Image classification models for Gluon
Project description
Image classification models on MXNet/Gluon
This is a collection of image classification models. Many of them are pretrained on ImageNet-1K and CIFAR-10/100
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
- AlexNet ('One weird trick for parallelizing convolutional neural networks')
- ZFNet ('Visualizing and Understanding Convolutional Networks')
- NIN ('Network In Network')
- VGG/BN-VGG ('Very Deep Convolutional Networks for Large-Scale Image Recognition')
- BN-Inception ('Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift')
- ResNet ('Deep Residual Learning for Image Recognition')
- PreResNet ('Identity Mappings in Deep Residual Networks')
- ResNeXt ('Aggregated Residual Transformations for Deep Neural Networks')
- SENet/SE-ResNet/SE-PreResNet/SE-ResNeXt ('Squeeze-and-Excitation Networks')
- IBN-ResNet/IBN-ResNeXt/IBN-DenseNet ('Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net')
- AirNet/AirNeXt ('Attention Inspiring Receptive-Fields Network for Learning Invariant Representations')
- BAM-ResNet ('BAM: Bottleneck Attention Module')
- CBAM-ResNet ('CBAM: Convolutional Block Attention Module')
- ResAttNet ('Residual Attention Network for Image Classification')
- PyramidNet ('Deep Pyramidal Residual Networks')
- DiracNetV2 ('DiracNets: Training Very Deep Neural Networks Without Skip-Connections')
- CRU-Net ('Sharing Residual Units Through Collective Tensor Factorization To Improve Deep Neural Networks')
- DenseNet ('Densely Connected Convolutional Networks')
- CondenseNet ('CondenseNet: An Efficient DenseNet using Learned Group Convolutions')
- SparseNet ('Sparsely Aggregated Convolutional Networks')
- PeleeNet ('Pelee: A Real-Time Object Detection System on Mobile Devices')
- WRN ('Wide Residual Networks')
- DRN-C/DRN-D ('Dilated Residual Networks')
- DPN ('Dual Path Networks')
- DarkNet Ref/Tiny/19 ('Darknet: Open source neural networks in c')
- DarkNet-53 ('YOLOv3: An Incremental Improvement')
- ChannelNet ('ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions')
- MSDNet ('Multi-Scale Dense Networks for Resource Efficient Image Classification')
- FishNet ('FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction')
- SqueezeNet/SqueezeResNet ('SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size')
- SqueezeNext ('SqueezeNext: Hardware-Aware Neural Network Design')
- ShuffleNet ('ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices')
- ShuffleNetV2 ('ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design')
- MENet ('Merging and Evolution: Improving Convolutional Neural Networks for Mobile Applications')
- MobileNet ('MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications')
- FD-MobileNet ('FD-MobileNet: Improved MobileNet with A Fast Downsampling Strategy')
- MobileNetV2 ('MobileNetV2: Inverted Residuals and Linear Bottlenecks')
- IGCV3 ('IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks')
- MnasNet ('MnasNet: Platform-Aware Neural Architecture Search for Mobile')
- DARTS ('DARTS: Differentiable Architecture Search')
- Xception ('Xception: Deep Learning with Depthwise Separable Convolutions')
- InceptionV3 ('Rethinking the Inception Architecture for Computer Vision')
- InceptionV4/InceptionResNetV2 ('Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning')
- PolyNet ('PolyNet: A Pursuit of Structural Diversity in Very Deep Networks')
- NASNet ('Learning Transferable Architectures for Scalable Image Recognition')
- PNASNet ('Progressive Neural Architecture Search')
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 the 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/ShuffleNetV2b/ShuffleNetV2c are different implementations of the same architecture.
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-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 | 37.09 | 15.55 | 5,418,792 | 894.04M | Training (log) |
ResNet-12 | 35.86 | 14.46 | 5,492,776 | 1,126.25M | Training (log) |
ResNet-14 | 32.85 | 12.41 | 5,788,200 | 1,357.94M | Training (log) |
ResNet-16 | 30.68 | 11.10 | 6,968,872 | 1,589.34M | Training (log) |
ResNet-18 x0.25 | 49.16 | 24.45 | 831,096 | 137.32M | Training (log) |
ResNet-18 x0.5 | 36.54 | 14.96 | 3,055,880 | 486.49M | Training (log) |
ResNet-18 x0.75 | 33.25 | 12.54 | 6,675,352 | 1,047.53M | Training (log) |
ResNet-18 | 28.09 | 9.51 | 11,689,512 | 1,820.41M | Training (log) |
ResNet-34 | 25.34 | 7.92 | 21,797,672 | 3,672.68M | From dmlc/gluon-cv (log) |
ResNet-50 | 22.65 | 6.41 | 25,557,032 | 3,877.95M | From dmlc/gluon-cv (log) |
ResNet-50b | 22.32 | 6.18 | 25,557,032 | 4,110.48M | From dmlc/gluon-cv (log) |
ResNet-101 | 21.66 | 5.99 | 44,549,160 | 7,597.95M | From dmlc/gluon-cv (log) |
ResNet-101b | 20.79 | 5.39 | 44,549,160 | 7,830.48M | From dmlc/gluon-cv (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-18 | 28.16 | 9.51 | 11,687,848 | 1,820.56M | Training (log) |
PreResNet-34 | 25.88 | 8.11 | 21,796,008 | 3,672.83M | From dmlc/gluon-cv (log) |
PreResNet-50 | 23.39 | 6.68 | 25,549,480 | 3,875.44M | From dmlc/gluon-cv (log) |
PreResNet-50b | 23.16 | 6.64 | 25,549,480 | 4,107.97M | From dmlc/gluon-cv (log) |
PreResNet-101 | 21.45 | 5.75 | 44,541,608 | 7,595.44M | From dmlc/gluon-cv (log) |
PreResNet-101b | 21.73 | 5.88 | 44,541,608 | 7,827.97M | From dmlc/gluon-cv (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) |
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-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-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 | 25.11 | 7.80 | 7,978,856 | 2,872.13M | From dmlc/gluon-cv (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 | 23.57 | 7.00 | 12,611,602 | 2,351.84M | From Cadene/pretrained...pytorch (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) |
FishNet-150 | 22.85 | 6.38 | 24,959,400 | 6,435.02M | From kevin-ssy/FishNet (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) |
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) |
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 | 32.38 | 12.37 | 4,406,098 | 320.77M | Training (log) |
ShuffleNetV2 x2.0 | 32.04 | 12.10 | 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) |
ShuffleNetV2c x0.5 | 39.87 | 18.11 | 1,366,792 | 43.31M | From tensorpack/tensorpack (log) |
ShuffleNetV2c x1.0 | 30.74 | 11.38 | 2,279,760 | 150.62M | From tensorpack/tensorpack (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) | 29.57 | 10.43 | 5,304,784 | 567.90M | From clavichord93/MENet (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 | 30.82 | 11.26 | 2,627,592 | 198.50M | From dmlc/gluon-cv (log) |
MobileNetV2 x1.0 | 28.51 | 9.90 | 3,504,960 | 329.36M | 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 x1.0 | 28.22 | 9.54 | 3,491,688 | 340.79M | From homles11/IGCV3 (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) |
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) |
CIFAR-10
Some remarks:
- Testing subset is used for validation purpose.
Model | Error, % | Params | FLOPs/2 | Remarks |
---|---|---|---|---|
NIN | 7.43 | 966,986 | 222.97M | Training (log) |
ResNet-20 | 5.97 | 272,474 | 41.29M | Training (log) |
ResNet-56 | 4.52 | 855,770 | 127.06M | Training (log) |
ResNet-110 | 3.69 | 1,730,714 | 255.70M | Training (log) |
ResNet-164(BN) | 3.68 | 1,704,154 | 255.31M | Training (log) |
PreResNet-20 | 6.51 | 272,282 | 41.27M | Training (log) |
PreResNet-56 | 4.49 | 855,578 | 127.03M | Training (log) |
PreResNet-110 | 3.86 | 1,730,522 | 255.68M | Training (log) |
PreResNet-164(BN) | 3.64 | 1,703,258 | 255.08M | Training (log) |
ResNeXt-29 (32x4d) | 3.15 | 4,775,754 | 780.55M | Training (log) |
ResNeXt-29 (16x64d) | 2.41 | 68,155,210 | 10,709.34M | Training (log) |
PyramidNet-110 (a=48) | 3.72 | 1,772,706 | 408.37M | Training (log) |
PyramidNet-110 (a=84) | 2.98 | 3,904,446 | 778.15M | Training (log) |
PyramidNet-110 (a=270) | 2.51 | 28,485,477 | 4,730.60M | Training (log) |
DenseNet-40 (k=12) | 5.61 | 599,050 | 210.80M | Training (log) |
DenseNet-BC-100 (k=12) | 4.16 | 769,162 | 298.45M | Training (log) |
WRN-16-10 | 2.93 | 17,116,634 | 2,414.04M | Training (log) |
WRN-28-10 | 2.39 | 36,479,194 | 5,246.98M | Training (log) |
WRN-40-8 | 2.37 | 35,748,314 | 5,176.90M | 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) |
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) |
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) |
DenseNet-40 (k=12) | 24.90 | 622,360 | 210.82M | Training (log) |
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