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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.
Model Top1 Top5 Params FLOPs/2 Remarks
AlexNet 44.12 21.26 61,100,840 714.83M From dmlc/gluon-cv (log)
VGG-11 31.91 11.76 132,863,336 7,615.87M From dmlc/gluon-cv (log)
VGG-13 31.06 11.12 133,047,848 11,317.65M From dmlc/gluon-cv (log)
VGG-16 26.78 8.69 138,357,544 15,480.10M From dmlc/gluon-cv (log)
VGG-19 25.88 8.23 143,667,240 19,642.55M From dmlc/gluon-cv (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 30.34 10.57 132,868,840 7,630.72M From dmlc/gluon-cv (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 25.09 7.76 11,295,240 2,048.06M From Cadene/pretrained...pytorch (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 20.31 5.25 60,192,808 11,554.38M From dmlc/gluon-cv (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 21.00 5.75 60,185,256 11,551.87M From dmlc/gluon-cv (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 (32x4d) 29.95 11.10 9,411,880 1,603.46M Training (log)
ResNeXt-26 (32x4d) 23.93 7.21 15,389,480 2,488.07M Training (log)
ResNeXt-101 (32x4d) 21.32 5.79 44,177,704 8,003.45M From Cadene/pretrained...pytorch (log)
ResNeXt-101 (64x4d) 20.60 5.41 83,455,272 15,500.27M From Cadene/pretrained...pytorch (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-50 22.51 6.44 28,088,024 3,880.49M From Cadene/pretrained...pytorch (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-ResNeXt-50 (32x4d) 21.06 5.58 27,559,896 4,258.40M From Cadene/pretrained...pytorch (log)
SE-ResNeXt-101 (32x4d) 19.99 5.00 48,955,416 8,008.26M From Cadene/pretrained...pytorch (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 22.40 6.18 28,681,000 7,793.16M From dmlc/gluon-cv (log)
DenseNet-169 23.89 6.89 14,149,480 3,403.89M From dmlc/gluon-cv (log)
DenseNet-201 22.71 6.36 20,013,928 4,347.15M From dmlc/gluon-cv (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)
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)
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 31.32 11.44 4,308,816 317.67M From zeusees/Mnasnet...Model (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)
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)
EfficientNet-B0b 23.41 6.97 5,288,548 413.13M From rwightman/pyt...models (log)
EfficientNet-B1b 21.57 5.91 7,794,184 606.80M From rwightman/pyt...models (log)
EfficientNet-B2b 20.67 5.28 9,109,994 697.57M From rwightman/pyt...models (log)
EfficientNet-B3b 19.45 4.86 12,233,232 1,013.63M 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-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-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-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)
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)
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-1001 19.79 10,351,732 1,536.43M 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-1001 18.41 10,350,836 1,536.20M 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)
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)
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)
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)
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)
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)
PyramidNet-110 (a=48) 2.47 1,772,706 408.37M 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)

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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