | 10.61 | 6.21 | 333.00 | 2.60 | 10 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_75_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV1_x0_75_infer.tar) | +| MobileNetV1 | 0.7099 | 0.8968 | 30.24 | 17.86 | 10.30 | 578.88 | 4.25 | 16 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV1_infer.tar) | +| MobileNetV1_
ssld | 0.7789 | 0.9394 | 30.24 | 17.86 | 10.30 | 578.88 | 4.25 | 16 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_ssld_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV1_ssld_infer.tar) | +| MobileNetV2_
x0_25 | 0.5321 | 0.7652 | 3.46 | 2.51 | 2.03 | 34.18 | 1.53 | 6.1 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x0_25_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV2_x0_25_infer.tar) | +| MobileNetV2_
x0_5 | 0.6503 | 0.8572 | 7.69 | 4.92 | 3.57 | 99.48 | 1.98 | 7.8 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x0_5_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV2_x0_5_infer.tar) | +| MobileNetV2_
x0_75 | 0.6983 | 0.8901 | 13.69 | 8.60 | 5.82 | 197.37 | 2.65 | 10 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x0_75_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV2_x0_75_infer.tar) | +| MobileNetV2 | 0.7215 | 0.9065 | 20.74 | 12.71 | 8.10 | 327.84 | 3.54 | 14 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV2_infer.tar) | +| MobileNetV2_
x1_5 | 0.7412 | 0.9167 | 40.79 | 24.49 | 15.50 | 702.35 | 6.90 | 26 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x1_5_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV2_x1_5_infer.tar) | +| MobileNetV2_
x2_0 | 0.7523 | 0.9258 | 67.50 | 40.03 | 25.55 | 1217.25 | 11.33 | 43 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x2_0_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV2_x2_0_infer.tar) | +| MobileNetV2_
ssld | 0.7674 | 0.9339 | 20.74 | 12.71 | 8.10 | 327.84 | 3.54 | 14 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_ssld_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV2_ssld_infer.tar) | +| MobileNetV3_
large_x1_25 | 0.7641 | 0.9295 | 24.52 | 14.76 | 9.89 | 362.70 | 7.47 | 29 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_25_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_large_x1_25_infer.tar) | +| MobileNetV3_
large_x1_0 | 0.7532 | 0.9231 | 16.55 | 10.09 | 6.84 | 229.66 | 5.50 | 21 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_0_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_large_x1_0_infer.tar) | +| MobileNetV3_
large_x0_75 | 0.7314 | 0.9108 | 11.53 | 7.06 | 4.94 | 151.70 | 3.93 | 16 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x0_75_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_large_x0_75_infer.tar) | +| MobileNetV3_
large_x0_5 | 0.6924 | 0.8852 | 6.50 | 4.22 | 3.15 | 71.83 | 2.69 | 11 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x0_5_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_large_x0_5_infer.tar) | +| MobileNetV3_
large_x0_35 | 0.6432 | 0.8546 | 4.43 | 3.11 | 2.41 | 40.90 | 2.11 | 8.6 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x0_35_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_large_x0_35_infer.tar) | +| MobileNetV3_
small_x1_25 | 0.7067 | 0.8951 | 7.88 | 4.91 | 3.45 | 100.07 | 3.64 | 14 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_25_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_small_x1_25_infer.tar) | +| MobileNetV3_
small_x1_0 | 0.6824 | 0.8806 | 5.63 | 3.65 | 2.60 | 63.67 | 2.95 | 12 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_0_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_small_x1_0_infer.tar) | +| MobileNetV3_
small_x0_75 | 0.6602 | 0.8633 | 4.50 | 2.96 | 2.19 | 46.02 | 2.38 | 9.6 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_75_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_small_x0_75_infer.tar) | +| MobileNetV3_
small_x0_5 | 0.5921 | 0.8152 | 2.89 | 2.04 | 1.62 | 22.60 | 1.91 | 7.8 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_5_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_small_x0_5_infer.tar) | +| MobileNetV3_
small_x0_35 | 0.5303 | 0.7637 | 2.23 | 1.66 | 1.43 | 14.56 | 1.67 | 6.9 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_35_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_small_x0_35_infer.tar) | +| MobileNetV3_
small_x0_35_ssld | 0.5555 | 0.7771 | 2.23 | 1.66 | 1.43 | 14.56 | 1.67 | 6.9 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_35_ssld_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_small_x0_35_ssld_infer.tar) | +| MobileNetV3_
large_x1_0_ssld | 0.7896 | 0.9448 | 16.55 | 10.09 | 6.84 | 229.66 | 5.50 | 21 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_0_ssld_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_large_x1_0_ssld_infer.tar) | +| MobileNetV3_small_
x1_0_ssld | 0.7129 | 0.9010 | 5.63 | 3.65 | 2.60 | 63.67 | 2.95 | 12 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_0_ssld_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_small_x1_0_ssld_infer.tar) | +| ShuffleNetV2 | 0.6880 | 0.8845 | 9.72 | 5.97 | 4.13 | 148.86 | 2.29 | 9 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x1_0_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ShuffleNetV2_x1_0_infer.tar) | +| ShuffleNetV2_
x0_25 | 0.4990 | 0.7379 | 1.94 | 1.53 | 1.43 | 18.95 | 0.61 | 2.7 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_25_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ShuffleNetV2_x0_25_infer.tar) | +| ShuffleNetV2_
x0_33 | 0.5373 | 0.7705 | 2.23 | 1.70 | 1.79 | 24.04 | 0.65 | 2.8 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_33_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ShuffleNetV2_x0_33_infer.tar) | +| ShuffleNetV2_
x0_5 | 0.6032 | 0.8226 | 3.67 | 2.63 | 2.06 | 42.58 | 1.37 | 5.6 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_5_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ShuffleNetV2_x0_5_infer.tar) | +| ShuffleNetV2_
x1_5 | 0.7163 | 0.9015 | 17.21 | 10.56 | 6.81 | 301.35 | 3.53 | 14 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x1_5_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ShuffleNetV2_x1_5_infer.tar) | +| ShuffleNetV2_
x2_0 | 0.7315 | 0.9120 | 31.21 | 18.98 | 11.65 | 571.70 | 7.40 | 28 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x2_0_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ShuffleNetV2_x2_0_infer.tar) | +| ShuffleNetV2_
swish | 0.7003 | 0.8917 | 31.21 | 9.06 | 5.74 | 148.86 | 2.29 | 9.1 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_swish_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ShuffleNetV2_swish_infer.tar) | +| GhostNet_
x0_5 | 0.6688 | 0.8695 | 5.28 | 3.95 | 3.29 | 46.15 | 2.60 | 10 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x0_5_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/GhostNet_x0_5_infer.tar) | +| GhostNet_
x1_0 | 0.7402 | 0.9165 | 12.89 | 8.66 | 6.72 | 148.78 | 5.21 | 20 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_0_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/GhostNet_x1_0_infer.tar) | +| GhostNet_
x1_3 | 0.7579 | 0.9254 | 19.16 | 12.25 | 9.40 | 236.89 | 7.38 | 29 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_3_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/GhostNet_x1_3_infer.tar) | +| GhostNet_
x1_3_ssld | 0.7938 | 0.9449 | 19.16 | 12.25 | 9.40 | 236.89 | 7.38 | 29 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_3_ssld_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/GhostNet_x1_3_ssld_infer.tar) | +| ESNet_x0_25 | 0.6248 | 0.8346 |4.12|2.97|2.51| 30.85 | 2.83 | 11 |[Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ESNet_x0_25_pretrained.pdparams) |[Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ESNet_x0_25_infer.tar) | +| ESNet_x0_5 | 0.6882 | 0.8804 |6.45|4.42|3.35| 67.31 | 3.25 | 13 |[Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ESNet_x0_5_pretrained.pdparams) |[Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ESNet_x0_5_infer.tar) | +| ESNet_x0_75 | 0.7224 | 0.9045 |9.59|6.28|4.52| 123.74 | 3.87 | 15 |[Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ESNet_x0_75_pretrained.pdparams) |[Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ESNet_x0_75_infer.tar) | +| ESNet_x1_0 | 0.7392 | 0.9140 |13.67|8.71|5.97| 197.33 | 4.64 | 18 |[Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ESNet_x1_0_pretrained.pdparams) |[Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ESNet_x1_0_infer.tar) | + + + +## 6. SEResNeXt and Res2Net series + +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.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 | +|---------------------------|-----------|-----------|-----------------------|----------------------|----------|-----------|----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------| +| Res2Net50_
26w_4s | 0.7933 | 0.9457 | 3.52 | 6.23 | 9.30 | 4.28 | 25.76 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_26w_4s_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Res2Net50_26w_4s_infer.tar) | +| Res2Net50_vd_
26w_4s | 0.7975 | 0.9491 | 3.59 | 6.35 | 9.50 | 4.52 | 25.78 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_vd_26w_4s_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Res2Net50_vd_26w_4s_infer.tar) | +| Res2Net50_
14w_8s | 0.7946 | 0.9470 | 4.39 | 7.21 | 10.38 | 4.20 | 25.12 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_14w_8s_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Res2Net50_14w_8s_infer.tar) | +| Res2Net101_vd_
26w_4s | 0.8064 | 0.9522 | 6.34 | 11.02 | 16.13 | 8.35 | 45.35 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net101_vd_26w_4s_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Res2Net101_vd_26w_4s_infer.tar) | +| Res2Net200_vd_
26w_4s | 0.8121 | 0.9571 | 11.45 | 19.77 | 28.81 | 15.77 | 76.44 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net200_vd_26w_4s_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Res2Net200_vd_26w_4s_infer.tar) | +| Res2Net200_vd_
26w_4s_ssld | 0.8513 | 0.9742 | 11.45 | 19.77 | 28.81 | 15.77 | 76.44 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net200_vd_26w_4s_ssld_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Res2Net200_vd_26w_4s_ssld_infer.tar) | +| ResNeXt50_
32x4d | 0.7775 | 0.9382 | 5.07 | 8.49 | 12.02 | 4.26 | 25.10 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_32x4d_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt50_32x4d_infer.tar) | +| ResNeXt50_vd_
32x4d | 0.7956 | 0.9462 | 5.29 | 8.68 | 12.33 | 4.50 | 25.12 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_vd_32x4d_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt50_vd_32x4d_infer.tar) | +| ResNeXt50_
64x4d | 0.7843 | 0.9413 | 9.39 | 13.97 | 20.56 | 8.02 | 45.29 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_64x4d_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt50_64x4d_infer.tar) | +| ResNeXt50_vd_
64x4d | 0.8012 | 0.9486 | 9.75 | 14.14 | 20.84 | 8.26 | 45.31 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_vd_64x4d_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt50_vd_64x4d_infer.tar) | +| ResNeXt101_
32x4d | 0.7865 | 0.9419 | 11.34 | 16.78 | 22.80 | 8.01 | 44.32 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x4d_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt101_32x4d_infer.tar) | +| ResNeXt101_vd_
32x4d | 0.8033 | 0.9512 | 11.36 | 17.01 | 23.07 | 8.25 | 44.33 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_vd_32x4d_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt101_vd_32x4d_infer.tar) | +| ResNeXt101_
64x4d | 0.7835 | 0.9452 | 21.57 | 28.08 | 39.49 | 15.52 | 83.66 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_64x4d_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt101_64x4d_infer.tar) | +| ResNeXt101_vd_
64x4d | 0.8078 | 0.9520 | 21.57 | 28.22 | 39.70 | 15.76 | 83.68 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_vd_64x4d_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt101_vd_64x4d_infer.tar) | +| ResNeXt152_
32x4d | 0.7898 | 0.9433 | 17.14 | 25.11 | 33.79 | 11.76 | 60.15 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_32x4d_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt152_32x4d_infer.tar) | +| ResNeXt152_vd_
32x4d | 0.8072 | 0.9520 | 16.99 | 25.29 | 33.85 | 12.01 | 60.17 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_vd_32x4d_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt152_vd_32x4d_infer.tar) | +| ResNeXt152_
64x4d | 0.7951 | 0.9471 | 33.07 | 42.05 | 59.13 | 23.03 | 115.27 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_64x4d_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt152_64x4d_infer.tar) | +| ResNeXt152_vd_
64x4d | 0.8108 | 0.9534 | 33.30 | 42.41 | 59.42 | 23.27 | 115.29 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_vd_64x4d_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt152_vd_64x4d_infer.tar) | +| SE_ResNet18_vd | 0.7333 | 0.9138 | 1.48 | 2.70 | 4.32 | 2.07 | 11.81 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNet18_vd_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SE_ResNet18_vd_infer.tar) | +| SE_ResNet34_vd | 0.7651 | 0.9320 | 2.42 | 3.69 | 6.29 | 3.93 | 22.00 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNet34_vd_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SE_ResNet34_vd_infer.tar) | +| SE_ResNet50_vd | 0.7952 | 0.9475 | 3.11 | 5.99 | 9.34 | 4.36 | 28.16 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNet50_vd_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SE_ResNet50_vd_infer.tar) | +| SE_ResNeXt50_
32x4d | 0.7844 | 0.9396 | 6.39 | 11.01 | 14.94 | 4.27 | 27.63 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt50_32x4d_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SE_ResNeXt50_32x4d_infer.tar) | +| SE_ResNeXt50_vd_
32x4d | 0.8024 | 0.9489 | 7.04 | 11.57 | 16.01 | 5.64 | 27.76 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt50_vd_32x4d_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SE_ResNeXt50_vd_32x4d_infer.tar) | +| SE_ResNeXt101_
32x4d | 0.7939 | 0.9443 | 13.31 | 21.85 | 28.77 | 8.03 | 49.09 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt101_32x4d_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SE_ResNeXt101_32x4d_infer.tar) | +| SENet154_vd | 0.8140 | 0.9548 | 34.83 | 51.22 | 69.74 | 24.45 | 122.03 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SENet154_vd_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SENet154_vd_infer.tar) | + + + +## 7. DPN and DenseNet series + +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.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 | +|-------------|-----------|-----------|-----------------------|----------------------|----------|-----------|--------------------------------------------------------------------------------------|-------------|-------------| +| DenseNet121 | 0.7566 | 0.9258 | 3.40 | 6.94 | 9.17 | 2.87 | 8.06 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet121_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DenseNet121_infer.tar) | +| DenseNet161 | 0.7857 | 0.9414 | 7.06 | 14.37 | 19.55 | 7.79 | 28.90 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet161_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DenseNet161_infer.tar) | +| DenseNet169 | 0.7681 | 0.9331 | 5.00 | 10.29 | 12.84 | 3.40 | 14.31 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet169_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DenseNet169_infer.tar) | +| DenseNet201 | 0.7763 | 0.9366 | 6.38 | 13.72 | 17.17 | 4.34 | 20.24 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet201_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DenseNet201_infer.tar) | +| DenseNet264 | 0.7796 | 0.9385 | 9.34 | 20.95 | 25.41 | 5.82 | 33.74 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet264_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DenseNet264_infer.tar) | +| DPN68 | 0.7678 | 0.9343 | 8.18 | 11.40 | 14.82 | 2.35 | 12.68 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN68_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DPN68_infer.tar) | +| DPN92 | 0.7985 | 0.9480 | 12.48 | 20.04 | 25.10 | 6.54 | 37.79 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN92_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DPN92_infer.tar) | +| DPN98 | 0.8059 | 0.9510 | 14.70 | 25.55 | 35.12 | 11.728 | 61.74 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN98_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DPN98_infer.tar) | +| 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 + +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.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 | +|-------------|-----------|-----------|------------------|------------------|----------|-----------|--------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------| +| HRNet_W18_C | 0.7692 | 0.9339 | 6.66 | 8.94 | 11.95 | 4.32 | 21.35 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W18_C_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HRNet_W18_C_infer.tar) | +| HRNet_W18_C_ssld | 0.81162 | 0.95804 | 6.66 | 8.94 | 11.95 | 4.32 | 21.35 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W18_C_ssld_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HRNet_W18_C_ssld_infer.tar) | +| HRNet_W30_C | 0.7804 | 0.9402 | 8.61 | 11.40 | 15.23 | 8.15 | 37.78 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W30_C_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HRNet_W30_C_infer.tar) | +| HRNet_W32_C | 0.7828 | 0.9424 | 8.54 | 11.58 | 15.57 | 8.97 | 41.30 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W32_C_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HRNet_W32_C_infer.tar) | +| HRNet_W40_C | 0.7877 | 0.9447 | 9.83 | 15.02 | 20.92 | 12.74 | 57.64 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W40_C_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HRNet_W40_C_infer.tar) | +| HRNet_W44_C | 0.7900 | 0.9451 | 10.62 | 16.18 | 25.92 | 14.94 | 67.16 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W44_C_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HRNet_W44_C_infer.tar) | +| HRNet_W48_C | 0.7895 | 0.9442 | 11.07 | 17.06 | 27.28 | 17.34 | 77.57 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W48_C_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HRNet_W48_C_infer.tar) | +| HRNet_W48_C_ssld | 0.8363 | 0.9682 | 11.07 | 17.06 | 27.28 | 17.34 | 77.57 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W48_C_ssld_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HRNet_W48_C_ssld_infer.tar) | +| HRNet_W64_C | 0.7930 | 0.9461 | 13.82 | 21.15 | 35.51 | 28.97 | 128.18 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W64_C_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HRNet_W64_C_infer.tar) | +| SE_HRNet_W64_C_ssld | 0.8475 | 0.9726 | 17.11 | 26.87 | 43.24 | 29.00 | 129.12 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SE_HRNet_W64_C_ssld_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SE_HRNet_W64_C_ssld_infer.tar) | + + + +## 9. Inception series + +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.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 | +|--------------------|-----------|-----------|-----------------------|----------------------|----------|-----------|---------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------| +| GoogLeNet | 0.7070 | 0.8966 | 1.41 | 3.25 | 5.00 | 1.44 | 11.54 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GoogLeNet_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/GoogLeNet_infer.tar) | +| Xception41 | 0.7930 | 0.9453 | 3.58 | 8.76 | 16.61 | 8.57 | 23.02 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception41_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Xception41_infer.tar) | +| Xception41_deeplab | 0.7955 | 0.9438 | 3.81 | 9.16 | 17.20 | 9.28 | 27.08 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception41_deeplab_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Xception41_deeplab_infer.tar) | +| Xception65 | 0.8100 | 0.9549 | 5.45 | 12.78 | 24.53 | 13.25 | 36.04 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception65_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Xception65_infer.tar) | +| Xception65_deeplab | 0.8032 | 0.9449 | 5.65 | 13.08 | 24.61 | 13.96 | 40.10 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception65_deeplab_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Xception65_deeplab_infer.tar) | +| Xception71 | 0.8111 | 0.9545 | 6.19 | 15.34 | 29.21 | 16.21 | 37.86 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception71_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Xception71_infer.tar) | +| InceptionV3 | 0.7914 | 0.9459 | 4.78 | 8.53 | 12.28 | 5.73 | 23.87 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/InceptionV3_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/InceptionV3_infer.tar) | +| InceptionV4 | 0.8077 | 0.9526 | 8.93 | 15.17 | 21.56 | 12.29 | 42.74 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/InceptionV4_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/InceptionV4_infer.tar) | + + + +## 10. EfficientNet and ResNeXt101_wsl series + +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.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 | +|---------------------------|-----------|-----------|------------------|------------------|----------|-----------|----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------| +| ResNeXt101_
32x8d_wsl | 0.8255 | 0.9674 | 13.55 | 23.39 | 36.18 | 16.48 | 88.99 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x8d_wsl_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt101_32x8d_wsl_infer.tar) | +| ResNeXt101_
32x16d_wsl | 0.8424 | 0.9726 | 21.96 | 38.35 | 63.29 | 36.26 | 194.36 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x16d_wsl_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt101_32x16d_wsl_infer.tar) | +| ResNeXt101_
32x32d_wsl | 0.8497 | 0.9759 | 37.28 | 76.50 | 121.56 | 87.28 | 469.12 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x32d_wsl_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt101_32x32d_wsl_infer.tar) | +| ResNeXt101_
32x48d_wsl | 0.8537 | 0.9769 | 55.07 | 124.39 | 205.01 | 153.57 | 829.26 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x48d_wsl_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt101_32x48d_wsl_infer.tar) | +| Fix_ResNeXt101_
32x48d_wsl | 0.8626 | 0.9797 | 55.01 | 122.63 | 204.66 | 313.41 | 829.26 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Fix_ResNeXt101_32x48d_wsl_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Fix_ResNeXt101_32x48d_wsl_infer.tar) | +| EfficientNetB0 | 0.7738 | 0.9331 | 1.96 | 3.71 | 5.56 | 0.40 | 5.33 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB0_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/EfficientNetB0_infer.tar) | +| EfficientNetB1 | 0.7915 | 0.9441 | 2.88 | 5.40 | 7.63 | 0.71 | 7.86 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB1_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/EfficientNetB1_infer.tar) | +| EfficientNetB2 | 0.7985 | 0.9474 | 3.26 | 6.20 | 9.17 | 1.02 | 9.18 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB2_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/EfficientNetB2_infer.tar) | +| EfficientNetB3 | 0.8115 | 0.9541 | 4.52 | 8.85 | 13.54 | 1.88 | 12.324 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB3_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/EfficientNetB3_infer.tar) | +| EfficientNetB4 | 0.8285 | 0.9623 | 6.78 | 15.47 | 24.95 | 4.51 | 19.47 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB4_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/EfficientNetB4_infer.tar) | +| EfficientNetB5 | 0.8362 | 0.9672 | 10.97 | 27.24 | 45.93 | 10.51 | 30.56 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB5_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/EfficientNetB5_infer.tar) | +| EfficientNetB6 | 0.8400 | 0.9688 | 17.09 | 43.32 | 76.90 | 19.47 | 43.27 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB6_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/EfficientNetB6_infer.tar) | +| EfficientNetB7 | 0.8430 | 0.9689 | 25.91 | 71.23 | 128.20 | 38.45 | 66.66 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB7_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/EfficientNetB7_infer.tar) | +| EfficientNetB0_
small | 0.7580 | 0.9258 | 1.24 | 2.59 | 3.92 | 0.40 | 4.69 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB0_small_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/EfficientNetB0_small_infer.tar) | + + + +## 11. ResNeSt and RegNet series + +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.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 | +|------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------| +| ResNeSt50_
fast_1s1x64d | 0.8035 | 0.9528 | 2.73 | 5.33 | 8.24 | 4.36 | 26.27 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeSt50_fast_1s1x64d_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeSt50_fast_1s1x64d_infer.tar) | +| ResNeSt50 | 0.8083 | 0.9542 | 7.36 | 10.23 | 13.84 | 5.40 | 27.54 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeSt50_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeSt50_infer.tar) | +| RegNetX_4GF | 0.785 | 0.9416 | 6.46 | 8.48 | 11.45 | 4.00 | 22.23 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetX_4GF_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RegNetX_4GF_infer.tar) | + + + +## 12. ViT and DeiT series + +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.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) | +| ViT_base_
patch16_224 | 0.8195 | 0.9617 | 6.12 | 14.84 | 28.51 | 16.85 | 86.42 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch16_224_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_base_patch16_224_infer.tar) | +| ViT_base_
patch16_384 | 0.8414 | 0.9717 | 14.15 | 48.38 | 95.06 | 49.35 | 86.42 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch16_384_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_base_patch16_384_infer.tar) | +| ViT_base_
patch32_384 | 0.8176 | 0.9613 | 4.94 | 13.43 | 24.08 | 12.66 | 88.19 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch32_384_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_base_patch32_384_infer.tar) | +| ViT_large_
patch16_224 | 0.8323 | 0.9650 | 15.53 | 49.50 | 94.09 | 59.65 | 304.12 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch16_224_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_large_patch16_224_infer.tar) | +|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 | Flops(G) | Params(M) | Download Address | -|---------------------------|-----------|-----------|------------------|------------------|----------|-----------|----------------------------------------------------------------------------------------------------| -| ResNeXt101_
32x8d_wsl | 0.8255 | 0.9674 | 18.52528 | 34.25319 | 29.14 | 78.44 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x8d_wsl_pretrained.pdparams) | -| ResNeXt101_
32x16d_wsl | 0.8424 | 0.9726 | 25.60395 | 71.88384 | 57.55 | 152.66 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x16d_wsl_pretrained.pdparams) | -| ResNeXt101_
32x32d_wsl | 0.8497 | 0.9759 | 54.87396 | 160.04337 | 115.17 | 303.11 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x32d_wsl_pretrained.pdparams) | -| ResNeXt101_
32x48d_wsl | 0.8537 | 0.9769 | 99.01698256 | 315.91261 | 173.58 | 456.2 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x48d_wsl_pretrained.pdparams) | -| Fix_ResNeXt101_
32x48d_wsl | 0.8626 | 0.9797 | 160.0838242 | 595.99296 | 354.23 | 456.2 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Fix_ResNeXt101_32x48d_wsl_pretrained.pdparams) | -| EfficientNetB0 | 0.7738 | 0.9331 | 3.442 | 6.11476 | 0.72 | 5.1 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB0_pretrained.pdparams) | -| EfficientNetB1 | 0.7915 | 0.9441 | 5.3322 | 9.41795 | 1.27 | 7.52 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB1_pretrained.pdparams) | -| EfficientNetB2 | 0.7985 | 0.9474 | 6.29351 | 10.95702 | 1.85 | 8.81 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB2_pretrained.pdparams) | -| EfficientNetB3 | 0.8115 | 0.9541 | 7.67749 | 16.53288 | 3.43 | 11.84 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB3_pretrained.pdparams) | -| EfficientNetB4 | 0.8285 | 0.9623 | 12.15894 | 30.94567 | 8.29 | 18.76 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB4_pretrained.pdparams) | -| EfficientNetB5 | 0.8362 | 0.9672 | 20.48571 | 61.60252 | 19.51 | 29.61 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB5_pretrained.pdparams) | -| EfficientNetB6 | 0.8400 | 0.9688 | 32.62402 | - | 36.27 | 42 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB6_pretrained.pdparams) | -| EfficientNetB7 | 0.8430 | 0.9689 | 53.93823 | - | 72.35 | 64.92 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB7_pretrained.pdparams) | -| EfficientNetB0_
small | 0.7580 | 0.9258 | 2.3076 | 4.71886 | 0.72 | 4.65 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB0_small_pretrained.pdparams) | - - - -### ResNeSt and RegNet series - -Accuracy and inference time metrics of ResNeSt and RegNet series models are shown as follows. More detailed information can be refered to [ResNeSt and RegNet series tutorial](../en/models/ResNeSt_RegNet_en.md). - - -| Model | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | Flops(G) | Params(M) | Download Address | -|------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------| -| ResNeSt50_
fast_1s1x64d | 0.8035 | 0.9528 | 3.45405 | 8.72680 | 8.68 | 26.3 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeSt50_fast_1s1x64d_pretrained.pdparams) | -| ResNeSt50 | 0.8083 | 0.9542 | 6.69042 | 8.01664 | 10.78 | 27.5 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeSt50_pretrained.pdparams) | -| RegNetX_4GF | 0.785 | 0.9416 | 6.46478 | 11.19862 | 8 | 22.1 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetX_4GF_pretrained.pdparams) | - -### ViT_DeiT series +| 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) | +| DeiT_small_
patch16_224 | 0.796 | 0.949 | 3.61 | 6.24 | 10.49 | 4.24 | 21.97 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_small_patch16_224_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_small_patch16_224_infer.tar) | +| DeiT_base_
patch16_224 | 0.817 | 0.957 | 6.13 | 14.87 | 28.50 | 16.85 | 86.42 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_patch16_224_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_base_patch16_224_infer.tar) | +| DeiT_base_
patch16_384 | 0.830 | 0.962 | 14.12 | 48.80 | 97.60 | 49.35 | 86.42 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_patch16_384_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_base_patch16_384_infer.tar) | +| DeiT_tiny_
distilled_patch16_224 | 0.741 | 0.918 | 3.51 | 4.05 | 6.03 | 1.08 | 5.87 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_tiny_distilled_patch16_224_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_tiny_distilled_patch16_224_infer.tar) | +| DeiT_small_
distilled_patch16_224 | 0.809 | 0.953 | 3.70 | 6.20 | 10.53 | 4.26 | 22.36 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_small_distilled_patch16_224_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_small_distilled_patch16_224_infer.tar) | +| DeiT_base_
distilled_patch16_224 | 0.831 | 0.964 | 6.17 | 14.94 | 28.58 | 16.93 | 87.18 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_distilled_patch16_224_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_base_distilled_patch16_224_infer.tar) | +| DeiT_base_
distilled_patch16_384 | 0.851 | 0.973 | 14.12 | 48.76 | 97.09 | 49.43 | 87.18 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_distilled_patch16_384_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_base_distilled_patch16_384_infer.tar) | -Accuracy and inference time metrics of ViT and DeiT series models are shown as follows. More detailed information can be refered to [Transformer series tutorial](../en/models/ViT_and_DeiT_en.md). + +## 13. RepVGG series -| Model | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | Flops(G) | Params(M) | Download Address | -|------------------------|-----------|-----------|------------------|------------------|----------|------------------------|------------------------| -| ViT_small_
patch16_224 | 0.7769 | 0.9342 | - | - | | | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_small_patch16_224_pretrained.pdparams) | -| ViT_base_
patch16_224 | 0.8195 | 0.9617 | - | - | | 86 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch16_224_pretrained.pdparams) | -| ViT_base_
patch16_384 | 0.8414 | 0.9717 | - | - | | | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch16_384_pretrained.pdparams) | -| ViT_base_
patch32_384 | 0.8176 | 0.9613 | - | - | | | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch32_384_pretrained.pdparams) | -| ViT_large_
patch16_224 | 0.8323 | 0.9650 | - | - | | 307 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch16_224_pretrained.pdparams) | -| ViT_large_
patch16_384 | 0.8513 | 0.9736 | - | - | | | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch16_384_pretrained.pdparams) | -| ViT_large_
patch32_384 | 0.8153 | 0.9608 | - | - | | | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch32_384_pretrained.pdparams) | -| | | | | | | | | +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.md). -| Model | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | Flops(G) | Params(M) | Download Address | -|------------------------|-----------|-----------|------------------|------------------|----------|------------------------|------------------------| -| DeiT_tiny_
patch16_224 | 0.718 | 0.910 | - | - | | 5 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_tiny_patch16_224_pretrained.pdparams) | -| DeiT_small_
patch16_224 | 0.796 | 0.949 | - | - | | 22 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_small_patch16_224_pretrained.pdparams) | -| DeiT_base_
patch16_224 | 0.817 | 0.957 | - | - | | 86 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_patch16_224_pretrained.pdparams) | -| DeiT_base_
patch16_384 | 0.830 | 0.962 | - | - | | 87 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_patch16_384_pretrained.pdparams) | -| DeiT_tiny_
distilled_patch16_224 | 0.741 | 0.918 | - | - | | 6 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_tiny_distilled_patch16_224_pretrained.pdparams) | -| DeiT_small_
distilled_patch16_224 | 0.809 | 0.953 | - | - | | 22 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_small_distilled_patch16_224_pretrained.pdparams) | -| DeiT_base_
distilled_patch16_224 | 0.831 | 0.964 | - | - | | 87 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_distilled_patch16_224_pretrained.pdparams) | -| DeiT_base_
distilled_patch16_384 | 0.851 | 0.973 | - | - | | 88 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_distilled_patch16_384_pretrained.pdparams) | -| | | | | | | | | +| 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) | +| RepVGG_A1 | 0.7380 | 0.9146 | | | | 2.37 | 12.79 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A1_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_A1_infer.tar) | +| RepVGG_A2 | 0.7571 | 0.9264 | | | | 5.12 | 25.50 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A2_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_A2_infer.tar) | +| RepVGG_B0 | 0.7450 | 0.9213 | | | | 3.06 | 14.34 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B0_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_B0_infer.tar) | +| RepVGG_B1 | 0.7773 | 0.9385 | | | | 11.82 | 51.83 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_B1_infer.tar) | +| RepVGG_B2 | 0.7813 | 0.9410 | | | | 18.38 | 80.32 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B2_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_B2_infer.tar) | +| RepVGG_B1g2 | 0.7732 | 0.9359 | | | | 8.82 | 41.36 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1g2_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_B1g2_infer.tar) | +| RepVGG_B1g4 | 0.7675 | 0.9335 | | | | 7.31 | 36.13 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1g4_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_B1g4_infer.tar) | +| RepVGG_B2g4 | 0.7881 | 0.9448 | | | | 11.34 | 55.78 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B2g4_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_B2g4_infer.tar) | +| RepVGG_B3g4 | 0.7965 | 0.9485 | | | | 16.07 | 75.63 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B3g4_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_B3g4_infer.tar) | + - +## 14. MixNet series -### RepVGG +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.md). -Accuracy and inference time metrics of RepVGG series models are shown as follows. More detailed information can be refered to [RepVGG series tutorial](../en/models/RepVGG_en.md). +| Model | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | time(ms)
bs=8 | FLOPs(M) | Params(M) | Pretrained Model Download Address | Inference Model Download Address | +| -------- | --------- | --------- | ---------------- | ---------------- | ----------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | +| MixNet_S | 0.7628 | 0.9299 | 2.31 | 3.63 | 5.20 | 252.977 | 4.167 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MixNet_S_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MixNet_S_infer.tar) | +| MixNet_M | 0.7767 | 0.9364 | 2.84 | 4.60 | 6.62 | 357.119 | 5.065 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MixNet_M_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MixNet_M_infer.tar) | +| MixNet_L | 0.7860 | 0.9437 | 3.16 | 5.55 | 8.03 | 579.017 | 7.384 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MixNet_L_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MixNet_L_infer.tar) | + -| Model | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | Flops(G) | Params(M) | Download Address | -|------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------| -| RepVGG_A0 | 0.7131 | 0.9016 | | | | | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A0_pretrained.pdparams) | -| RepVGG_A1 | 0.7380 | 0.9146 | | | | | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A1_pretrained.pdparams) | -| RepVGG_A2 | 0.7571 | 0.9264 | | | | | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A2_pretrained.pdparams) | -| RepVGG_B0 | 0.7450 | 0.9213 | | | | | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B0_pretrained.pdparams) | -| RepVGG_B1 | 0.7773 | 0.9385 | | | | | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1_pretrained.pdparams) | -| RepVGG_B2 | 0.7813 | 0.9410 | | | | | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B2_pretrained.pdparams) | -| RepVGG_B1g2 | 0.7732 | 0.9359 | | | | | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1g2_pretrained.pdparams) | -| RepVGG_B1g4 | 0.7675 | 0.9335 | | | | | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1g4_pretrained.pdparams) | -| RepVGG_B2g4 | 0.7881 | 0.9448 | | | | | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B2g4_pretrained.pdparams) | -| RepVGG_B3g4 | 0.7965 | 0.9485 | | | | | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B3g4_pretrained.pdparams) | +## 15. ReXNet series - +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.md). -### MixNet +| 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 | +| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | +| ReXNet_1_0 | 0.7746 | 0.9370 | 3.08 | 4.15 | 5.49 | 0.415 | 4.84 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_1_0_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ReXNet_1_0_infer.tar) | +| ReXNet_1_3 | 0.7913 | 0.9464 | 3.54 | 4.87 | 6.54 | 0.68 | 7.61 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_1_3_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ReXNet_1_3_infer.tar) | +| ReXNet_1_5 | 0.8006 | 0.9512 | 3.68 | 5.31 | 7.38 | 0.90 | 9.79 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_1_5_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ReXNet_1_5_infer.tar) | +| ReXNet_2_0 | 0.8122 | 0.9536 | 4.30 | 6.54 | 9.19 | 1.56 | 16.45 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_2_0_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ReXNet_2_0_infer.tar) | +| ReXNet_3_0 | 0.8209 | 0.9612 | 5.74 | 9.49 | 13.62 | 3.44 | 34.83 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_3_0_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ReXNet_3_0_infer.tar) | -Accuracy and inference time metrics of MixNet series models are shown as follows. More detailed information can be refered to [MixNet series tutorial](../en/models/MixNet_en.md). + -| Model | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | Flops(M) | Params(M) | Download Address | -| -------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | -| MixNet_S | 0.7628 | 0.9299 | | | 252.977 | 4.167 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MixNet_S_pretrained.pdparams) | -| MixNet_M | 0.7767 | 0.9364 | | | 357.119 | 5.065 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MixNet_M_pretrained.pdparams) | -| MixNet_L | 0.7860 | 0.9437 | | | 579.017 | 7.384 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MixNet_L_pretrained.pdparams) | +## 16. SwinTransformer series - +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.md). -### ReXNet +| 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 | +| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | +| SwinTransformer_tiny_patch4_window7_224 | 0.8069 | 0.9534 | 6.59 | 9.68 | 16.32 | 4.35 | 28.26 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_tiny_patch4_window7_224_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_tiny_patch4_window7_224_infer.tar) | +| SwinTransformer_small_patch4_window7_224 | 0.8275 | 0.9613 | 12.54 | 17.07 | 28.08 | 8.51 | 49.56 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_small_patch4_window7_224_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_small_patch4_window7_224_infer.tar) | +| SwinTransformer_base_patch4_window7_224 | 0.8300 | 0.9626 | 13.37 | 23.53 | 39.11 | 15.13 | 87.70 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window7_224_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window7_224_infer.tar) | +| SwinTransformer_base_patch4_window12_384 | 0.8439 | 0.9693 | 19.52 | 64.56 | 123.30 | 44.45 | 87.70 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window12_384_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window12_384_infer.tar) | +| SwinTransformer_base_patch4_window7_224[1] | 0.8487 | 0.9746 | 13.53 | 23.46 | 39.13 | 15.13 | 87.70 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window7_224_22kto1k_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window7_224_infer.tar) | +| SwinTransformer_base_patch4_window12_384[1] | 0.8642 | 0.9807 | 19.65 | 64.72 | 123.42 | 44.45 | 87.70 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window12_384_22kto1k_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window12_384_infer.tar) | +| SwinTransformer_large_patch4_window7_224[1] | 0.8596 | 0.9783 | 15.74 | 38.57 | 71.49 | 34.02 | 196.43 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window7_224_22kto1k_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_large_patch4_window7_224_infer.tar) | +| SwinTransformer_large_patch4_window12_384[1] | 0.8719 | 0.9823 | 32.61 | 116.59 | 223.23 | 99.97 | 196.43 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window12_384_22kto1k_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_large_patch4_window12_384_infer.tar) | -Accuracy and inference time metrics of ReXNet series models are shown as follows. More detailed information can be refered to [ReXNet series tutorial](../en/models/ReXNet_en.md). +[1]：It is pre-trained based on the ImageNet22k dataset, and then transferred and learned from the ImageNet1k dataset. -| Model | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | Flops(G) | Params(M) | Download Address | -| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | -| ReXNet_1_0 | 0.7746 | 0.9370 | | | 0.415 | 4.838 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_1_0_pretrained.pdparams) | -| ReXNet_1_3 | 0.7913 | 0.9464 | | | 0.683 | 7.611 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_1_3_pretrained.pdparams) | -| ReXNet_1_5 | 0.8006 | 0.9512 | | | 0.900 | 9.791 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_1_5_pretrained.pdparams) | -| ReXNet_2_0 | 0.8122 | 0.9536 | | | 1.561 | 16.449 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_2_0_pretrained.pdparams) | -| ReXNet_3_0 | 0.8209 | 0.9612 | | | 3.445 | 34.833 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_3_0_pretrained.pdparams) | + - +## 17. LeViT series - -### SwinTransformer +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.md). -Accuracy and inference time metrics of SwinTransformer series models are shown as follows. More detailed information can be refered to[SwinTransformer series tutorial](../en/models/SwinTransformer_en.md). +| Model | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | time(ms)
bs=8 | FLOPs(M) | Params(M) | Pretrained Model Download Address | Inference Model Download Address | +| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | +| LeViT_128S | 0.7598 | 0.9269 | | | | 281 | 7.42 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_128S_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/eViT_128S_infer.tar) | +| LeViT_128 | 0.7810 | 0.9371 | | | | 365 | 8.87 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_128_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/LeViT_128_infer.tar) | +| LeViT_192 | 0.7934 | 0.9446 | | | | 597 | 10.61 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_192_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/LeViT_192_infer.tar) | +| LeViT_256 | 0.8085 | 0.9497 | | | | 1049 | 18.45 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_256_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/LeViT_256_infer.tar) | +| LeViT_384 | 0.8191 | 0.9551 | | | | 2234 | 38.45 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_384_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/LeViT_384_infer.tar) | -| Model | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | Flops(G) | Params(M) | Download Address | -| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | -| SwinTransformer_tiny_patch4_window7_224 | 0.8069 | 0.9534 | | | 4.5 | 28 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_tiny_patch4_window7_224_pretrained.pdparams) | -| SwinTransformer_small_patch4_window7_224 | 0.8275 | 0.9613 | | | 8.7 | 50 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_small_patch4_window7_224_pretrained.pdparams) | -| SwinTransformer_base_patch4_window7_224 | 0.8300 | 0.9626 | | | 15.4 | 88 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window7_224_pretrained.pdparams) | -| SwinTransformer_base_patch4_window12_384 | 0.8439 | 0.9693 | | | 47.1 | 88 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window12_384_pretrained.pdparams) | -| SwinTransformer_base_patch4_window7_224[1] | 0.8487 | 0.9746 | | | 15.4 | 88 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window7_224_22kto1k_pretrained.pdparams) | -| SwinTransformer_base_patch4_window12_384[1] | 0.8642 | 0.9807 | | | 47.1 | 88 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window12_384_22kto1k_pretrained.pdparams) | -| SwinTransformer_large_patch4_window7_224[1] | 0.8596 | 0.9783 | | | 34.5 | 197 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window7_224_22kto1k_pretrained.pdparams) | -| SwinTransformer_large_patch4_window12_384[1] | 0.8719 | 0.9823 | | | 103.9 | 197 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window12_384_22kto1k_pretrained.pdparams) | +**Note**: The accuracy difference with Reference is due to the difference in data preprocessing and the use of no distilled head as output. -[1] Based on the pre-trained model of the ImageNet22k dataset, it is obtained by finetuning from the ImageNet1k data set. + - -### LeViT +## 18. Twins series -Accuracy and inference time metrics of LeViT series models are shown as follows. More detailed information can be refered to[LeViT series tutorial](../en/models/LeViT_en.md). +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.md). -| Model | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | Flops(M) | Params(M) | Download Address | -| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | -| LeViT_128S | 0.7598 | 0.9269 | | | 305 | 7.8 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_128S_pretrained.pdparams) | -| LeViT_128 | 0.7810 | 0.9371 | | | 406 | 9.2 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_128_pretrained.pdparams) | -| LeViT_192 | 0.7934 | 0.9446 | | | 658 | 11 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_192_pretrained.pdparams) | -| LeViT_256 | 0.8085 | 0.9497 | | | 1120 | 19 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_256_pretrained.pdparams) | -| LeViT_384 | 0.8191 | 0.9551 | | | 2353 | 39 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_384_pretrained.pdparams) | +| 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 | +| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | +| pcpvt_small | 0.8082 | 0.9552 | 7.32 | 10.51 | 15.27 |3.67 | 24.06 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/pcpvt_small_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/pcpvt_small_infer.tar) | +| pcpvt_base | 0.8242 | 0.9619 | 12.20 | 16.22 | 23.16 | 6.44 | 43.83 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/pcpvt_base_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/pcpvt_base_infer.tar) | +| pcpvt_large | 0.8273 | 0.9650 | 16.47 | 22.90 | 32.73 | 9.50 | 60.99 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/pcpvt_large_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/pcpvt_large_infer.tar) | +| alt_gvt_small | 0.8140 | 0.9546 | 6.94 | 9.01 | 12.27 |2.81 | 24.06 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/alt_gvt_small_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/alt_gvt_small_infer.tar) | +| alt_gvt_base | 0.8294 | 0.9621 | 9.37 | 15.02 | 24.54 | 8.34 | 56.07 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/alt_gvt_base_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/alt_gvt_base_infer.tar) | +| alt_gvt_large | 0.8331 | 0.9642 | 11.76 | 22.08 | 35.12 | 14.81 | 99.27 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/alt_gvt_large_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/alt_gvt_large_infer.tar) | -**Note**：The difference in accuracy from Reference is due to the difference in data preprocessing and the absence of distilled head as output. +**Note**: The accuracy difference with Reference is due to the difference in data preprocessing. - -### Twins + -Accuracy and inference time metrics of Twins series models are shown as follows. More detailed information can be refered to[Twins series tutorial](../en/models/Twins_en.md). +## 19. HarDNet series -| Model | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | Flops(G) | Params(M) | Download Address | -| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | -| pcpvt_small | 0.8082 | 0.9552 | | |3.7 | 24.1 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/pcpvt_small_pretrained.pdparams) | -| pcpvt_base | 0.8242 | 0.9619 | | | 6.4 | 43.8 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/pcpvt_base_pretrained.pdparams) | -| pcpvt_large | 0.8273 | 0.9650 | | | 9.5 | 60.9 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/pcpvt_large_pretrained.pdparams) | -| alt_gvt_small | 0.8140 | 0.9546 | | |2.8 | 24 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/alt_gvt_small_pretrained.pdparams) | -| alt_gvt_base | 0.8294 | 0.9621 | | | 8.3 | 56 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/alt_gvt_base_pretrained.pdparams) | -| alt_gvt_large | 0.8331 | 0.9642 | | | 14.8 | 99.2 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/alt_gvt_large_pretrained.pdparams) | +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.md). -**Note**：The difference in accuracy from Reference is due to the difference in data preprocessing. +| 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 | +| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | +| HarDNet39_ds | 0.7133 |0.8998 | 1.40 | 2.30 | 3.33 | 0.44 | 3.51 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet39_ds_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HarDNet39_ds_infer.tar) | +| HarDNet68_ds |0.7362 | 0.9152 | 2.26 | 3.34 | 5.06 | 0.79 | 4.20 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet68_ds_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HarDNet68_ds_infer.tar) | +| HarDNet68| 0.7546 | 0.9265 | 3.58 | 8.53 | 11.58 | 4.26 | 17.58 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet68_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HarDNet68_infer.tar) | +| HarDNet85 | 0.7744 | 0.9355 | 6.24 | 14.85 | 20.57 | 9.09 | 36.69 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet85_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HarDNet85_infer.tar) | - -### HarDNet + -Accuracy and inference time metrics of HarDNet series models are shown as follows. More detailed information can be refered to[HarDNet series tutorial](../en/models/HarDNet_en.md). +## 20. DLA series +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.md). -| Model | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | Flops(G) | Params(M) | Download Address | -| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | -| HarDNet39_ds | 0.7133 |0.8998 | | | 0.4 | 3.5 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet39_ds_pretrained.pdparams) | -| HarDNet68_ds |0.7362 | 0.9152 | | | 0.8 | 4.2 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet68_ds_pretrained.pdparams) | -| HarDNet68| 0.7546 | 0.9265 | | | 4.3 | 17.6 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet68_pretrained.pdparams) | -| HarDNet85 | 0.7744 | 0.9355 | | | 9.1 | 36.7 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet85_pretrained.pdparams) | +| 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 | +| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | +| DLA102 | 0.7893 |0.9452 | 4.95 | 8.08 | 12.40 | 7.19 | 33.34 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA102_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DLA102_infer.tar) | +| DLA102x2 |0.7885 | 0.9445 | 19.58 | 23.97 | 31.37 | 9.34 | 41.42 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA102x2_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DLA102x2_infer.tar) | +| DLA102x| 0.781 | 0.9400 | 11.12 | 15.60 | 20.37 | 5.89 | 26.40 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA102x_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DLA102x_infer.tar) | +| DLA169 | 0.7809 | 0.9409 | 7.70 | 12.25 | 18.90 | 11.59 | 53.50 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA169_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DLA169_infer.tar) | +| DLA34 | 0.7603 | 0.9298 | 1.83 | 3.37 | 5.98 | 3.07 | 15.76 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA34_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DLA34_infer.tar) | +| DLA46_c |0.6321 | 0.853 | 1.06 | 2.08 | 3.23 | 0.54 | 1.31 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA46_c_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DLA46_c_infer.tar) | +| DLA60 | 0.7610 | 0.9292 | 2.78 | 5.36 | 8.29 | 4.26 | 22.08 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA60_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DLA60_infer.tar) | +| DLA60x_c | 0.6645 | 0.8754 | 1.79 | 3.68 | 5.19 | 0.59 | 1.33 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA60x_c_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DLA60x_c_infer.tar) | +| DLA60x | 0.7753 | 0.9378 | 5.98 | 9.24 | 12.52 | 3.54 | 17.41 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA60x_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DLA60x_infer.tar) | - -### DLA + -Accuracy and inference time metrics of DLA series models are shown as follows. More detailed information can be refered to[DLA series tutorial](../en/models/DLA_en.md). +## 21. RedNet series -| Model | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | Flops(G) | Params(M) | Download Address | -| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | -| DLA102 | 0.7893 |0.9452 | | | 7.2 | 33.3 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA102_pretrained.pdparams) | -| DLA102x2 |0.7885 | 0.9445 | | | 9.3 | 41.4 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA102x2_pretrained.pdparams) | -| DLA102x| 0.781 | 0.9400 | | | 5.9 | 26.4 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA102x_pretrained.pdparams) | -| DLA169 | 0.7809 | 0.9409 | | | 11.6 | 53.5 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA169_pretrained.pdparams) | -| DLA34 | 0.7603 | 0.9298 | | | 3.1 | 15.8 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA34_pretrained.pdparams) | -| DLA46_c |0.6321 | 0.853 | | | 0.5 | 1.3 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA46_c_pretrained.pdparams) | -| DLA60 | 0.7610 | 0.9292 | | | 4.2 | 22.0 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA60_pretrained.pdparams) | -| DLA60x_c | 0.6645 | 0.8754 | | | 0.6 | 1.3 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA60x_c_pretrained.pdparams) | -| DLA60x | 0.7753 | 0.9378 | | | 3.5 | 17.4 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA60x_pretrained.pdparams) | +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.md). - -### RedNet +| 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 | +| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | +| RedNet26 | 0.7595 |0.9319 | 4.45 | 15.16 | 29.03 | 1.69 | 9.26 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet26_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RedNet26_infer.tar) | +| RedNet38 |0.7747 | 0.9356 | 6.24 | 21.39 | 41.26 | 2.14 | 12.43 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet38_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RedNet38_infer.tar) | +| RedNet50| 0.7833 | 0.9417 | 8.04 | 27.71 | 53.73 | 2.61 | 15.60 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet50_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RedNet50_infer.tar) | +| RedNet101 | 0.7894 | 0.9436 | 13.07 | 44.12 | 83.28 | 4.59 | 25.76 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet101_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RedNet101_infer.tar) | +| RedNet152 | 0.7917 | 0.9440 | 18.66 | 63.27 | 119.48 | 6.57 | 34.14 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet152_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RedNet152_infer.tar) | -Accuracy and inference time metrics of RedNet series models are shown as follows. More detailed information can be refered to[RedNet series tutorial](../en/models/RedNet_en.md). + -| Model | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | Flops(G) | Params(M) | Download Address | -| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | -| RedNet26 | 0.7595 |0.9319 | | | 1.7 | 9.2 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet26_pretrained.pdparams) | -| RedNet38 |0.7747 | 0.9356 | | | 2.2 | 12.4 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet38_pretrained.pdparams) | -| RedNet50| 0.7833 | 0.9417 | | | 2.7 | 15.5 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet50_pretrained.pdparams) | -| RedNet101 | 0.7894 | 0.9436 | | | 4.7 | 25.7 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet101_pretrained.pdparams) | -| RedNet152 | 0.7917 | 0.9440 | | | 6.8 | 34.0 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet152_pretrained.pdparams) | +## 22. TNT series - -### TNT +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.md). -Accuracy and inference time metrics of TNT series models are shown as follows. More detailed information can be refered to[TNT series tutorial](../en/models/TNT_en.md). +| Model | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | FLOPs(G) | Params(M) | Pretrained Model Download Address | Inference Model Download Address | +| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | +| TNT_small | 0.8121 |0.9563 | | | 4.83 | 23.68 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/TNT_small_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/TNT_small_infer.tar) | -| Model | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | Flops(G) | Params(M) | Download Address | -| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | -| TNT_small | 0.8121 |0.9563 | | | 5.2 | 23.8 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/TNT_small_pretrained.pdparams) | | +**Note**: Both `mean` and `std` in the data preprocessing part of the TNT model `NormalizeImage` are 0.5. -**Note**：The `mean` and `std` in `NormalizeImage` in the data preprocessing part of the TNT model are both 0.5. + -### Others - -Accuracy and inference time metrics of AlexNet, SqueezeNet series, VGG series and DarkNet53 models are shown as follows. More detailed information can be refered to [Others](../en/models/Others_en.md). +## 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.md). -| Model | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | Flops(G) | Params(M) | Download Address | -|------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------| -| AlexNet | 0.567 | 0.792 | 1.44993 | 2.46696 | 1.370 | 61.090 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/AlexNet_pretrained.pdparams) | -| SqueezeNet1_0 | 0.596 | 0.817 | 0.96736 | 2.53221 | 1.550 | 1.240 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_0_pretrained.pdparams) | -| SqueezeNet1_1 | 0.601 | 0.819 | 0.76032 | 1.877 | 0.690 | 1.230 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_1_pretrained.pdparams) | -| VGG11 | 0.693 | 0.891 | 3.90412 | 9.51147 | 15.090 | 132.850 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VGG11_pretrained.pdparams) | -| VGG13 | 0.700 | 0.894 | 4.64684 | 12.61558 | 22.480 | 133.030 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VGG13_pretrained.pdparams) | -| VGG16 | 0.720 | 0.907 | 5.61769 | 16.40064 | 30.810 | 138.340 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VGG16_pretrained.pdparams) | -| VGG19 | 0.726 | 0.909 | 6.65221 | 20.4334 | 39.130 | 143.650 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VGG19_pretrained.pdparams) | -| DarkNet53 | 0.780 | 0.941 | 4.10829 | 12.1714 | 18.580 | 41.600 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DarkNet53_pretrained.pdparams) | +| 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 | +|------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------| +| AlexNet | 0.567 | 0.792 | 0.81 | 1.50 | 2.33 | 0.71 | 61.10 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/AlexNet_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/AlexNet_infer.tar) | +| SqueezeNet1_0 | 0.596 | 0.817 | 0.68 | 1.64 | 2.62 | 0.78 | 1.25 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_0_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SqueezeNet1_0_infer.tar) | +| SqueezeNet1_1 | 0.601 | 0.819 | 0.62 | 1.30 | 2.09 | 0.35 | 1.24 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_1_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SqueezeNet1_1_infer.tar) | +| VGG11 | 0.693 | 0.891 | 1.72 | 4.15 | 7.24 | 7.61 | 132.86 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG11_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/VGG11_infer.tar) | +| VGG13 | 0.700 | 0.894 | 2.02 | 5.28 | 9.54 | 11.31 | 133.05 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG13_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/VGG13_infer.tar) | +| 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) | diff --git a/docs/en/algorithm_introduction/image_classification_en.md b/docs/en/algorithm_introduction/image_classification_en.md index acb01eca1b94fab79ddbf3ade3417cd8259c7a23..575be52c70249d3f78207315e3e906dfd3adc1ba 100644 --- a/docs/en/algorithm_introduction/image_classification_en.md +++ b/docs/en/algorithm_introduction/image_classification_en.md @@ -1,4 +1,4 @@ -## Contents +## Catalogue - [1. Dataset Introduction](#1) - [1.1 ImageNet-1k](#1.1) diff --git a/docs/en/quick_start/quick_start_multilabel_classification_en.md b/docs/en/quick_start/quick_start_multilabel_classification_en.md index a398967a2076cd223c4682ff0409e74e4e34c7f7..39adbb7655b29f4bfe5f9545199d15fa6ac2876b 100644 --- a/docs/en/quick_start/quick_start_multilabel_classification_en.md +++ b/docs/en/quick_start/quick_start_multilabel_classification_en.md @@ -2,7 +2,7 @@ Experience the training, evaluation, and prediction of multi-label classification based on the [NUS-WIDE-SCENE](https://lms.comp.nus.edu.sg/wp-content/uploads/2019/research/nuswide/NUS-WIDE.html) dataset, which is a subset of the NUS-WIDE dataset. Please first install PaddlePaddle and PaddleClas, see [Paddle Installation](https://github.com/PaddlePaddle/PaddleClas/blob/develop/docs/zh_CN/installation) and [PaddleClas installation](https://github.com/PaddlePaddle/PaddleClas/blob/develop/docs/zh_CN/installation/install_ paddleclas.md) for more details. -## Contents +## Catalogue - [1. Data and Model Preparation](#1) - [2. Model Training](#2) @@ -114,4 +114,4 @@ Obtain an output silimar to the following: ``` 0517_2715693311.jpg: class id(s): [6, 13, 17, 23, 26, 30], score(s): [0.96, 0.56, 0.55, 0.99, 0.59, 0.79], label_name(s): [] -``` \ No newline at end of file +``` diff --git a/docs/make.bat b/docs/make.bat new file mode 100644 index 0000000000000000000000000000000000000000..6fcf05b4b76f8b9774c317ac8ada402f8a7087de --- /dev/null +++ b/docs/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=source +set BUILDDIR=build + +if "%1" == "" goto help + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.https://www.sphinx-doc.org/ + exit /b 1 +) + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/docs/source/index.rst b/docs/source/index.rst new file mode 100644 index 0000000000000000000000000000000000000000..6ff5f7e1590fb1ea1f8bcdcc0a39f3fbe31e72df --- /dev/null +++ b/docs/source/index.rst @@ -0,0 +1,20 @@ +.. paddleclas documentation master file, created by + sphinx-quickstart on Wed Dec 15 16:13:23 2021. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to paddleclas's documentation! +====================================== + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/docs/zh_CN/algorithm_introduction/DataAugmentation.md b/docs/zh_CN/algorithm_introduction/DataAugmentation.md index dbc3469808689971ad70172c8a82e8d0fd37517f..3b14de810f561fa18fc2b0bf5f3144bb04f8d18f 100644 --- a/docs/zh_CN/algorithm_introduction/DataAugmentation.md +++ b/docs/zh_CN/algorithm_introduction/DataAugmentation.md @@ -1,6 +1,5 @@ # 数据增强 ------ - ## 目录 - [1. 数据增强简介](#1) diff --git a/docs/zh_CN/algorithm_introduction/image_classification.md b/docs/zh_CN/algorithm_introduction/image_classification.md index e5120bcf28b8893220d42197995c0b7fe828cc6a..afe9be54995e78fe0a8eac374d714cfd37db9969 100644 --- a/docs/zh_CN/algorithm_introduction/image_classification.md +++ b/docs/zh_CN/algorithm_introduction/image_classification.md @@ -1,7 +1,6 @@ # 图像分类任务介绍 ------- ## 目录 diff --git a/docs/zh_CN/conf.py b/docs/zh_CN/conf.py index 889ef592d9a4f8dda990f82fd4e3c5a116e461f9..482270dbd6a24436a132a569e279eb70c76de7c6 100644 --- a/docs/zh_CN/conf.py +++ b/docs/zh_CN/conf.py @@ -15,7 +15,7 @@ # sys.path.insert(0, os.path.abspath('.')) import sphinx_rtd_theme from recommonmark.parser import CommonMarkParser -#import sphinx-markdown-tables +import sphinx-markdown-tables # -- Project information ----------------------------------------------------- project = 'PaddleClas' diff --git a/docs/zh_CN/index.rst b/docs/zh_CN/index.rst index 33f9581edeb6eb3d072963aacad0e5769ccba54d..22559c4b33d74bf2dac7da968986d3f0d3876f61 100644 --- a/docs/zh_CN/index.rst +++ b/docs/zh_CN/index.rst @@ -16,8 +16,4 @@ advanced_tutorials/index others/index faq_series/index - - - - - + diff --git a/docs/zh_CN/make.bat b/docs/zh_CN/make.bat index 6fcf05b4b76f8b9774c317ac8ada402f8a7087de..1de82f81e6d1830691433379075f9ddef3b3d302 100644 --- a/docs/zh_CN/make.bat +++ b/docs/zh_CN/make.bat @@ -7,6 +7,7 @@ REM Command file for Sphinx documentation if "%SPHINXBUILD%" == "" ( set SPHINXBUILD=sphinx-build ) + set SOURCEDIR=source set BUILDDIR=build diff --git a/docs/zh_CN/others/course_link.md b/docs/zh_CN/others/course_link.md new file mode 100644 index 0000000000000000000000000000000000000000..8f4135b45537255bb14cdd500cdf6de78862334c --- /dev/null +++ b/docs/zh_CN/others/course_link.md @@ -0,0 +1,34 @@ +# 往期课程链接： +--- + +- [**【AI快车道PaddleClas系列直播课】**](https://aistudio.baidu.com/aistudio/course/introduce/24519) + + - [图像识别系统解析](https://aistudio.baidu.com/aistudio/education/group/info/24519) + - 图像识别全能优势 + - 整体架构及快速落地详解 + - 个性化需求实现方案 + - [商品识别系统全拆解](https://aistudio.baidu.com/aistudio/education/lessonvideo/1495317) + - 小样本多类别场景方案 + - 图像检索技术及快速构建策略 + - 动漫搜索趣味应用 + - [车辆ReID核心技术方案](https://aistudio.baidu.com/aistudio/education/lessonvideo/1496537) + - ReID及跨境头场景应用 + - Metric Learning——更鲁棒的检索特征 + - Logo识别等方向延展 + - [超轻量图像识别系统概览](https://aistudio.baidu.com/aistudio/education/lessonvideo/1890318) + - 图像识别技术选型策略 + - 推理速度提升8倍的秘籍 + - 四大典型行业应用案例 + - [SOTA模型炼丹秘诀](https://aistudio.baidu.com/aistudio/education/lessonvideo/1890323) + - CPU定制模型PP-LCNet优化思路 + - Vison Transformer模型的应用拓展 + - [商品识别产业痛点剖析](https://aistudio.baidu.com/aistudio/education/lessonvideo/1896890) + - 特征提取技术详解 + - 向量快速检索揭秘 + - [手把手教你玩转图像识别](https://aistudio.baidu.com/aistudio/education/lessonvideo/1911507) + - 产业应用十问十答 + - 智能零售下的应用案例 + - 识别系统快速落地方案 + + + diff --git a/docs/zh_CN/others/train_with_DALI.md b/docs/zh_CN/others/train_with_DALI.md index db93de14149d9d46b0e32abd927b071112d4e8ec..34782ea3cce44880b4f379e08ea28ce4336edad2 100644 --- a/docs/zh_CN/others/train_with_DALI.md +++ b/docs/zh_CN/others/train_with_DALI.md @@ -31,29 +31,29 @@ ## 3. 使用 DALI -PaddleClas 支持在静态图训练方式中使用 DALI 加速，由于 DALI 仅支持 GPU 训练，因此需要设置 GPU，且 DALI 需要占用 GPU 显存，需要为 DALI 预留显存。使用 DALI 训练只需在训练配置文件中设置字段 `use_dali=True`，或通过以下命令启动训练即可： +PaddleClas 支持使用 DALI 对图像预处理进行加速，由于 DALI 仅支持 GPU 训练，因此需要设置 GPU，且 DALI 需要占用 GPU 显存，需要为 DALI 预留显存。使用 DALI 训练只需在训练配置文件中设置字段 `use_dali=True`，或通过以下命令启动训练即可： ```shell # 设置用于训练的 GPU 卡号 export CUDA_VISIBLE_DEVICES="0" -python ppcls/static/train.py -c ppcls/configs/ImageNet/ResNet/ResNet50.yaml -o use_dali=True +python ppcls/train.py -c ppcls/configs/ImageNet/ResNet/ResNet50.yaml -o Global.use_dali=True ``` 也可以使用多卡训练： ```shell # 设置用于训练的 GPU 卡号 -export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" +export CUDA_VISIBLE_DEVICES="0,1,2,3" # 设置用于神经网络训练的显存大小，可根据具体情况设置，一般可设置为 0.8 或 0.7，剩余显存则预留 DALI 使用 export FLAGS_fraction_of_gpu_memory_to_use=0.80 python -m paddle.distributed.launch \ - --gpus="0,1,2,3,4,5,6,7" \ - ppcls/static/train.py \ + --gpus="0,1,2,3" \ + ppcls/train.py \ -c ./ppcls/configs/ImageNet/ResNet/ResNet50.yaml \ - -o use_dali=True + -o Global.use_dali=True ``` @@ -62,11 +62,11 @@ python -m paddle.distributed.launch \ 在上述基础上，使用 FP16 半精度训练，可以进一步提高速度，可以参考下面的配置与运行命令。 ```shell -export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +export CUDA_VISIBLE_DEVICES=0,1,2,3 export FLAGS_fraction_of_gpu_memory_to_use=0.8 python -m paddle.distributed.launch \ - --gpus="0,1,2,3,4,5,6,7" \ - ppcls/static/train.py \ - -c ./ppcls/configs/ImageNet/ResNet/ResNet50_fp16.yaml + --gpus="0,1,2,3" \ + ppcls/train.py \ + -c ./ppcls/configs/ImageNet/ResNet/ResNet50_fp16_dygraph.yaml ``` diff --git a/ppcls/configs/slim/GeneralRecognition_PPLCNet_x2_5_quantization.yaml b/ppcls/configs/slim/GeneralRecognition_PPLCNet_x2_5_quantization.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3d2b0eb5e1dc32455cc5136eeea6ddae95b3207e --- /dev/null +++ b/ppcls/configs/slim/GeneralRecognition_PPLCNet_x2_5_quantization.yaml @@ -0,0 +1,154 @@ +# global configs +Global: + checkpoints: null + pretrained_model: https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/pretrain/general_PPLCNet_x2_5_pretrained_v1.0.pdparams + output_dir: ./output/ + device: gpu + save_interval: 1 + eval_during_train: True + eval_interval: 1 + epochs: 30 + print_batch_step: 10 + use_visualdl: False + # used for static mode and model export + image_shape: [3, 224, 224] + save_inference_dir: ./inference + eval_mode: retrieval + use_dali: False + to_static: False + +# for quantizaiton or prune model +Slim: + ## for prune + quant: + name: pact + +# model architecture +Arch: + name: RecModel + infer_output_key: features + infer_add_softmax: False + + Backbone: + name: PPLCNet_x2_5 + pretrained: False + use_ssld: True + BackboneStopLayer: + name: flatten_0 + Neck: + name: FC + embedding_size: 1280 + class_num: 512 + Head: + name: ArcMargin + embedding_size: 512 + class_num: 185341 + margin: 0.2 + scale: 30 + +# loss function config for traing/eval process +Loss: + Train: + - CELoss: + weight: 1.0 + Eval: + - CELoss: + weight: 1.0 + +Optimizer: + name: Momentum + momentum: 0.9 + lr: + name: Cosine + learning_rate: 0.002 + warmup_epoch: 5 + regularizer: + name: 'L2' + coeff: 0.00001 + + +# data loader for train and eval +DataLoader: + Train: + dataset: + name: ImageNetDataset + image_root: ./dataset/ + cls_label_path: ./dataset/train_reg_all_data.txt + transform_ops: + - DecodeImage: + to_rgb: True + channel_first: False + - RandCropImage: + size: 224 + - RandFlipImage: + flip_code: 1 + - NormalizeImage: + scale: 1.0/255.0 + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: '' + + sampler: + name: DistributedBatchSampler + batch_size: 128 + drop_last: False + shuffle: True + loader: + num_workers: 4 + use_shared_memory: True + + Eval: + Query: + dataset: + name: VeriWild + image_root: ./dataset/Aliproduct/ + cls_label_path: ./dataset/Aliproduct/val_list.txt + transform_ops: + - DecodeImage: + to_rgb: True + channel_first: False + - ResizeImage: + size: 224 + - NormalizeImage: + scale: 0.00392157 + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: '' + sampler: + name: DistributedBatchSampler + batch_size: 64 + drop_last: False + shuffle: False + loader: + num_workers: 4 + use_shared_memory: True + + Gallery: + dataset: + name: VeriWild + image_root: ./dataset/Aliproduct/ + cls_label_path: ./dataset/Aliproduct/val_list.txt + transform_ops: + - DecodeImage: + to_rgb: True + channel_first: False + - ResizeImage: + size: 224 + - NormalizeImage: + scale: 0.00392157 + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: '' + sampler: + name: DistributedBatchSampler + batch_size: 64 + drop_last: False + shuffle: False + loader: + num_workers: 4 + use_shared_memory: True + +Metric: + Eval: + - Recallk: + topk: [1, 5] diff --git a/ppcls/configs/slim/PPLCNet_x1_0_quantization.yaml b/ppcls/configs/slim/PPLCNet_x1_0_quantization.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b01122cf7f608331fc4295923511cbb814c3c4fb --- /dev/null +++ b/ppcls/configs/slim/PPLCNet_x1_0_quantization.yaml @@ -0,0 +1,138 @@ +# global configs +Global: + checkpoints: null + pretrained_model: null + output_dir: ./output/ + device: gpu + save_interval: 1 + eval_during_train: True + eval_interval: 1 + epochs: 60 + print_batch_step: 10 + use_visualdl: False + # used for static mode and model export + image_shape: [3, 224, 224] + save_inference_dir: ./inference + +# for quantalization or prune model +Slim: + ## for quantization + quant: + name: pact + +# model architecture +Arch: + name: PPLCNet_x1_0 + class_num: 1000 + pretrained: True + +# loss function config for traing/eval process +Loss: + Train: + - CELoss: + weight: 1.0 + epsilon: 0.1 + Eval: + - CELoss: + weight: 1.0 + + +Optimizer: + name: Momentum + momentum: 0.9 + lr: + name: Cosine + learning_rate: 0.02 + warmup_epoch: 0 + regularizer: + name: 'L2' + coeff: 0.00003 + + +# data loader for train and eval +DataLoader: + Train: + dataset: + name: ImageNetDataset + image_root: ./dataset/ILSVRC2012/ + cls_label_path: ./dataset/ILSVRC2012/train_list.txt + transform_ops: + - DecodeImage: + to_rgb: True + channel_first: False + - RandCropImage: + size: 224 + - RandFlipImage: + flip_code: 1 + - AutoAugment: + - NormalizeImage: + scale: 1.0/255.0 + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: '' + + sampler: + name: DistributedBatchSampler + batch_size: 128 + drop_last: False + shuffle: True + loader: + num_workers: 4 + use_shared_memory: True + + Eval: + dataset: + name: ImageNetDataset + image_root: ./dataset/ILSVRC2012/ + cls_label_path: ./dataset/ILSVRC2012/val_list.txt + transform_ops: + - DecodeImage: + to_rgb: True + channel_first: False + - ResizeImage: + resize_short: 256 + - CropImage: + size: 224 + - NormalizeImage: + scale: 1.0/255.0 + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: '' + sampler: + name: DistributedBatchSampler + batch_size: 64 + drop_last: False + shuffle: False + loader: + num_workers: 4 + use_shared_memory: True + +Infer: + infer_imgs: docs/images/whl/demo.jpg + batch_size: 10 + transforms: + - DecodeImage: + to_rgb: True + channel_first: False + - ResizeImage: + resize_short: 256 + - CropImage: + size: 224 + - NormalizeImage: + scale: 1.0/255.0 + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: '' + - ToCHWImage: + PostProcess: + name: Topk + topk: 5 + class_id_map_file: ppcls/utils/imagenet1k_label_list.txt + +Metric: + Train: + - TopkAcc: + topk: [1, 5] + Eval: + - TopkAcc: + topk: [1, 5] diff --git a/ppcls/engine/engine.py b/ppcls/engine/engine.py index fe069b1dee8dea8a54121229e26c3f188faa3c0c..fca3a82bbf05f6e2285e1ffe2b526c688d0e1af6 100644 --- a/ppcls/engine/engine.py +++ b/ppcls/engine/engine.py @@ -250,6 +250,8 @@ class Engine(object): self.scaler = paddle.amp.GradScaler( init_loss_scaling=self.scale_loss, use_dynamic_loss_scaling=self.use_dynamic_loss_scaling) + if self.config['AMP']['use_pure_fp16'] is True: + self.model = paddle.amp.decorate(models=self.model, level='O2') self.max_iter = len(self.train_dataloader) - 1 if platform.system( ) == "Windows" else len(self.train_dataloader) diff --git a/ppcls/engine/train/train.py b/ppcls/engine/train/train.py index cbf868e4e6d1d118b417568625c493afea6cd23a..b7fa9d3a060bfe6134bb7f42d8bb9926d03b73bc 100644 --- a/ppcls/engine/train/train.py +++ b/ppcls/engine/train/train.py @@ -21,6 +21,7 @@ from ppcls.utils import profiler def train_epoch(engine, epoch_id, print_batch_step): tic = time.time() + v_current = [int(i) for i in paddle.__version__.split(".")] for iter_id, batch in enumerate(engine.train_dataloader): if iter_id >= engine.max_iter: break @@ -41,14 +42,15 @@ def train_epoch(engine, epoch_id, print_batch_step): # image input if engine.amp: - with paddle.amp.auto_cast(custom_black_list={ - "flatten_contiguous_range", "greater_than" - }): + amp_level = 'O1' + if engine.config['AMP']['use_pure_fp16'] is True: + amp_level = 'O2' + with paddle.amp.auto_cast(custom_black_list={"flatten_contiguous_range", "greater_than"}, level=amp_level): out = forward(engine, batch) + loss_dict = engine.train_loss_func(out, batch[1]) else: out = forward(engine, batch) - - loss_dict = engine.train_loss_func(out, batch[1]) + loss_dict = engine.train_loss_func(out, batch[1]) # step opt and lr if engine.amp: diff --git a/ppcls/optimizer/optimizer.py b/ppcls/optimizer/optimizer.py index f429755fcc4fc189871526bae11639b83e870d05..4422ea70d32a3ed1ce89c33ec806e2035aa25420 100644 --- a/ppcls/optimizer/optimizer.py +++ b/ppcls/optimizer/optimizer.py @@ -17,6 +17,7 @@ from __future__ import division from __future__ import print_function from paddle import optimizer as optim +import paddle from ppcls.utils import logger @@ -36,7 +37,7 @@ class Momentum(object): momentum, weight_decay=None, grad_clip=None, - multi_precision=False): + multi_precision=True): super().__init__() self.learning_rate = learning_rate self.momentum = momentum @@ -55,6 +56,15 @@ class Momentum(object): grad_clip=self.grad_clip, multi_precision=self.multi_precision, parameters=parameters) + if hasattr(opt, '_use_multi_tensor'): + opt = optim.Momentum( + learning_rate=self.learning_rate, + momentum=self.momentum, + weight_decay=self.weight_decay, + grad_clip=self.grad_clip, + multi_precision=self.multi_precision, + parameters=parameters, + use_multi_tensor=True) return opt diff --git "a/readthedoc\346\214\207\345\215\227.md" "b/readthedoc\346\214\207\345\215\227.md" new file mode 100644 index 0000000000000000000000000000000000000000..a25626b26172d594dfb393c9e67e39a3d51d1e5e --- /dev/null +++ "b/readthedoc\346\214\207\345\215\227.md" @@ -0,0 +1,166 @@ +1. 注册ReadtheDocs并连接到github + +2. 在github上将项目克隆到本地 + +3. 在本地仓库中安装Sphinx + + ```shell + pip install sphinx + ``` + +4. 创建工程 + + ```shell + sphinx-quickstart + ``` + +5. 对工程进行配置 + + 5.1 更改主题 + + 在source/conf.py中更改或添加如下代码 + + ```python + import sphinx_rtd_theme + html_theme = "sphinx_rtd_theme" + html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] + ``` + + 5.2 添加markdown支持和markdown表格支持 + + 首先需要安装recommonmark和sphinx_markdown_tables + + ```shell + pip install recommonmark + pip install sphinx_markdown_tables + ``` + + 在source/conf.py中更改或添加如下代码 + + ```python + from recommonmark.parser import CommonMarkParser + source_parsers = { + '.md': CommonMarkParser, + } + source_suffix = ['.rst', '.md'] + extensions = [ + 'recommonmark', + 'sphinx_markdown_tables' + ] + ``` + + 以上五步具体效果可以参考https://www.jianshu.com/p/d1d59d0cd58c + +6. 在创建好项目以后，根目录下应该有如下几个文件： + + - **Makefile**：在使用 `make` 命令时，可以使用这些指令（e.g. `sphinx-build`）来构建文档输出。 + - **_build**：这是触发特定输出后用来存放所生成的文件的目录。 + - **_static**：所有不属于源代码（e.g. 图片）一部分的文件均存放于此处，稍后会在构建目录中将它们链接在一起。 + - **conf.py**：用于存放 Sphinx 的配置值，包括在终端执行 `sphinx-quickstart`时选中的那些值。 + - **index.rst**：文档项目的 root 目录。如果将文档划分为其他文件，该目录会连接这些文件 + +7. **编写文档**：在 index.rst 文件中的主标题之后，有一个内容清单，其中包括 `toctree` 声明，它将所有文档链接都汇集到 Index。 + + 以根目录下的index.rst为例： + + ```rst + 欢迎使用PaddleClas图像分类库！ + ================================ + + .. toctree:: + :maxdepth: 1 + + models_training/index + introduction/index + image_recognition_pipeline/index + others/index + faq_series/index + data_preparation/index + installation/index + models/index + advanced_tutorials/index + algorithm_introduction/index + inference_deployment/index + quick_start/index + ``` + + 可以用下面的python代码实现根目录和各个子目录下的`index.rst`文件的编写 + + 注意：此代码应该在需要生成文档书的文件夹根目录上运行 + + ```python + import os + + def file_name(file_dir): + temp = [] + for root, dirs, files in os.walk(file_dir): + print(dirs) #当前路径下所有子目录 + temp = dirs #存储需要的子目录 + break + + # 删除不需要的子目录 + temp.remove('images') + temp.remove('_templates') + temp.remove('_build') + temp.remove('_static') + chinese_name = ['模型训练', '介绍', '图像识别流程', '其他', 'FAQ系列', '数据准备', '安装', '模型库', '高级教程', '算法介绍', '推理部署', '快速开始'] + # 写根目录下的rst文件 + with open('./index.rst', 'w') as f: + f.write('欢迎使用PaddleClas图像分类库！\n') + f.write('================================\n\n') + f.write('.. toctree::\n') + f.write(' :maxdepth: 1\n\n') + for dir in temp: + f.write(' ' + dir + '/index\n') + f.close() + + # 写各个子目录下的rst文件 + for dir in temp: + for root, dirs, files in os.walk(dir): + print(root) #当前目录路径 + + files.remove('index.rst') + print(files) #当前路径下所有非目录子文件 + curDir = os.path.join(file_dir, dir) + filename = curDir + '/index.rst' + idx = temp.index(dir) + ch_name = chinese_name[idx] + with open(filename, 'w') as f: + f.write(ch_name+'\n') + f.write('================================\n\n') + f.write('.. toctree::\n') + f.write(' :maxdepth: 2\n\n') + + for f1 in files: + f.write(' ' + f1 + '\n') + + f.close() + + + def readfile(filename): + file = open(filename) + i = 0 + while 1: + line = file.readline() + print(i) + print(line) + i += 1 + if not line: + break + pass # do something + file.close() + + + file_name('./') + # filename = './index.rst' + # readfile(filename) + ``` + +8. 生成文档 + + 运行 `make html` 命令 + +9. 使用浏览器查看在build/html目录下的 `index.html`文件可以查看静态网页 + + + diff --git a/test_tipc/config/LeViT/LeViT_384_train_infer_python.txt b/test_tipc/config/LeViT/LeViT_384_train_infer_python.txt index 47451d4f75a9dc1d19e7d7a89957d837a0e1c464..0ae0d40fbfcd9a9a1c8ea187c534943613228d75 100644 --- a/test_tipc/config/LeViT/LeViT_384_train_infer_python.txt +++ b/test_tipc/config/LeViT/LeViT_384_train_infer_python.txt @@ -6,7 +6,7 @@ gpu_list:0|0,1 -o Global.auto_cast:null -o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120 -o Global.output_dir:./output/ --o DataLoader.Train.sampler.batch_size:8 +-o DataLoader.Train.sampler.batch_size:2 -o Global.pretrained_model:null train_model_name:latest train_infer_img_dir:./dataset/ILSVRC2012/val @@ -37,7 +37,7 @@ pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/L infer_model:../inference/ infer_export:True infer_quant:Fasle -inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=384 -o PreProcess.transform_ops.1.CropImage.size=384 +inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=256 -o PreProcess.transform_ops.1.CropImage.size=224 -o Global.use_gpu:True|False -o Global.enable_mkldnn:True|False -o Global.cpu_num_threads:1|6 diff --git a/test_tipc/config/RedNet/RedNet101_train_infer_python.txt b/test_tipc/config/RedNet/RedNet101_train_infer_python.txt index b16633f41a1e687edbdbeb0a40c9d9f290a8d563..6d7d3b9d82d968ffbd2134fdbff2d8ac49221d4a 100644 --- a/test_tipc/config/RedNet/RedNet101_train_infer_python.txt +++ b/test_tipc/config/RedNet/RedNet101_train_infer_python.txt @@ -6,7 +6,7 @@ gpu_list:0|0,1 -o Global.auto_cast:null -o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120 -o Global.output_dir:./output/ --o DataLoader.Train.sampler.batch_size:8 +-o DataLoader.Train.sampler.batch_size:2 -o Global.pretrained_model:null train_model_name:latest train_infer_img_dir:./dataset/ILSVRC2012/val diff --git a/test_tipc/config/SwinTransformer/SwinTransformer_large_patch4_window12_384_train_infer_python.txt b/test_tipc/config/SwinTransformer/SwinTransformer_large_patch4_window12_384_train_infer_python.txt index 6da5cedf6f07074c01ed4c0f7ca53d290ee62040..f1a1873d99e5f85ae7264f36f8068f78d3d5dfbd 100644 --- a/test_tipc/config/SwinTransformer/SwinTransformer_large_patch4_window12_384_train_infer_python.txt +++ b/test_tipc/config/SwinTransformer/SwinTransformer_large_patch4_window12_384_train_infer_python.txt @@ -6,7 +6,7 @@ gpu_list:0|0,1 -o Global.auto_cast:null -o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120 -o Global.output_dir:./output/ --o DataLoader.Train.sampler.batch_size:8 +-o DataLoader.Train.sampler.batch_size:2 -o Global.pretrained_model:null train_model_name:latest train_infer_img_dir:./dataset/ILSVRC2012/val