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    README.md

    简体中文 | English

    PaddleClas

    Introduction

    PaddleClas is a toolset for image classification tasks prepared for the industry and academia. It helps users train better computer vision models and apply them in real scenarios.

    Recent update

    • 2020.09.17 Add HRNet_W48_C_ssld pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 83.62%. Add ResNet34_vd_ssld pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 79.72%.
    • 2020.09.07 Add HRNet_W18_C_ssld pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 81.16%.
    • 2020.07.14 Add Res2Net200_vd_26w_4s_ssld pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 85.13%. Add Fix_ResNet50_vd_ssld_v2 pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 84.00%.
    • 2020.06.17 Add English documents.
    • 2020.06.12 Add support for training and evaluation on Windows or CPU.
    • 2020.05.17 Add support for mixed precision training.
    • more

    Features

    • Rich model zoo. Based on the ImageNet-1k classification dataset, PaddleClas provides 24 series of classification network structures and training configurations, 122 models' pretrained weights and their evaluation metrics.

    • SSLD Knowledge Distillation. Based on this SSLD distillation strategy, the top-1 acc of the distilled model is generally increased by more than 3%.

    • Data augmentation: PaddleClas provides detailed introduction of 8 data augmentation algorithms such as AutoAugment, Cutout, Cutmix, code reproduction and effect evaluation in a unified experimental environment.

    • Pretrained model with 100,000 categories: Based on ResNet50_vd model, Baidu open sourced the ResNet50_vd pretrained model trained on a 100,000-category dataset. In some practical scenarios, the accuracy based on the pretrained weights can be increased by up to 30%.

    • A variety of training modes, including multi-machine training, mixed precision training, etc.

    • A variety of inference and deployment solutions, including TensorRT inference, Paddle-Lite inference, model service deployment, model quantification, Paddle Hub, etc.

    • Support Linux, Windows, macOS and other systems.

    Tutorials

    Model zoo overview

    Based on the ImageNet-1k classification dataset, the 24 classification network structures supported by PaddleClas and the corresponding 122 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).

    Curves of accuracy to the inference time of common server-side models are shown as follows.

    Curves of accuracy to the inference time and storage size of common mobile-side models are shown as follows.

    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.

    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
    ResNet18_vd 0.7226 0.9080 1.54557 3.85363 4.14 11.71 Download link
    ResNet34 0.7457 0.9214 2.34957 5.89821 7.36 21.8 Download link
    ResNet34_vd 0.7598 0.9298 2.43427 6.22257 7.39 21.82 Download link
    ResNet34_vd_ssld 0.7972 0.9490 2.43427 6.22257 7.39 21.82 Download link
    ResNet50 0.7650 0.9300 3.47712 7.84421 8.19 25.56 Download link
    ResNet50_vc 0.7835 0.9403 3.52346 8.10725 8.67 25.58 Download link
    ResNet50_vd 0.7912 0.9444 3.53131 8.09057 8.67 25.58 Download link
    ResNet50_vd_v2 0.7984 0.9493 3.53131 8.09057 8.67 25.58 Download link
    ResNet101 0.7756 0.9364 6.07125 13.40573 15.52 44.55 Download link
    ResNet101_vd 0.8017 0.9497 6.11704 13.76222 16.1 44.57 Download link
    ResNet152 0.7826 0.9396 8.50198 19.17073 23.05 60.19 Download link
    ResNet152_vd 0.8059 0.9530 8.54376 19.52157 23.53 60.21 Download link
    ResNet200_vd 0.8093 0.9533 10.80619 25.01731 30.53 74.74 Download link
    ResNet50_vd_
    ssld    		 0.8239 0.9610 3.53131 8.09057 8.67 25.58 Download link
    ResNet50_vd_
    ssld_v2    		 0.8300 0.9640 3.53131 8.09057 8.67 25.58 Download link
    ResNet101_vd_
    ssld    		 0.8373 0.9669 6.11704 13.76222 16.1 44.57 Download link

    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.

    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
    MobileNetV1_
    x0_5    		 0.6352 0.8473 9.579599 0.28 1.31 5.2 Download link
    MobileNetV1_
    x0_75    		 0.6881 0.8823 19.436399 0.63 2.55 10 Download link
    MobileNetV1 0.7099 0.8968 32.523048 1.11 4.19 16 Download link
    MobileNetV1_
    ssld    		 0.7789 0.9394 32.523048 1.11 4.19 16 Download link
    MobileNetV2_
    x0_25    		 0.5321 0.7652 3.79925 0.05 1.5 6.1 Download link
    MobileNetV2_
    x0_5    		 0.6503 0.8572 8.7021 0.17 1.93 7.8 Download link
    MobileNetV2_
    x0_75    		 0.6983 0.8901 15.531351 0.35 2.58 10 Download link
    MobileNetV2 0.7215 0.9065 23.317699 0.6 3.44 14 Download link
    MobileNetV2_
    x1_5    		 0.7412 0.9167 45.623848 1.32 6.76 26 Download link
    MobileNetV2_
    x2_0    		 0.7523 0.9258 74.291649 2.32 11.13 43 Download link
    MobileNetV2_
    ssld    		 0.7674 0.9339 23.317699 0.6 3.44 14 Download link
    MobileNetV3_
    large_x1_25    		 0.7641 0.9295 28.217701 0.714 7.44 29 Download link
    MobileNetV3_
    large_x1_0    		 0.7532 0.9231 19.30835 0.45 5.47 21 Download link
    MobileNetV3_
    large_x0_75    		 0.7314 0.9108 13.5646 0.296 3.91 16 Download link
    MobileNetV3_
    large_x0_5    		 0.6924 0.8852 7.49315 0.138 2.67 11 Download link
    MobileNetV3_
    large_x0_35    		 0.6432 0.8546 5.13695 0.077 2.1 8.6 Download link
    MobileNetV3_
    small_x1_25    		 0.7067 0.8951 9.2745 0.195 3.62 14 Download link
    MobileNetV3_
    small_x1_0    		 0.6824 0.8806 6.5463 0.123 2.94 12 Download link
    MobileNetV3_
    small_x0_75    		 0.6602 0.8633 5.28435 0.088 2.37 9.6 Download link
    MobileNetV3_
    small_x0_5    		 0.5921 0.8152 3.35165 0.043 1.9 7.8 Download link
    MobileNetV3_
    small_x0_35    		 0.5303 0.7637 2.6352 0.026 1.66 6.9 Download link
    MobileNetV3_
    small_x0_35_ssld    		 0.5555 0.7771 2.6352 0.026 1.66 6.9 Download link
    MobileNetV3_
    large_x1_0_ssld    		 0.7896 0.9448 19.30835 0.45 5.47 21 Download link
    MobileNetV3_large_
    x1_0_ssld_int8    		 0.7605 - 14.395 - - 10 Download link
    MobileNetV3_small_
    x1_0_ssld    		 0.7129 0.9010 6.5463 0.123 2.94 12 Download link
    ShuffleNetV2 0.6880 0.8845 10.941 0.28 2.26 9 Download link
    ShuffleNetV2_
    x0_25    		 0.4990 0.7379 2.329 0.03 0.6 2.7 Download link
    ShuffleNetV2_
    x0_33    		 0.5373 0.7705 2.64335 0.04 0.64 2.8 Download link
    ShuffleNetV2_
    x0_5    		 0.6032 0.8226 4.2613 0.08 1.36 5.6 Download link
    ShuffleNetV2_
    x1_5    		 0.7163 0.9015 19.3522 0.58 3.47 14 Download link
    ShuffleNetV2_
    x2_0    		 0.7315 0.9120 34.770149 1.12 7.32 28 Download link
    ShuffleNetV2_
    swish    		 0.7003 0.8917 16.023151 0.29 2.26 9.1 Download link
    DARTS_GS_4M 0.7523 0.9215 47.204948 1.04 4.77 21 Download link
    DARTS_GS_6M 0.7603 0.9279 53.720802 1.22 5.69 24 Download link
    GhostNet_
    x0_5    		 0.6688 0.8695 5.7143 0.082 2.6 10 Download link
    GhostNet_
    x1_0    		 0.7402 0.9165 13.5587 0.294 5.2 20 Download link
    GhostNet_
    x1_3    		 0.7579 0.9254 19.9825 0.44 7.3 29 Download link

    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.

    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
    Res2Net50_vd_
    26w_4s    		 0.7975 0.9491 4.52712 9.93247 8.37 25.06 Download link
    Res2Net50_
    14w_8s    		 0.7946 0.9470 5.4026 10.60273 9.01 25.72 Download link
    Res2Net101_vd_
    26w_4s    		 0.8064 0.9522 8.08729 17.31208 16.67 45.22 Download link
    Res2Net200_vd_
    26w_4s    		 0.8121 0.9571 14.67806 32.35032 31.49 76.21 Download link
    Res2Net200_vd_
    26w_4s_ssld    		 0.8513 0.9742 14.67806 32.35032 31.49 76.21 Download link
    ResNeXt50_
    32x4d    		 0.7775 0.9382 7.56327 10.6134 8.02 23.64 Download link
    ResNeXt50_vd_
    32x4d    		 0.7956 0.9462 7.62044 11.03385 8.5 23.66 Download link
    ResNeXt50_
    64x4d    		 0.7843 0.9413 13.80962 18.4712 15.06 42.36 Download link
    ResNeXt50_vd_
    64x4d    		 0.8012 0.9486 13.94449 18.88759 15.54 42.38 Download link
    ResNeXt101_
    32x4d    		 0.7865 0.9419 16.21503 19.96568 15.01 41.54 Download link
    ResNeXt101_vd_
    32x4d    		 0.8033 0.9512 16.28103 20.25611 15.49 41.56 Download link
    ResNeXt101_
    64x4d    		 0.7835 0.9452 30.4788 36.29801 29.05 78.12 Download link
    ResNeXt101_vd_
    64x4d    		 0.8078 0.9520 30.40456 36.77324 29.53 78.14 Download link
    ResNeXt152_
    32x4d    		 0.7898 0.9433 24.86299 29.36764 22.01 56.28 Download link
    ResNeXt152_vd_
    32x4d    		 0.8072 0.9520 25.03258 30.08987 22.49 56.3 Download link
    ResNeXt152_
    64x4d    		 0.7951 0.9471 46.7564 56.34108 43.03 107.57 Download link
    ResNeXt152_vd_
    64x4d    		 0.8108 0.9534 47.18638 57.16257 43.52 107.59 Download link
    SE_ResNet18_vd 0.7333 0.9138 1.7691 4.19877 4.14 11.8 Download link
    SE_ResNet34_vd 0.7651 0.9320 2.88559 7.03291 7.84 21.98 Download link
    SE_ResNet50_vd 0.7952 0.9475 4.28393 10.38846 8.67 28.09 Download link
    SE_ResNeXt50_
    32x4d    		 0.7844 0.9396 8.74121 13.563 8.02 26.16 Download link
    SE_ResNeXt50_vd_
    32x4d    		 0.8024 0.9489 9.17134 14.76192 10.76 26.28 Download link
    SE_ResNeXt101_
    32x4d    		 0.7912 0.9420 18.82604 25.31814 15.02 46.28 Download link
    SENet154_vd 0.8140 0.9548 53.79794 66.31684 45.83 114.29 Download link

    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.

    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
    DenseNet161 0.7857 0.9414 10.39152 22.15555 15.49 28.68 Download link
    DenseNet169 0.7681 0.9331 6.43598 12.98832 6.74 14.15 Download link
    DenseNet201 0.7763 0.9366 8.20652 17.45838 8.61 20.01 Download link
    DenseNet264 0.7796 0.9385 12.14722 26.27707 11.54 33.37 Download link
    DPN68 0.7678 0.9343 11.64915 12.82807 4.03 10.78 Download link
    DPN92 0.7985 0.9480 18.15746 23.87545 12.54 36.29 Download link
    DPN98 0.8059 0.9510 21.18196 33.23925 22.22 58.46 Download link
    DPN107 0.8089 0.9532 27.62046 52.65353 35.06 82.97 Download link
    DPN131 0.8070 0.9514 28.33119 46.19439 30.51 75.36 Download link

    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.

    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
    HRNet_W18_C_ssld 0.81162 0.95804 7.40636 13.29752 4.14 21.29 Download link
    HRNet_W30_C 0.7804 0.9402 9.57594 17.35485 16.23 37.71 Download link
    HRNet_W32_C 0.7828 0.9424 9.49807 17.72921 17.86 41.23 Download link
    HRNet_W40_C 0.7877 0.9447 12.12202 25.68184 25.41 57.55 Download link
    HRNet_W44_C 0.7900 0.9451 13.19858 32.25202 29.79 67.06 Download link
    HRNet_W48_C 0.7895 0.9442 13.70761 34.43572 34.58 77.47 Download link
    HRNet_W48_C_ssld 0.8363 0.9682 13.70761 34.43572 34.58 77.47 Download link
    HRNet_W64_C 0.7930 0.9461 17.57527 47.9533 57.83 128.06 Download link

    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.

    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
    Xception41 0.7930 0.9453 4.96939 17.01361 16.74 22.69 Download link
    Xception41_deeplab 0.7955 0.9438 5.33541 17.55938 18.16 26.73 Download link
    Xception65 0.8100 0.9549 7.26158 25.88778 25.95 35.48 Download link
    Xception65_deeplab 0.8032 0.9449 7.60208 26.03699 27.37 39.52 Download link
    Xception71 0.8111 0.9545 8.72457 31.55549 31.77 37.28 Download link
    InceptionV4 0.8077 0.9526 12.99342 25.23416 24.57 42.68 Download link

    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.

    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
    ResNeXt101_
    32x16d_wsl    		 0.8424 0.9726 25.60395 71.88384 57.55 152.66 Download link
    ResNeXt101_
    32x32d_wsl    		 0.8497 0.9759 54.87396 160.04337 115.17 303.11 Download link
    ResNeXt101_
    32x48d_wsl    		 0.8537 0.9769 99.01698256 315.91261 173.58 456.2 Download link
    Fix_ResNeXt101_
    32x48d_wsl    		 0.8626 0.9797 160.0838242 595.99296 354.23 456.2 Download link
    EfficientNetB0 0.7738 0.9331 3.442 6.11476 0.72 5.1 Download link
    EfficientNetB1 0.7915 0.9441 5.3322 9.41795 1.27 7.52 Download link
    EfficientNetB2 0.7985 0.9474 6.29351 10.95702 1.85 8.81 Download link
    EfficientNetB3 0.8115 0.9541 7.67749 16.53288 3.43 11.84 Download link
    EfficientNetB4 0.8285 0.9623 12.15894 30.94567 8.29 18.76 Download link
    EfficientNetB5 0.8362 0.9672 20.48571 61.60252 19.51 29.61 Download link
    EfficientNetB6 0.8400 0.9688 32.62402 - 36.27 42 Download link
    EfficientNetB7 0.8430 0.9689 53.93823 - 72.35 64.92 Download link
    EfficientNetB0_
    small    		 0.7580 0.9258 2.3076 4.71886 0.72 4.65 Download link

    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.

    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
    ResNeSt50 0.8102 0.9542 6.69042 8.01664 10.78 27.5 Download link
    RegNetX_4GF 0.785 0.9416 6.46478 11.19862 8 22.1 Download link

    License

    PaddleClas is released under the Apache 2.0 license

    Contribution

    Contributions are highly welcomed and we would really appreciate your feedback!!

    • Thank nblib to fix bug of RandErasing.
    • Thank chenpy228 to fix some typos PaddleClas.

    项目简介

    A treasure chest for visual classification and recognition powered by PaddlePaddle

    🚀 Github 镜像仓库 🚀

    源项目地址 ⬇ ⬇ ⬇

    https://github.com/PaddlePaddle/PaddleClas

    发行版本 9

    PaddleClas v2.5.1

    全部发行版

    贡献者 48

    C G S Y L L C W D B Y
    全部贡献者

    开发语言

    • Python 90.8 %
    • C++ 6.3 %
    • Shell 1.9 %
    • CMake 0.7 %
    • Makefile 0.3 %