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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

  • 2020.10.12 Add Paddle-Lite demo。
  • 2020.10.10 Add cpp inference demo and improve FAQ tutorial.
  • 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.
  • 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.

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飞桨图像分类套件PaddleClas是飞桨为工业界和学术界所准备的一个图像分类任务的工具集,助力使用者训练出更好的视觉模型和应用落地 spread retract
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