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Image classification and segmentation models for Gluon

Project description

Computer vision models on MXNet/Gluon

PyPI Downloads

This is a collection of image classification and segmentation models. Many of them are pretrained on ImageNet-1K, CIFAR-10/100, SVHN, CUB-200-2011, Pascal VOC2012, ADE20K, Cityscapes, and COCO datasets and loaded automatically during use. All pretrained models require the same ordinary normalization. Scripts for training/evaluating/converting models are in the imgclsmob repo.

List of implemented models

Installation

To use the models in your project, simply install the gluoncv2 package with mxnet:

pip install gluoncv2 mxnet>=1.2.1

To enable different hardware supports such as GPUs, check out MXNet variants. For example, you can install with CUDA-9.2 supported MXNet:

pip install gluoncv2 mxnet-cu92>=1.2.1

Usage

Example of using a pretrained ResNet-18 model:

from gluoncv2.model_provider import get_model as glcv2_get_model
import mxnet as mx

net = glcv2_get_model("resnet18", pretrained=True)
x = mx.nd.zeros((1, 3, 224, 224), ctx=mx.cpu())
y = net(x)

Pretrained models

ImageNet-1K

Some remarks:

  • Top1/Top5 are the standard 1-crop Top-1/Top-5 errors (in percents) on the validation subset of the ImageNet-1K dataset.
  • FLOPs/2 is the number of FLOPs divided by two to be similar to the number of MACs.
  • ResNet/PreResNet with b-suffix is a version of the networks with the stride in the second convolution of the bottleneck block. Respectively a network without b-suffix has the stride in the first convolution.
  • ResNet/PreResNet models do not use biases in convolutions at all.
  • CondenseNet models are only so-called converted versions.
  • ShuffleNetV2 and ShuffleNetV2b are different implementations of the same architecture.
  • ResNet(D) is a dilated ResNet intended for use as an feature extractor in some segmentation networks.
  • Models with *-suffix use non-standard preprocessing (see the training log).
Model Top1 Top5 Params FLOPs/2 Remarks
AlexNet 40.46 17.70 62,378,344 1,132.33M Training (log)
AlexNet-b 41.08 18.53 61,100,840 714.83M Training (log)
ZFNet 39.21 16.78 62,357,608 1,170.33M Training (log)
ZFNet-b 35.81 14.59 107,627,624 2,479.13M Training (log)
VGG-11 29.59 10.16 132,863,336 7,615.87M Training (log)
VGG-13 28.37 9.50 133,047,848 11,317.65M Training (log)
VGG-16 26.61 8.32 138,357,544 15,480.10M Training (log)
VGG-19 25.58 7.67 143,667,240 19,642.55M Training (log)
BN-VGG-11 28.56 9.34 132,866,088 7,630.21M Training (log)
BN-VGG-13 27.68 8.87 133,050,792 11,341.62M Training (log)
BN-VGG-16 25.50 7.57 138,361,768 15,506.38M Training (log)
BN-VGG-19 23.91 6.89 143,672,744 19,671.15M Training (log)
BN-VGG-11b 29.24 9.75 132,868,840 7,630.72M Training (log)
BN-VGG-13b 29.48 10.16 133,053,736 11,342.14M From dmlc/gluon-cv (log)
BN-VGG-16b 26.89 8.65 138,365,992 15,507.20M From dmlc/gluon-cv (log)
BN-VGG-19b 25.66 8.15 143,678,248 19,672.26M From dmlc/gluon-cv (log)
BN-Inception 25.12 7.54 11,295,240 2,048.06M Training (log)
ResNet-10 34.61 13.85 5,418,792 894.04M Training (log)
ResNet-12 33.42 13.03 5,492,776 1,126.25M Training (log)
ResNet-14 32.18 12.20 5,788,200 1,357.94M Training (log)
ResNet-BC-14b 30.26 11.16 10,064,936 1,479.12M Training (log)
ResNet-16 30.24 10.88 6,968,872 1,589.34M Training (log)
ResNet-18 x0.25 39.31 17.40 3,937,400 270.94M Training (log)
ResNet-18 x0.5 33.41 12.84 5,804,296 608.70M Training (log)
ResNet-18 x0.75 30.00 10.66 8,476,056 1,129.45M Training (log)
ResNet-18 28.09 9.51 11,689,512 1,820.41M Training (log)
ResNet-26 26.14 8.37 17,960,232 2,746.79M Training (log)
ResNet-BC-26b 24.86 7.58 15,995,176 2,356.67M Training (log)
ResNet-34 24.53 7.43 21,797,672 3,672.68M Training (log)
ResNet-BC-38b 23.50 6.72 21,925,416 3,234.21M Training (log)
ResNet-50 22.15 6.04 25,557,032 3,877.95M Training (log)
ResNet-50b 22.06 6.11 25,557,032 4,110.48M Training (log)
ResNet-101 21.66 5.99 44,549,160 7,597.95M From dmlc/gluon-cv (log)
ResNet-101b 20.26 5.12 44,549,160 7,830.48M Training (log)
ResNet-152 20.76 5.35 60,192,808 11,321.85M From dmlc/gluon-cv (log)
ResNet-152b 19.63 4.80 60,192,808 11,554.38M Training (log)
PreResNet-10 34.65 14.01 5,417,128 894.19M Training (log)
PreResNet-12 33.57 13.21 5,491,112 1,126.40M Training (log)
PreResNet-14 32.29 12.18 5,786,536 1,358.09M Training (log)
PreResNet-BC-14b 30.67 11.51 10,057,384 1,476.62M Training (log)
PreResNet-16 30.21 10.81 6,967,208 1,589.49M Training (log)
PreResNet-18 x0.25 39.62 17.78 3,935,960 270.93M Training (log)
PreResNet-18 x0.5 33.67 13.19 5,802,440 608.73M Training (log)
PreResNet-18 x0.75 29.96 10.68 8,473,784 1,129.51M Training (log)
PreResNet-18 28.16 9.51 11,687,848 1,820.56M Training (log)
PreResNet-26 26.03 8.34 17,958,568 2,746.94M Training (log)
PreResNet-BC-26b 25.21 7.86 15,987,624 2,354.16M Training (log)
PreResNet-34 24.55 7.51 21,796,008 3,672.83M Training (log)
PreResNet-BC-38b 22.67 6.33 21,917,864 3,231.70M Training (log)
PreResNet-50 22.27 6.20 25,549,480 3,875.44M Training (log)
PreResNet-50b 22.36 6.32 25,549,480 4,107.97M Training (log)
PreResNet-101 21.45 5.75 44,541,608 7,595.44M From dmlc/gluon-cv (log)
PreResNet-101b 20.85 5.40 44,541,608 7,827.97M Training (log)
PreResNet-152 20.70 5.32 60,185,256 11,319.34M From dmlc/gluon-cv (log)
PreResNet-152b 19.90 5.00 60,185,256 11,551.87M Training (log)
PreResNet-200b 21.10 5.64 64,666,280 15,068.63M From tornadomeet/ResNet (log)
PreResNet-269b 20.71 5.56 102,065,832 20,101.11M From soeaver/mxnet-model (log)
ResNeXt-14 (16x4d) 31.66 12.23 7,127,336 1,045.77M Training (log)
ResNeXt-14 (32x2d) 32.16 12.47 7,029,416 1,031.32M Training (log)
ResNeXt-14 (32x4d) 29.95 11.10 9,411,880 1,603.46M Training (log)
ResNeXt-26 (32x2d) 26.34 8.50 9,924,136 1,461.06M Training (log)
ResNeXt-26 (32x4d) 23.93 7.21 15,389,480 2,488.07M Training (log)
ResNeXt-50 (32x4d) 20.64 5.46 25,028,904 4,255.86M From dmlc/gluon-cv (log)
ResNeXt-101 (32x4d) 19.62 4.92 44,177,704 8,003.45M From dmlc/gluon-cv (log)
ResNeXt-101 (64x4d) 19.28 4.83 83,455,272 15,500.27M From dmlc/gluon-cv (log)
SE-ResNet-10 33.55 13.29 5,463,332 894.08M Training (log)
SE-ResNet-18 27.95 9.20 11,778,592 1,820.51M Training (log)
SE-ResNet-26 25.42 8.03 18,093,852 2,746.93M Training (log)
SE-ResNet-BC-26b 23.44 6.82 17,395,976 2,358.07M Training (log)
SE-ResNet-BC-38b 21.44 5.75 24,026,616 3,236.32M Training (log)
SE-ResNet-50 21.07 5.60 28,088,024 3,880.49M Training (log)
SE-ResNet-50b 20.58 5.33 28,088,024 4,113.02M Training (log)
SE-ResNet-101 21.92 5.89 49,326,872 7,602.76M From Cadene/pretrained...pytorch (log)
SE-ResNet-152 21.48 5.77 66,821,848 11,328.52M From Cadene/pretrained...pytorch (log)
SE-PreResNet-10 33.60 13.06 5,461,668 894.23M Training (log)
SE-PreResNet-18 27.67 9.38 11,776,928 1,820.66M Training (log)
SE-PreResNet-BC-26b 22.95 6.36 17,388,424 2,355.57M Training (log)
SE-PreResNet-BC-38b 21.42 5.63 24,019,064 3,233.81M Training (log)
SE-ResNeXt-50 (32x4d) 20.03 5.05 27,559,896 4,258.40M From dmlc/gluon-cv (log)
SE-ResNeXt-101 (32x4d) 19.07 4.60 48,955,416 8,008.26M From dmlc/gluon-cv (log)
SE-ResNeXt-101 (64x4d) 18.98 4.66 88,232,984 15,505.08M From dmlc/gluon-cv (log)
SENet-16 25.34 8.06 31,366,168 5,080.55M Training (log)
SENet-28 21.68 5.91 36,453,768 5,731.20M Training (log)
SENet-154 18.84 4.65 115,088,984 20,745.78M From Cadene/pretrained...pytorch (log)
IBN-ResNet-50 23.56 6.68 25,557,032 4,110.48M From XingangPan/IBN-Net (log)
IBN-ResNet-101 21.89 5.87 44,549,160 7,830.48M From XingangPan/IBN-Net (log)
IBN(b)-ResNet-50 23.91 6.97 25,558,568 4,112.89M From XingangPan/IBN-Net (log)
IBN-ResNeXt-101 (32x4d) 21.43 5.62 44,177,704 8,003.45M From XingangPan/IBN-Net (log)
IBN-DenseNet-121 24.98 7.47 7,978,856 2,872.13M From XingangPan/IBN-Net (log)
IBN-DenseNet-169 23.78 6.82 14,149,480 3,403.89M From XingangPan/IBN-Net (log)
AirNet50-1x64d (r=2) 22.48 6.21 27,425,864 4,772.11M From soeaver/AirNet-PyTorch (log)
AirNet50-1x64d (r=16) 22.91 6.46 25,714,952 4,399.97M From soeaver/AirNet-PyTorch (log)
AirNeXt50-32x4d (r=2) 21.51 5.75 27,604,296 5,339.58M From soeaver/AirNet-PyTorch (log)
BAM-ResNet-50 23.68 6.96 25,915,099 4,196.09M From Jongchan/attention-module (log)
CBAM-ResNet-50 23.02 6.38 28,089,624 4,116.97M From Jongchan/attention-module (log)
PyramidNet-101 (a=360) 22.72 6.52 42,455,070 8,743.54M From dyhan0920/Pyramid...PyTorch (log)
DiracNetV2-18 30.61 11.17 11,511,784 1,796.62M From szagoruyko/diracnets (log)
DiracNetV2-34 27.93 9.46 21,616,232 3,646.93M From szagoruyko/diracnets (log)
CRU-Net-56 25.72 8.25 25,609,384 5,660.66M From cypw/CRU-Net (log)
DenseNet-121 23.25 6.85 7,978,856 2,872.13M Training (log)
DenseNet-161 21.82 5.92 28,681,000 7,793.16M Training (log)
DenseNet-169 22.10 6.05 14,149,480 3,403.89M Training (log)
DenseNet-201 21.56 5.90 20,013,928 4,347.15M Training (log)
CondenseNet-74 (C=G=4) 26.82 8.64 4,773,944 546.06M From ShichenLiu/CondenseNet (log)
CondenseNet-74 (C=G=8) 29.76 10.49 2,935,416 291.52M From ShichenLiu/CondenseNet (log)
PeleeNet 31.71 11.25 2,802,248 514.87M Training (log)
WRN-50-2 22.15 6.12 68,849,128 11,405.42M From szagoruyko/functional-zoo (log)
DRN-C-26 25.68 7.89 21,126,584 16,993.90M From fyu/drn (log)
DRN-C-42 23.80 6.92 31,234,744 25,093.75M From fyu/drn (log)
DRN-C-58 22.35 6.27 40,542,008 32,489.94M From fyu/drn (log)
DRN-D-22 26.67 8.52 16,393,752 13,051.33M From fyu/drn (log)
DRN-D-38 24.51 7.36 26,501,912 21,151.19M From fyu/drn (log)
DRN-D-54 22.05 6.27 35,809,176 28,547.38M From fyu/drn (log)
DRN-D-105 21.31 5.81 54,801,304 43,442.43M From fyu/drn (log)
DPN-68 22.87 6.58 12,611,602 2,351.84M Training (log)
DPN-98 20.23 5.28 61,570,728 11,716.51M From Cadene/pretrained...pytorch (log)
DPN-131 20.03 5.22 79,254,504 16,076.15M From Cadene/pretrained...pytorch (log)
DarkNet Tiny 40.31 17.46 1,042,104 500.85M Training (log)
DarkNet Ref 38.00 16.68 7,319,416 367.59M Training (log)
DarkNet-53 21.44 5.56 41,609,928 7,133.86M From dmlc/gluon-cv (log)
i-RevNet-301 26.97 8.97 125,120,356 14,453.87M From jhjacobsen/pytorch-i-revnet (log)
BagNet-9 59.57 35.44 15,688,744 16,049.19M From wielandbrendel/bag...models (log)
BagNet-17 44.76 21.52 16,213,032 15,768.77M From wielandbrendel/bag...models (log)
BagNet-33 36.43 14.95 18,310,184 16,371.52M From wielandbrendel/bag...models (log)
DLA-34 26.14 8.21 15,742,104 3,071.37M From ucbdrive/dla (log)
DLA-46-C 33.84 12.86 1,301,400 585.45M Training (log)
DLA-X-46-C 32.96 12.25 1,068,440 546.72M Training (log)
DLA-60 23.84 7.08 22,036,632 4,255.49M From ucbdrive/dla (log)
DLA-X-60 22.48 6.21 17,352,344 3,543.68M From ucbdrive/dla (log)
DLA-X-60-C 30.67 10.74 1,319,832 596.06M Training (log)
DLA-102 22.87 6.44 33,268,888 7,190.95M From ucbdrive/dla (log)
DLA-X-102 21.97 6.02 26,309,272 5,884.94M From ucbdrive/dla (log)
DLA-X2-102 21.12 5.53 41,282,200 9,340.61M From ucbdrive/dla (log)
DLA-169 21.95 5.87 53,389,720 11,593.20M From ucbdrive/dla (log)
FishNet-150 22.85 6.38 24,959,400 6,435.02M From kevin-ssy/FishNet (log)
ESPNetv2 x0.5 43.61 21.07 1,241,332 35.36M From sacmehta/ESPNetv2 (log)
ESPNetv2 x1.0 35.33 14.27 1,670,072 98.09M From sacmehta/ESPNetv2 (log)
ESPNetv2 x1.25 33.14 12.73 1,965,440 138.18M From sacmehta/ESPNetv2 (log)
ESPNetv2 x1.5 32.04 11.94 2,314,856 185.77M From sacmehta/ESPNetv2 (log)
ESPNetv2 x2.0 28.91 9.94 3,498,136 306.93M From sacmehta/ESPNetv2 (log)
HRNet-W18 Small V1 28.46 9.75 13,187,464 1,614.87M From HRNet/HRNet...ation (log)
HRNet-W18 Small V2 25.75 8.02 15,597,464 2,618.54M From HRNet/HRNet...ation (log)
HRNetV2-W18 24.01 6.85 21,299,004 4,322.66M From HRNet/HRNet...ation (log)
HRNetV2-W30 22.31 6.07 37,712,220 8,156.14M From HRNet/HRNet...ation (log)
HRNetV2-W32 22.27 6.07 41,232,680 8,973.31M From HRNet/HRNet...ation (log)
HRNetV2-W40 21.72 5.71 57,557,160 12,751.34M From HRNet/HRNet...ation (log)
HRNetV2-W44 21.73 5.92 67,064,984 14,945.95M From HRNet/HRNet...ation (log)
HRNetV2-W48 21.41 5.78 77,469,864 17,344.29M From HRNet/HRNet...ation (log)
HRNetV2-W64 21.08 5.52 128,059,944 28,974.95M From HRNet/HRNet...ation (log)
VoVNet-39 23.90 6.89 22,600,296 7,086.16M From stigma0617/VoVNet.pytorch (log)
VoVNet-57 22.95 6.60 36,640,296 8,943.09M From stigma0617/VoVNet.pytorch (log)
SelecSLS-42b 23.31 6.78 32,458,248 2,980.62M From rwightman/pyt...models (log)
SelecSLS-60 22.50 6.33 30,670,768 3,591.78M From rwightman/pyt...models (log)
SelecSLS-60b 21.90 6.00 32,774,064 3,629.14M From rwightman/pyt...models (log)
HarDNet-39DS 28.71 10.04 3,488,228 437.52M From PingoLH/Pytorch-HarDNet (log)
HarDNet-68DS 26.43 8.47 4,180,602 788.86M From PingoLH/Pytorch-HarDNet (log)
HarDNet-68 24.58 7.36 17,565,348 4,256.32M From PingoLH/Pytorch-HarDNet (log)
HarDNet-85 22.61 6.42 36,670,212 9,088.58M From PingoLH/Pytorch-HarDNet (log)
SqueezeNet v1.0 38.73 17.34 1,248,424 823.67M Training (log)
SqueezeNet v1.1 39.09 17.39 1,235,496 352.02M Training (log)
SqueezeResNet v1.0 39.32 17.67 1,248,424 823.67M Training (log)
SqueezeResNet v1.1 39.83 17.84 1,235,496 352.02M Training (log)
1.0-SqNxt-23 42.25 18.66 724,056 287.28M Training (log)
1.0-SqNxt-23v5 40.43 17.43 921,816 285.82M Training (log)
1.5-SqNxt-23 34.46 13.21 1,511,824 552.39M Training (log)
1.5-SqNxt-23v5 33.48 12.68 1,953,616 550.97M Training (log)
2.0-SqNxt-23 30.24 10.63 2,583,752 898.48M Training (log)
2.0-SqNxt-23v5 29.27 10.24 3,366,344 897.60M Training (log)
ShuffleNet x0.25 (g=1) 62.00 36.77 209,746 12.35M Training (log)
ShuffleNet x0.25 (g=3) 61.34 36.17 305,902 13.09M Training (log)
ShuffleNet x0.5 (g=1) 46.22 22.38 534,484 41.16M Training (log)
ShuffleNet x0.5 (g=3) 43.83 20.60 718,324 41.70M Training (log)
ShuffleNet x0.75 (g=1) 39.25 16.75 975,214 86.42M Training (log)
ShuffleNet x0.75 (g=3) 37.81 16.09 1,238,266 85.82M Training (log)
ShuffleNet x1.0 (g=1) 34.41 13.50 1,531,936 148.13M Training (log)
ShuffleNet x1.0 (g=2) 33.98 13.32 1,733,848 147.60M Training (log)
ShuffleNet x1.0 (g=3) 33.96 13.29 1,865,728 145.46M Training (log)
ShuffleNet x1.0 (g=4) 33.84 13.10 1,968,344 143.33M Training (log)
ShuffleNet x1.0 (g=8) 33.65 13.19 2,434,768 150.76M Training (log)
ShuffleNetV2 x0.5 40.61 18.30 1,366,792 43.31M Training (log)
ShuffleNetV2 x1.0 30.94 11.23 2,278,604 149.72M Training (log)
ShuffleNetV2 x1.5 27.17 9.13 4,406,098 320.77M Training (log)
ShuffleNetV2 x2.0 25.80 8.23 7,601,686 595.84M Training (log)
ShuffleNetV2b x0.5 39.81 17.82 1,366,792 43.31M Training (log)
ShuffleNetV2b x1.0 30.39 11.01 2,279,760 150.62M Training (log)
ShuffleNetV2b x1.5 26.90 8.79 4,410,194 323.98M Training (log)
ShuffleNetV2b x2.0 25.20 8.10 7,611,290 603.37M Training (log)
108-MENet-8x1 (g=3) 43.62 20.30 654,516 42.68M Training (log)
128-MENet-8x1 (g=4) 42.10 19.13 750,796 45.98M Training (log)
128-MENet-8x1 (g=4) 42.10 19.13 750,796 45.98M Training (log)
160-MENet-8x1 (g=8) 43.47 20.28 850,120 45.63M Training (log)
256-MENet-12x1 (g=4) 32.23 12.16 1,888,240 150.65M Training (log)
348-MENet-12x1 (g=3) 27.85 9.36 3,368,128 312.00M Training (log)
352-MENet-12x1 (g=8) 31.30 11.67 2,272,872 157.35M Training (log)
456-MENet-24x1 (g=3) 25.02 7.80 5,304,784 567.90M Training (log)
MobileNet x0.25 45.78 22.18 470,072 44.09M Training (log)
MobileNet x0.5 33.94 13.30 1,331,592 155.42M Training (log)
MobileNet x0.75 29.85 10.51 2,585,560 333.99M Training (log)
MobileNet x1.0 26.43 8.65 4,231,976 579.80M Training (log)
FD-MobileNet x0.25 55.44 30.53 383,160 12.95M Training (log)
FD-MobileNet x0.5 42.62 19.69 993,928 41.84M Training (log)
FD-MobileNet x0.75 37.91 16.01 1,833,304 86.68M Training (log)
FD-MobileNet x1.0 33.80 13.12 2,901,288 147.46M Training (log)
MobileNetV2 x0.25 48.08 24.12 1,516,392 34.24M Training (log)
MobileNetV2 x0.5 35.63 14.42 1,964,736 100.13M Training (log)
MobileNetV2 x0.75 29.78 10.44 2,627,592 198.50M Training (log)
MobileNetV2 x1.0 26.77 8.64 3,504,960 329.36M Training (log)
MobileNetV3 L/224/1.0 24.63 7.69 5,481,752 226.80M From dmlc/gluon-cv (log)
IGCV3 x0.25 53.43 28.30 1,534,020 41.29M Training (log)
IGCV3 x0.5 39.41 17.03 1,985,528 111.12M Training (log)
IGCV3 x0.75 30.71 10.96 2,638,084 210.95M Training (log)
IGCV3 x1.0 27.73 9.00 3,491,688 340.79M Training (log)
MnasNet-B1 25.76 8.00 4,383,312 326.30M From rwightman/pyt...models (log)
MnasNet-A1 25.02 7.55 3,887,038 325.77M From rwightman/pyt...models (log)
DARTS 27.23 8.97 4,718,752 539.86M From quark0/darts (log)
ProxylessNAS CPU 24.78 7.50 4,361,648 459.96M Training (log)
ProxylessNAS GPU 24.67 7.24 7,119,848 476.08M Training (log)
ProxylessNAS Mobile 25.31 7.80 4,080,512 332.46M Training (log)
ProxylessNAS Mob-14 22.96 6.51 6,857,568 597.10M Training (log)
FBNet-Cb 25.47 7.86 5,572,200 399.26M From rwightman/pyt...models (log)
Xception 20.99 5.56 22,855,952 8,403.63M From Cadene/pretrained...pytorch (log)
InceptionV3 21.22 5.59 23,834,568 5,743.06M From dmlc/gluon-cv (log)
InceptionV4 20.60 5.25 42,679,816 12,304.93M From Cadene/pretrained...pytorch (log)
InceptionResNetV2 19.96 4.94 55,843,464 13,188.64M From Cadene/pretrained...pytorch (log)
PolyNet 19.09 4.53 95,366,600 34,821.34M From Cadene/pretrained...pytorch (log)
NASNet-A 4@1056 25.37 7.95 5,289,978 584.90M From Cadene/pretrained...pytorch (log)
NASNet-A 6@4032 18.17 4.24 88,753,150 23,976.44M From Cadene/pretrained...pytorch (log)
PNASNet-5-Large 17.90 4.28 86,057,668 25,140.77M From Cadene/pretrained...pytorch (log)
SPNASNet 26.92 8.67 4,421,616 346.73M From rwightman/pyt...models (log)
EfficientNet-B0 24.50 7.22 5,288,548 413.13M Training (log)
EfficientNet-B1 22.89 6.26 7,794,184 730.44M Training (log)
EfficientNet-B0b 22.96 6.70 5,288,548 413.13M From rwightman/pyt...models (log)
EfficientNet-B1b 20.98 5.65 7,794,184 730.44M From rwightman/pyt...models (log)
EfficientNet-B2b 19.94 5.16 9,109,994 1,049.29M From rwightman/pyt...models (log)
EfficientNet-B3b 18.60 4.31 12,233,232 1,923.98M From rwightman/pyt...models (log)
EfficientNet-B4b 17.25 3.76 19,341,616 4,597.56M From rwightman/pyt...models (log)
EfficientNet-B5b 16.39 3.34 30,389,784 10,674.67M From rwightman/pyt...models (log)
EfficientNet-B6b 15.96 3.12 43,040,704 19,761.35M From rwightman/pyt...models (log)
EfficientNet-B7b 15.70 3.11 66,347,960 38,949.07M From rwightman/pyt...models (log)
EfficientNet-B0c* 22.52 6.46 5,288,548 413.13M From rwightman/pyt...models (log)
EfficientNet-B1c* 20.50 5.55 7,794,184 730.44M From rwightman/pyt...models (log)
EfficientNet-B2c* 19.60 4.89 9,109,994 1,049.29M From rwightman/pyt...models (log)
EfficientNet-B3c* 18.19 4.34 12,233,232 1,923.98M From rwightman/pyt...models (log)
EfficientNet-B4c* 16.74 3.59 19,341,616 4,597.56M From rwightman/pyt...models (log)
EfficientNet-B5c* 15.79 3.02 30,389,784 10,674.67M From rwightman/pyt...models (log)
EfficientNet-B6c* 15.29 2.85 43,040,704 19,761.35M From rwightman/pyt...models (log)
EfficientNet-B7c* 14.87 2.77 66,347,960 38,949.07M From rwightman/pyt...models (log)
EfficientNet-B8c* 14.61 2.70 87,413,142 64,446.06M From rwightman/pyt...models (log)
EfficientNet-Edge-Small-b* 22.48 6.29 5,438,392 2,378.09M From rwightman/pyt...models (log)
EfficientNet-Edge-Medium-b* 21.08 5.53 6,899,496 3,700.08M From rwightman/pyt...models (log)
EfficientNet-Edge-Large-b* 19.50 4.77 10,589,712 9,747.58M From rwightman/pyt...models (log)
MixNet-S 24.32 7.39 4,134,606 260.26M From rwightman/pyt...models (log)
MixNet-M 23.31 6.78 5,014,382 366.05M From rwightman/pyt...models (log)
MixNet-L 21.53 6.03 7,329,252 590.45M From rwightman/pyt...models (log)
ResNet(D)-50b 20.79 5.49 25,680,808 20,496.80M From dmlc/gluon-cv (log)
ResNet(D)-101b 19.49 4.61 44,672,936 35,391.85M From dmlc/gluon-cv (log)
ResNet(D)-152b 19.39 4.67 60,316,584 47,661.38M From dmlc/gluon-cv (log)

CIFAR-10

Some remarks:

  • Testing subset is used for validation purpose.
  • Features means feature extractor output size.
Model Error, % Features Params FLOPs/2 Remarks
NIN 7.43 192 966,986 222.97M Training (log)
ResNet-20 5.97 64 272,474 41.29M Training (log)
ResNet-56 4.52 64 855,770 127.06M Training (log)
ResNet-110 3.69 64 1,730,714 255.70M Training (log)
ResNet-164(BN) 3.68 256 1,704,154 255.31M Training (log)
ResNet-272(BN) 3.33 256 2,816,986 420.61M Training (log)
ResNet-542(BN) 3.43 256 5,599,066 833.87M Training (log)
ResNet-1001 3.28 256 10,328,602 1,536.40M Training (log)
ResNet-1202 3.53 64 19,424,026 2,857.17M Training (log)
PreResNet-20 6.51 64 272,282 41.27M Training (log)
PreResNet-56 4.49 64 855,578 127.03M Training (log)
PreResNet-110 3.86 64 1,730,522 255.68M Training (log)
PreResNet-164(BN) 3.64 256 1,703,258 255.08M Training (log)
PreResNet-272(BN) 3.25 256 2,816,090 420.38M Training (log)
PreResNet-542(BN) 3.14 256 5,598,170 833.64M Training (log)
PreResNet-1001 2.65 256 10,327,706 1,536.18M Training (log)
PreResNet-1202 3.39 64 19,423,834 2,857.14M Training (log)
ResNeXt-20 (1x64d) 4.33 1024 3,446,602 538.36M Training (log)
ResNeXt-20 (2x32d) 4.53 1024 2,672,458 425.15M Training (log)
ResNeXt-20 (4x16d) 4.70 1024 2,285,386 368.55M Training (log)
ResNeXt-20 (8x8d) 4.66 1024 2,091,850 340.25M Training (log)
ResNeXt-20 (16x4d) 4.04 1024 1,995,082 326.10M Training (log)
ResNeXt-20 (32x2d) 4.61 1024 1,946,698 319.03M Training (log)
ResNeXt-20 (64x1d) 4.93 1024 1,922,506 315.49M Training (log)
ResNeXt-20 (2x64d) 4.03 1024 6,198,602 987.98M Training (log)
ResNeXt-20 (4x32d) 3.73 1024 4,650,314 761.57M Training (log)
ResNeXt-20 (8x16d) 4.04 1024 3,876,170 648.37M Training (log)
ResNeXt-20 (16x8d) 3.94 1024 3,489,098 591.77M Training (log)
ResNeXt-20 (32x4d) 4.20 1024 3,295,562 563.47M Training (log)
ResNeXt-20 (64x2d) 4.38 1024 3,198,794 549.32M Training (log)
ResNeXt-56 (1x64d) 2.87 1024 9,317,194 1,399.33M Training (log)
ResNeXt-56 (2x32d) 3.01 1024 6,994,762 1,059.72M Training (log)
ResNeXt-56 (4x16d) 3.11 1024 5,833,546 889.91M Training (log)
ResNeXt-56 (8x8d) 3.07 1024 5,252,938 805.01M Training (log)
ResNeXt-56 (16x4d) 3.12 1024 4,962,634 762.56M Training (log)
ResNeXt-56 (32x2d) 3.14 1024 4,817,482 741.34M Training (log)
ResNeXt-56 (64x1d) 3.41 1024 4,744,906 730.72M Training (log)
ResNeXt-29 (32x4d) 3.15 1024 4,775,754 780.55M Training (log)
ResNeXt-29 (16x64d) 2.41 1024 68,155,210 10,709.34M Training (log)
ResNeXt-272 (1x64d) 2.55 1024 44,540,746 6,565.15M Training (log)
ResNeXt-272 (2x32d) 2.74 1024 32,928,586 4,867.11M Training (log)
SE-ResNet-20 6.01 64 274,847 41.30M Training (log)
SE-ResNet-56 4.13 64 862,889 127.07M Training (log)
SE-ResNet-110 3.63 64 1,744,952 255.72M Training (log)
SE-ResNet-164(BN) 3.39 256 1,906,258 255.52M Training (log)
SE-ResNet-272(BN) 3.39 256 3,153,826 420.96M Training (log)
SE-ResNet-542(BN) 3.47 256 6,272,746 834.57M Training (log)
SE-PreResNet-20 6.18 64 274,559 41.30M Training (log)
SE-PreResNet-56 4.51 64 862,601 127.07M Training (log)
SE-PreResNet-110 4.54 64 1,744,664 255.72M Training (log)
SE-PreResNet-164(BN) 3.73 256 1,904,882 255.29M Training (log)
SE-PreResNet-272(BN) 3.39 256 3,152,450 420.73M Training (log)
SE-PreResNet-542(BN) 3.08 256 6,271,370 834.34M Training (log)
PyramidNet-110 (a=48) 3.72 64 1,772,706 408.37M Training (log)
PyramidNet-110 (a=84) 2.98 100 3,904,446 778.15M Training (log)
PyramidNet-110 (a=270) 2.51 286 28,485,477 4,730.60M Training (log)
PyramidNet-164 (a=270, BN) 2.42 1144 27,216,021 4,608.81M Training (log)
PyramidNet-200 (a=240, BN) 2.44 1024 26,752,702 4,563.40M Training (log)
PyramidNet-236 (a=220, BN) 2.47 944 26,969,046 4,631.32M Training (log)
PyramidNet-272 (a=200, BN) 2.39 864 26,210,842 4,541.36M Training (log)
DenseNet-40 (k=12) 5.61 258 599,050 210.80M Training (log)
DenseNet-BC-40 (k=12) 6.43 132 176,122 74.89M Training (log)
DenseNet-BC-40 (k=24) 4.52 264 690,346 293.09M Training (log)
DenseNet-BC-40 (k=36) 4.04 396 1,542,682 654.60M Training (log)
DenseNet-100 (k=12) 3.66 678 4,068,490 1,353.55M Training (log)
DenseNet-100 (k=24) 3.13 1356 16,114,138 5,354.19M Training (log)
DenseNet-BC-100 (k=12) 4.16 342 769,162 298.45M Training (log)
DenseNet-BC-190 (k=40) 2.52 2190 25,624,430 9,400.45M Training (log)
DenseNet-BC-250 (k=24) 2.67 1734 15,324,406 5,519.54M Training (log)
X-DenseNet-BC-40-2 (k=24) 5.31 264 690,346 293.09M Training (log)
X-DenseNet-BC-40-2 (k=36) 4.37 396 1,542,682 654.60M Training (log)
WRN-16-10 2.93 640 17,116,634 2,414.04M Training (log)
WRN-28-10 2.39 640 36,479,194 5,246.98M Training (log)
WRN-40-8 2.37 512 35,748,314 5,176.90M Training (log)
WRN-20-10-1bit 3.26 640 26,737,140 4,019.14M Training (log)
WRN-20-10-32bit 3.14 640 26,737,140 4,019.14M Training (log)
RoR-3-56 5.43 64 762,746 113.43M Training (log)
RoR-3-110 4.35 64 1,637,690 242.07M Training (log)
RoR-3-164 3.93 64 2,512,634 370.72M Training (log)
RiR 3.28 384 9,492,980 1,281.08M Training (log)
Shake-Shake-ResNet-20-2x16d 5.15 64 541,082 81.78M Training (log)
Shake-Shake-ResNet-26-2x32d 3.17 64 2,923,162 428.89M Training (log)
DIA-ResNet-20 6.22 64 286,866 41.34M Training (log)
DIA-ResNet-56 5.05 64 870,162 127.18M Training (log)
DIA-ResNet-110 4.10 64 1,745,106 255.94M Training (log)
DIA-ResNet-164(BN) 3.50 256 1,923,002 259.18M Training (log)
DIA-PreResNet-20 6.42 64 286,674 41.31M Training (log)
DIA-PreResNet-56 4.83 64 869,970 127.15M Training (log)
DIA-PreResNet-110 4.25 64 1,744,914 255.92M Training (log)
DIA-PreResNet-164(BN) 3.56 256 1,922,106 258.95M Training (log)

CIFAR-100

Some remarks:

  • Testing subset is used for validation purpose.
Model Error, % Params FLOPs/2 Remarks
NIN 28.39 984,356 224.08M Training (log)
ResNet-20 29.64 278,324 41.30M Training (log)
ResNet-56 24.88 861,620 127.06M Training (log)
ResNet-110 22.80 1,736,564 255.71M Training (log)
ResNet-164(BN) 20.44 1,727,284 255.33M Training (log)
ResNet-272(BN) 20.07 2,840,116 420.63M Training (log)
ResNet-542(BN) 19.32 5,622,196 833.89M Training (log)
ResNet-1001 19.79 10,351,732 1,536.43M Training (log)
ResNet-1202 21.56 19,429,876 2,857.17M Training (log)
PreResNet-20 30.22 278,132 41.28M Training (log)
PreResNet-56 25.05 861,428 127.04M Training (log)
PreResNet-110 22.67 1,736,372 255.68M Training (log)
PreResNet-164(BN) 20.18 1,726,388 255.10M Training (log)
PreResNet-272(BN) 19.63 2,839,220 420.40M Training (log)
PreResNet-542(BN) 18.71 5,621,300 833.66M Training (log)
PreResNet-1001 18.41 10,350,836 1,536.20M Training (log)
ResNeXt-20 (1x64d) 21.97 3,538,852 538.45M Training (log)
ResNeXt-20 (2x32d) 22.55 2,764,708 425.25M Training (log)
ResNeXt-20 (4x16d) 23.04 2,377,636 368.65M Training (log)
ResNeXt-20 (8x8d) 22.82 2,184,100 340.34M Training (log)
ResNeXt-20 (16x4d) 22.82 2,087,332 326.19M Training (log)
ResNeXt-20 (32x2d) 21.73 2,038,948 319.12M Training (log)
ResNeXt-20 (64x1d) 23.53 2,014,756 315.58M Training (log)
ResNeXt-20 (2x64d) 20.60 6,290,852 988.07M Training (log)
ResNeXt-20 (4x32d) 21.31 4,742,564 761.66M Training (log)
ResNeXt-20 (8x16d) 21.72 3,968,420 648.46M Training (log)
ResNeXt-20 (16x8d) 21.73 3,581,348 591.86M Training (log)
ResNeXt-20 (32x4d) 22.13 3,387,812 563.56M Training (log)
ResNeXt-20 (64x2d) 22.35 3,291,044 549.41M Training (log)
ResNeXt-56 (1x64d) 18.25 9,409,444 1,399.42M Training (log)
ResNeXt-56 (2x32d) 17.86 7,087,012 1,059.81M Training (log)
ResNeXt-56 (4x16d) 18.09 5,925,796 890.01M Training (log)
ResNeXt-56 (8x8d) 18.06 5,345,188 805.10M Training (log)
ResNeXt-56 (16x4d) 18.24 5,054,884 762.65M Training (log)
ResNeXt-56 (32x2d) 18.60 4,909,732 741.43M Training (log)
ResNeXt-56 (64x1d) 18.16 4,837,156 730.81M Training (log)
ResNeXt-29 (32x4d) 19.50 4,868,004 780.64M Training (log)
ResNeXt-29 (16x64d) 16.93 68,247,460 10,709.43M Training (log)
ResNeXt-272 (1x64d) 19.11 44,632,996 6,565.25M Training (log)
ResNeXt-272 (2x32d) 18.34 33,020,836 4,867.20M Training (log)
SE-ResNet-20 28.54 280,697 41.30M Training (log)
SE-ResNet-56 22.94 868,739 127.07M Training (log)
SE-ResNet-110 20.86 1,750,802 255.72M Training (log)
SE-ResNet-164(BN) 19.95 1,929,388 255.54M Training (log)
SE-ResNet-272(BN) 19.07 3,176,956 420.98M Training (log)
SE-ResNet-542(BN) 18.87 6,295,876 834.59M Training (log)
SE-PreResNet-20 28.31 280,409 41.31M Training (log)
SE-PreResNet-56 23.05 868,451 127.08M Training (log)
SE-PreResNet-110 22.61 1,750,514 255.73M Training (log)
SE-PreResNet-164(BN) 20.05 1,928,012 255.31M Training (log)
SE-PreResNet-272(BN) 19.13 3,175,580 420.75M Training (log)
SE-PreResNet-542(BN) 19.45 6,294,500 834.36M Training (log)
PyramidNet-110 (a=48) 20.95 1,778,556 408.38M Training (log)
PyramidNet-110 (a=84) 18.87 3,913,536 778.16M Training (log)
PyramidNet-110 (a=270) 17.10 28,511,307 4,730.62M Training (log)
PyramidNet-164 (a=270, BN) 16.70 27,319,071 4,608.91M Training (log)
PyramidNet-200 (a=240, BN) 16.09 26,844,952 4,563.49M Training (log)
PyramidNet-236 (a=220, BN) 16.34 27,054,096 4,631.41M Training (log)
PyramidNet-272 (a=200, BN) 16.19 26,288,692 4,541.43M Training (log)
DenseNet-40 (k=12) 24.90 622,360 210.82M Training (log)
DenseNet-BC-40 (k=12) 28.41 188,092 74.90M Training (log)
DenseNet-BC-40 (k=24) 22.67 714,196 293.11M Training (log)
DenseNet-BC-40 (k=36) 20.50 1,578,412 654.64M Training (log)
DenseNet-100 (k=12) 19.64 4,129,600 1,353.62M Training (log)
DenseNet-100 (k=24) 18.08 16,236,268 5,354.32M Training (log)
DenseNet-BC-100 (k=12) 21.19 800,032 298.48M Training (log)
DenseNet-BC-250 (k=24) 17.39 15,480,556 5,519.69M Training (log)
X-DenseNet-BC-40-2 (k=24) 23.96 714,196 293.11M Training (log)
X-DenseNet-BC-40-2 (k=36) 21.65 1,578,412 654.64M Training (log)
WRN-16-10 18.95 17,174,324 2,414.09M Training (log)
WRN-28-10 17.88 36,536,884 5,247.04M Training (log)
WRN-40-8 18.03 35,794,484 5,176.95M Training (log)
WRN-20-10-1bit 19.04 26,794,920 4,022.81M Training (log)
WRN-20-10-32bit 18.12 26,794,920 4,022.81M Training (log)
RoR-3-56 25.49 768,596 113.43M Training (log)
RoR-3-110 23.64 1,643,540 242.08M Training (log)
RoR-3-164 22.34 2,518,484 370.72M Training (log)
RiR 19.23 9,527,720 1,283.29M Training (log)
Shake-Shake-ResNet-20-2x16d 29.22 546,932 81.79M Training (log)
Shake-Shake-ResNet-26-2x32d 18.80 2,934,772 428.90M Training (log)
DIA-ResNet-20 27.71 292,716 41.34M Training (log)
DIA-ResNet-56 24.35 876,012 127.18M Training (log)
DIA-ResNet-110 22.11 1,750,956 255.95M Training (log)
DIA-ResNet-164(BN) 19.53 1,946,132 259.20M Training (log)
DIA-PreResNet-20 28.37 292,524 41.32M Training (log)
DIA-PreResNet-56 25.05 875,820 127.16M Training (log)
DIA-PreResNet-110 22.69 1,750,764 255.92M Training (log)
DIA-PreResNet-164(BN) 19.99 1,945,236 258.97M Training (log)

SVHN

Model Error, % Params FLOPs/2 Remarks
NIN 3.76 966,986 222.97M Training (log)
ResNet-20 3.43 272,474 41.29M Training (log)
ResNet-56 2.75 855,770 127.06M Training (log)
ResNet-110 2.45 1,730,714 255.70M Training (log)
ResNet-164(BN) 2.42 1,704,154 255.31M Training (log)
ResNet-272(BN) 2.43 2,816,986 420.61M Training (log)
ResNet-542(BN) 2.34 5,599,066 833.87M Training (log)
ResNet-1001 2.41 10,328,602 1,536.40M Training (log)
PreResNet-20 3.22 272,282 41.27M Training (log)
PreResNet-56 2.80 855,578 127.03M Training (log)
PreResNet-110 2.79 1,730,522 255.68M Training (log)
PreResNet-164(BN) 2.58 1,703,258 255.08M Training (log)
PreResNet-272(BN) 2.34 2,816,090 420.38M Training (log)
PreResNet-542(BN) 2.36 5,598,170 833.64M Training (log)
ResNeXt-20 (1x64d) 2.98 3,446,602 538.36M Training (log)
ResNeXt-20 (2x32d) 2.96 2,672,458 425.15M Training (log)
ResNeXt-20 (4x16d) 3.17 2,285,386 368.55M Training (log)
ResNeXt-20 (8x8d) 3.18 2,091,850 340.25M Training (log)
ResNeXt-20 (16x4d) 3.21 1,995,082 326.10M Training (log)
ResNeXt-20 (32x2d) 3.27 1,946,698 319.03M Training (log)
ResNeXt-20 (64x1d) 3.42 1,922,506 315.49M Training (log)
ResNeXt-20 (2x64d) 2.83 6,198,602 987.98M Training (log)
ResNeXt-20 (4x32d) 2.98 4,650,314 761.57M Training (log)
ResNeXt-20 (8x16d) 3.01 3,876,170 648.37M Training (log)
ResNeXt-20 (16x8d) 2.93 3,489,098 591.77M Training (log)
ResNeXt-20 (32x4d) 3.09 3,295,562 563.47M Training (log)
ResNeXt-20 (64x2d) 3.14 3,198,794 549.32M Training (log)
ResNeXt-56 (1x64d) 2.42 9,317,194 1,399.33M Training (log)
ResNeXt-56 (2x32d) 2.46 6,994,762 1,059.72M Training (log)
ResNeXt-56 (4x16d) 2.44 5,833,546 889.91M Training (log)
ResNeXt-56 (8x8d) 2.47 5,252,938 805.01M Training (log)
ResNeXt-56 (16x4d) 2.56 4,962,634 762.56M Training (log)
ResNeXt-56 (32x2d) 2.53 4,817,482 741.34M Training (log)
ResNeXt-56 (64x1d) 2.55 4,744,906 730.72M Training (log)
ResNeXt-29 (32x4d) 2.80 4,775,754 780.55M Training (log)
ResNeXt-29 (16x64d) 2.68 68,155,210 10,709.34M Training (log)
ResNeXt-272 (1x64d) 2.35 44,540,746 6,565.15M Training (log)
ResNeXt-272 (2x32d) 2.44 32,928,586 4,867.11M Training (log)
SE-ResNet-20 3.23 274,847 41.30M Training (log)
SE-ResNet-56 2.64 862,889 127.07M Training (log)
SE-ResNet-110 2.35 1,744,952 255.72M Training (log)
SE-ResNet-164(BN) 2.45 1,906,258 255.52M Training (log)
SE-ResNet-272(BN) 2.38 3,153,826 420.96M Training (log)
SE-ResNet-542(BN) 2.26 6,272,746 834.57M Training (log)
SE-PreResNet-20 3.24 274,559 41.30M Training (log)
SE-PreResNet-56 2.71 862,601 127.07M Training (log)
SE-PreResNet-110 2.59 1,744,664 255.72M Training (log)
SE-PreResNet-164(BN) 2.56 1,904,882 255.29M Training (log)
SE-PreResNet-272(BN) 2.49 3,152,450 420.73M Training (log)
SE-PreResNet-542(BN) 2.47 6,271,370 834.34M Training (log)
PyramidNet-110 (a=48) 2.47 1,772,706 408.37M Training (log)
PyramidNet-110 (a=84) 2.43 3,904,446 778.15M Training (log)
PyramidNet-110 (a=270) 2.38 28,485,477 4,730.60M Training (log)
PyramidNet-164 (a=270, BN) 2.33 27,216,021 4,608.81M Training (log)
PyramidNet-200 (a=240, BN) 2.32 26,752,702 4,563.40M Training (log)
PyramidNet-236 (a=220, BN) 2.35 26,969,046 4,631.32M Training (log)
PyramidNet-272 (a=200, BN) 2.40 26,210,842 4,541.36M Training (log)
DenseNet-40 (k=12) 3.05 599,050 210.80M Training (log)
DenseNet-BC-40 (k=12) 3.20 176,122 74.89M Training (log)
DenseNet-BC-40 (k=24) 2.90 690,346 293.09M Training (log)
DenseNet-BC-40 (k=36) 2.60 1,542,682 654.60M Training (log)
DenseNet-100 (k=12) 2.60 4,068,490 1,353.55M Training (log)
X-DenseNet-BC-40-2 (k=24) 2.87 690,346 293.09M Training (log)
X-DenseNet-BC-40-2 (k=36) 2.74 1,542,682 654.60M Training (log)
WRN-16-10 2.78 17,116,634 2,414.04M Training (log)
WRN-28-10 2.71 36,479,194 5,246.98M Training (log)
WRN-40-8 2.54 35,748,314 5,176.90M Training (log)
WRN-20-10-1bit 2.73 26,737,140 4,019.14M Training (log)
WRN-20-10-32bit 2.59 26,737,140 4,019.14M Training (log)
RoR-3-56 2.69 762,746 113.43M Training (log)
RoR-3-110 2.57 1,637,690 242.07M Training (log)
RoR-3-164 2.73 2,512,634 370.72M Training (log)
RiR 2.68 9,492,980 1,281.08M Training (log)
Shake-Shake-ResNet-20-2x16d 3.17 541,082 81.78M Training (log)
Shake-Shake-ResNet-26-2x32d 2.62 2,923,162 428.89M Training (log)
DIA-ResNet-20 3.23 286,866 41.34M Training (log)
DIA-ResNet-56 2.68 870,162 127.18M Training (log)
DIA-ResNet-110 2.47 1,745,106 255.94M Training (log)
DIA-ResNet-164(BN) 2.44 1,923,002 259.18M Training (log)
DIA-PreResNet-20 3.03 286,674 41.31M Training (log)
DIA-PreResNet-56 2.80 869,970 127.15M Training (log)
DIA-PreResNet-110 2.42 1,744,914 255.92M Training (log)
DIA-PreResNet-164(BN) 2.56 1,922,106 258.95M Training (log)

CUB-200-2011

Model Error, % Params FLOPs/2 Remarks
ResNet-10 27.65 5,008,392 893.63M Training (log)
ResNet-12 26.58 5,082,376 1,125.84M Training (log)
ResNet-14 24.35 5,377,800 1,357.53M Training (log)
ResNet-16 23.21 6,558,472 1,588.93M Training (log)
ResNet-18 23.30 11,279,112 1,820.00M Training (log)
ResNet-26 22.52 17,549,832 2,746.38M Training (log)
SE-ResNet-10 27.39 5,052,932 893.67M Training (log)
SE-ResNet-12 26.04 5,127,496 1,125.88M Training (log)
SE-ResNet-14 23.63 5,425,104 1,357.58M Training (log)
SE-ResNet-16 23.21 6,614,240 1,588.99M Training (log)
SE-ResNet-18 23.08 11,368,192 1,820.10M Training (log)
SE-ResNet-26 22.51 17,683,452 2,746.52M Training (log)
MobileNet x1.0 23.46 3,411,976 578.98M Training (log)
ProxylessNAS Mobile 21.88 3,055,712 331.44M Training (log)
NTS-Net 13.26 28,623,333 33,361.39M From yangze0930/NTS-Net (log)

Pascal VOC20102

Model Extractor Pix.Acc.,% mIoU,% Params FLOPs/2 Remarks
PSPNet ResNet(D)-101b 98.09 81.44 65,708,501 230,586.69M From dmlc/gluon-cv (log)
DeepLabv3 ResNet(D)-101b 97.95 80.24 58,754,773 47,624.54M From dmlc/gluon-cv (log)
DeepLabv3 ResNet(D)-152b 98.11 81.20 74,398,421 59,894.06M From dmlc/gluon-cv (log)
FCN-8s(d) ResNet(D)-101b 97.80 80.40 52,072,917 196,562.96M From dmlc/gluon-cv (log)

ADE20K

Model Extractor Pix.Acc.,% mIoU,% Params FLOPs/2 Remarks
PSPNet ResNet(D)-50b 79.37 36.87 46,782,550 162,410.82M From dmlc/gluon-cv (log)
PSPNet ResNet(D)-101b 79.93 37.97 65,774,678 230,824.47M From dmlc/gluon-cv (log)
DeepLabv3 ResNet(D)-50b 79.72 37.13 39,795,798 32,755.38M From dmlc/gluon-cv (log)
DeepLabv3 ResNet(D)-101b 80.21 37.84 58,787,926 47,650.43M From dmlc/gluon-cv (log)
FCN-8s(d) ResNet(D)-50b 76.92 33.39 33,146,966 128,387.08M From dmlc/gluon-cv (log)
FCN-8s(d) ResNet(D)-101b 79.01 35.88 52,139,094 196,800.73M From dmlc/gluon-cv (log)

Cityscapes

Model Extractor Pix.Acc.,% mIoU,% Params FLOPs/2 Remarks
PSPNet ResNet(D)-101b 96.17 71.72 65,707,475 230,583.01M From dmlc/gluon-cv (log)
SINet - 93.66 60.31 119,418 1,411.97M From clovaai/c3_sinet (log)

COCO

Model Extractor Pix.Acc.,% mIoU,% Params FLOPs/2 Remarks
PSPNet ResNet(D)-101b 92.05 67.41 65,708,501 230,586.69M From dmlc/gluon-cv (log)
DeepLabv3 ResNet(D)-101b 92.19 67.73 58,754,773 47,624.54M From dmlc/gluon-cv (log)
DeepLabv3 ResNet(D)-152b 92.24 68.99 74,398,421 275,084.22M From dmlc/gluon-cv (log)
FCN-8s(d) ResNet(D)-101b 91.44 60.11 52,072,917 196,562.96M From dmlc/gluon-cv (log)

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