Image classification models for TensorFlow 2.0
Project description
Large-scale image classification models on TensorFlow 2.x
This is a collection of large-scale image classification models. Many of them are pretrained on
ImageNet-1K dataset 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')
- 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')
- PyramidNet ('Deep Pyramidal Residual Networks')
- DiracNetV2 ('DiracNets: Training Very Deep Neural Networks Without Skip-Connections')
- DenseNet ('Densely Connected 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')
- BagNet ('Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet')
- HRNet ('Deep High-Resolution Representation Learning for Visual Recognition')
- SelecSLS ('XNect: Real-time Multi-person 3D Human Pose Estimation with a Single RGB Camera')
- 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')
- MobileNetV3 ('Searching for MobileNetV3')
- IGCV3 ('IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks')
- GhostNet ('GhostNet: More Features from Cheap Operations')
- MnasNet ('MnasNet: Platform-Aware Neural Architecture Search for Mobile')
- ProxylessNAS ('ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware')
- FBNet ('FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural 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')
- SPNASNet ('Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours')
- EfficientNet ('EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks')
- MixNet ('MixConv: Mixed Depthwise Convolutional Kernels')
Installation
To use the models in your project, simply install the tf2cv
package with tensorflow
:
pip install tf2cv tensorflow>=2.0.0
To enable/disable different hardware supports, check out TensorFlow installation instructions.
Usage
Example of using a pretrained ResNet-18 model (with channels_first
data format):
from tf2cv.model_provider import get_model as tf2cv_get_model
import tensorflow as tf
net = tf2cv_get_model("resnet18", pretrained=True, data_format="channels_last")
x = tf.random.normal((1, 224, 224, 3))
y_net = 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.
- Remark
Converted from GL model
means that the model was trained onMXNet/Gluon
and then converted to TensorFlow.
Model | Top1 | Top5 | Params | FLOPs/2 | Remarks |
---|---|---|---|---|---|
AlexNet | 40.50 | 17.89 | 62,378,344 | 1,132.33M | Converted from GL model (log) |
AlexNet-b | 41.03 | 18.59 | 61,100,840 | 714.83M | Converted from GL model (log) |
ZFNet | 395.0 | 17.17 | 62,357,608 | 1,170.33M | Converted from GL model (log) |
ZFNet-b | 36.28 | 14.80 | 107,627,624 | 2,479.13M | Converted from GL model (log) |
VGG-11 | 29.59 | 10.17 | 132,863,336 | 7,615.87M | Converted from GL model (log) |
VGG-13 | 28.41 | 9.51 | 133,047,848 | 11,317.65M | Converted from GL model (log) |
VGG-16 | 26.59 | 8.34 | 138,357,544 | 15,480.10M | Converted from GL model (log) |
VGG-19 | 25.57 | 7.68 | 143,667,240 | 19,642.55M | Converted from GL model (log) |
BN-VGG-11 | 28.57 | 9.36 | 132,866,088 | 7,630.21M | Converted from GL model (log) |
BN-VGG-13 | 27.67 | 8.87 | 133,050,792 | 11,341.62M | Converted from GL model (log) |
BN-VGG-16 | 25.46 | 7.59 | 138,361,768 | 15,506.38M | Converted from GL model (log) |
BN-VGG-19 | 23.89 | 6.88 | 143,672,744 | 19,671.15M | Converted from GL model (log) |
BN-VGG-11b | 29.31 | 9.75 | 132,868,840 | 7,630.72M | Converted from GL model (log) |
BN-VGG-13b | 29.46 | 10.19 | 133,053,736 | 11,342.14M | From dmlc/gluon-cv (log) |
BN-VGG-16b | 26.89 | 8.62 | 138,365,992 | 15,507.20M | From dmlc/gluon-cv (log) |
BN-VGG-19b | 25.64 | 8.17 | 143,678,248 | 19,672.26M | From dmlc/gluon-cv (log) |
BN-Inception | 26.62 | 8.65 | 11,295,240 | 2,048.06M | Converted from GL model (log) |
ResNet-10 | 34.68 | 13.90 | 5,418,792 | 894.04M | Converted from GL model (log) |
ResNet-12 | 33.43 | 13.01 | 5,492,776 | 1,126.25M | Converted from GL model (log) |
ResNet-14 | 32.21 | 12.24 | 5,788,200 | 1,357.94M | Converted from GL model (log) |
ResNet-BC-14b | 30.21 | 11.15 | 10,064,936 | 1,479.12M | Converted from GL model (log) |
ResNet-16 | 30.22 | 10.88 | 6,968,872 | 1,589.34M | Converted from GL model (log) |
ResNet-18 x0.25 | 39.30 | 17.45 | 3,937,400 | 270.94M | Converted from GL model (log) |
ResNet-18 x0.5 | 33.40 | 12.83 | 5,804,296 | 608.70M | Converted from GL model (log) |
ResNet-18 x0.75 | 29.98 | 10.67 | 8,476,056 | 1,129.45M | Converted from GL model (log) |
ResNet-18 | 28.10 | 9.56 | 11,689,512 | 1,820.41M | Converted from GL model (log) |
ResNet-26 | 26.15 | 8.37 | 17,960,232 | 2,746.79M | Converted from GL model (log) |
ResNet-BC-26b | 24.80 | 7.57 | 15,995,176 | 2,356.67M | Converted from GL model (log) |
ResNet-34 | 24.50 | 7.44 | 21,797,672 | 3,672.68M | Converted from GL model (log) |
ResNet-BC-38b | 23.44 | 6.77 | 21,925,416 | 3,234.21M | Converted from GL model (log) |
ResNet-50 | 22.09 | 6.04 | 25,557,032 | 3,877.95M | Converted from GL model (log) |
ResNet-50b | 22.09 | 6.14 | 25,557,032 | 4,110.48M | Converted from GL model (log) |
ResNet-101 | 21.59 | 6.01 | 44,549,160 | 7,597.95M | From dmlc/gluon-cv (log) |
ResNet-101b | 20.25 | 5.11 | 44,549,160 | 7,830.48M | Converted from GL model (log) |
ResNet-152 | 20.72 | 5.34 | 60,192,808 | 11,321.85M | From dmlc/gluon-cv (log) |
ResNet-152b | 19.60 | 4.80 | 60,192,808 | 11,554.38M | Converted from GL model (log) |
PreResNet-10 | 34.71 | 14.02 | 5,417,128 | 894.19M | Converted from GL model (log) |
PreResNet-12 | 33.63 | 13.20 | 5,491,112 | 1,126.40M | Converted from GL model (log) |
PreResNet-14 | 32.29 | 12.24 | 5,786,536 | 1,358.09M | Converted from GL model (log) |
PreResNet-BC-14b | 30.73 | 11.52 | 10,057,384 | 1,476.62M | Converted from GL model (log) |
PreResNet-16 | 30.17 | 10.80 | 6,967,208 | 1,589.49M | Converted from GL model (log) |
PreResNet-18 x0.25 | 39.61 | 17.80 | 3,935,960 | 270.93M | Converted from GL model (log) |
PreResNet-18 x0.5 | 33.70 | 13.14 | 5,802,440 | 608.73M | Converted from GL model (log) |
PreResNet-18 x0.75 | 29.95 | 10.70 | 8,473,784 | 1,129.51M | Converted from GL model (log) |
PreResNet-18 | 28.20 | 9.55 | 11,687,848 | 1,820.56M | Converted from GL model (log) |
PreResNet-26 | 25.98 | 8.37 | 17,958,568 | 2,746.94M | Converted from GL model (log) |
PreResNet-BC-26b | 25.22 | 7.88 | 15,987,624 | 2,354.16M | Converted from GL model (log) |
PreResNet-34 | 24.60 | 7.54 | 21,796,008 | 3,672.83M | Converted from GL model (log) |
PreResNet-BC-38b | 22.70 | 6.36 | 21,917,864 | 3,231.70M | Converted from GL model (log) |
PreResNet-50 | 22.22 | 6.25 | 25,549,480 | 3,875.44M | Converted from GL model (log) |
PreResNet-50b | 22.37 | 6.34 | 25,549,480 | 4,107.97M | Converted from GL model (log) |
PreResNet-101 | 21.47 | 5.73 | 44,541,608 | 7,595.44M | From dmlc/gluon-cv (log) |
PreResNet-101b | 20.86 | 5.39 | 44,541,608 | 7,827.97M | Converted from GL model (log) |
PreResNet-152 | 20.71 | 5.32 | 60,185,256 | 11,319.34M | From dmlc/gluon-cv (log) |
PreResNet-152b | 19.86 | 5.00 | 60,185,256 | 11,551.87M | Converted from GL model (log) |
PreResNet-200b | 21.07 | 5.63 | 64,666,280 | 15,068.63M | From tornadomeet/ResNet (log) |
PreResNet-269b | 20.75 | 5.57 | 102,065,832 | 20,101.11M | From soeaver/mxnet-model (log) |
ResNeXt-14 (16x4d) | 31.69 | 12.22 | 7,127,336 | 1,045.77M | Converted from GL model (log) |
ResNeXt-14 (32x2d) | 32.14 | 12.47 | 7,029,416 | 1,031.32M | Converted from GL model (log) |
ResNeXt-14 (32x4d) | 29.94 | 11.15 | 9,411,880 | 1,603.46M | Converted from GL model (log) |
ResNeXt-26 (32x2d) | 26.32 | 8.51 | 9,924,136 | 1,461.06M | Converted from GL model (log) |
ResNeXt-26 (32x4d) | 23.94 | 7.18 | 15,389,480 | 2,488.07M | Converted from GL model (log) |
ResNeXt-50 (32x4d) | 20.62 | 5.47 | 25,028,904 | 4,255.86M | From dmlc/gluon-cv (log) |
ResNeXt-101 (32x4d) | 19.65 | 4.94 | 44,177,704 | 8,003.45M | From dmlc/gluon-cv (log) |
ResNeXt-101 (64x4d) | 19.31 | 4.84 | 83,455,272 | 15,500.27M | From dmlc/gluon-cv (log) |
SE-ResNet-10 | 33.54 | 13.32 | 5,463,332 | 894.27M | Converted from GL model (log) |
SE-ResNet-18 | 27.97 | 9.21 | 11,778,592 | 1,820.88M | Converted from GL model (log) |
SE-ResNet-26 | 25.42 | 8.07 | 18,093,852 | 2,747.49M | Converted from GL model (log) |
SE-ResNet-BC-26b | 23.39 | 6.84 | 17,395,976 | 2,359.58M | Converted from GL model (log) |
SE-ResNet-BC-38b | 21.43 | 5.75 | 24,026,616 | 3,238.58M | Converted from GL model (log) |
SE-ResNet-50 | 22.52 | 6.42 | 28,088,024 | 3,880.49M | From Cadene/pretrained...pytorch (log) |
SE-ResNet-50b | 20.58 | 5.33 | 28,088,024 | 4,115.78M | Converted from GL model (log) |
SE-ResNet-101 | 21.94 | 5.89 | 49,326,872 | 7,602.76M | From Cadene/pretrained...pytorch (log) |
SE-ResNet-152 | 21.47 | 5.76 | 66,821,848 | 11,328.52M | From Cadene/pretrained...pytorch (log) |
SE-PreResNet-10 | 33.62 | 13.09 | 5,461,668 | 894.42M | Converted from GL model (log) |
SE-PreResNet-18 | 27.70 | 9.40 | 11,776,928 | 1,821.03M | Converted from GL model (log) |
SE-PreResNet-BC-26b | 22.95 | 6.40 | 17,388,424 | 2,357.07M | Converted from GL model (log) |
SE-PreResNet-BC-38b | 21.44 | 5.67 | 24,019,064 | 3,236.07M | Converted from GL model (log) |
SE-ResNeXt-50 (32x4d) | 19.98 | 5.09 | 27,559,896 | 4,261.16M | From dmlc/gluon-cv (log) |
SE-ResNeXt-101 (32x4d) | 19.01 | 4.59 | 48,955,416 | 8,012.73M | From dmlc/gluon-cv (log) |
SE-ResNeXt-101 (64x4d) | 18.96 | 4.65 | 88,232,984 | 15,509.54M | From dmlc/gluon-cv (log) |
SENet-16 | 25.37 | 8.05 | 31,366,168 | 5,081.30M | Converted from GL model (log) |
SENet-28 | 21.68 | 5.90 | 36,453,768 | 5,732.71M | Converted from GL model (log) |
SENet-154 | 18.78 | 4.66 | 115,088,984 | 20,745.78M | From Cadene/pretrained...pytorch (log) |
IBN-ResNet-50 | 23.53 | 6.68 | 25,557,032 | 4,110.48M | From XingangPan/IBN-Net (log) |
IBN-ResNet-101 | 21.86 | 5.84 | 44,549,160 | 7,830.48M | From XingangPan/IBN-Net (log) |
IBN(b)-ResNet-50 | 23.88 | 6.95 | 25,558,568 | 4,112.89M | From XingangPan/IBN-Net (log) |
IBN-ResNeXt-101 (32x4d) | 21.41 | 5.64 | 44,177,704 | 8,003.45M | From XingangPan/IBN-Net (log) |
IBN-DenseNet-121 | 24.96 | 7.49 | 7,978,856 | 2,872.13M | From XingangPan/IBN-Net (log) |
IBN-DenseNet-169 | 23.75 | 6.84 | 14,149,480 | 3,403.89M | From XingangPan/IBN-Net (log) |
AirNet50-1x64d (r=2) | 22.54 | 6.23 | 27,425,864 | 4,772.11M | From soeaver/AirNet-PyTorch (log) |
AirNet50-1x64d (r=16) | 22.89 | 6.50 | 25,714,952 | 4,399.97M | From soeaver/AirNet-PyTorch (log) |
AirNeXt50-32x4d (r=2) | 21.47 | 5.72 | 27,604,296 | 5,339.58M | From soeaver/AirNet-PyTorch (log) |
BAM-ResNet-50 | 23.67 | 6.97 | 25,915,099 | 4,196.09M | From Jongchan/attention-module (log) |
CBAM-ResNet-50 | 22.96 | 6.39 | 28,089,624 | 4,116.97M | From Jongchan/attention-module (log) |
PyramidNet-101 (a=360) | 22.68 | 6.51 | 42,455,070 | 8,743.54M | From dyhan0920/Pyramid...PyTorch (log) |
DiracNetV2-18 | 30.59 | 11.13 | 11,511,784 | 1,796.62M | From [szagoruyko/diracnets] (log) |
DiracNetV2-34 | 27.92 | 9.50 | 21,616,232 | 3,646.93M | From [szagoruyko/diracnets] (log) |
DenseNet-121 | 23.23 | 6.84 | 7,978,856 | 2,872.13M | Converted from GL model (log) |
DenseNet-161 | 22.37 | 6.18 | 28,681,000 | 7,793.16M | From dmlc/gluon-cv (log) |
DenseNet-169 | 22.13 | 6.06 | 14,149,480 | 3,403.89M | Converted from GL model (log) |
DenseNet-201 | 21.57 | 5.91 | 20,013,928 | 4,347.15M | Converted from GL model (log) |
PeleeNet | 31.65 | 11.29 | 2,802,248 | 514.87M | Converted from GL model (log) |
WRN-50-2 | 22.10 | 6.14 | 68,849,128 | 11,405.42M | From szagoruyko/functional-zoo (log) |
DRN-C-26 | 25.70 | 7.88 | 21,126,584 | 16,993.90M | From fyu/drn (log) |
DRN-C-42 | 23.74 | 6.93 | 31,234,744 | 25,093.75M | From fyu/drn (log) |
DRN-C-58 | 22.36 | 6.26 | 40,542,008 | 32,489.94M | From fyu/drn (log) |
DRN-D-22 | 26.67 | 8.48 | 16,393,752 | 13,051.33M | From fyu/drn (log) |
DRN-D-38 | 24.52 | 7.37 | 26,501,912 | 21,151.19M | From fyu/drn (log) |
DRN-D-54 | 22.07 | 6.26 | 35,809,176 | 28,547.38M | From fyu/drn (log) |
DRN-D-105 | 21.31 | 5.83 | 54,801,304 | 43,442.43M | From fyu/drn (log) |
DPN-68 | 22.92 | 6.58 | 12,611,602 | 2,351.84M | Converted from GL model (log) |
DPN-98 | 20.24 | 5.28 | 61,570,728 | 11,716.51M | From Cadene/pretrained...pytorch (log) |
DPN-131 | 20.05 | 5.24 | 79,254,504 | 16,076.15M | From Cadene/pretrained...pytorch (log) |
DarkNet Tiny | 40.34 | 17.45 | 1,042,104 | 500.85M | Converted from GL model (log) |
DarkNet Ref | 38.10 | 16.71 | 7,319,416 | 367.59M | Converted from GL model (log) |
DarkNet-53 | 21.41 | 5.58 | 41,609,928 | 7,133.86M | From dmlc/gluon-cv (log) |
BagNet-9 | 59.59 | 35.53 | 15,688,744 | 16,049.19M | From wielandbrendel/bag...models (log) |
BagNet-17 | 44.75 | 21.54 | 16,213,032 | 15,768.77M | From wielandbrendel/bag...models (log) |
BagNet-33 | 36.42 | 14.97 | 18,310,184 | 16,371.52M | From wielandbrendel/bag...models (log) |
DLA-34 | 26.15 | 8.23 | 15,742,104 | 3,071.37M | From ucbdrive/dla (log) |
DLA-46-C | 33.83 | 12.87 | 1,301,400 | 585.45M | Converted from GL model (log) |
DLA-X-46-C | 32.90 | 12.29 | 1,068,440 | 546.72M | Converted from GL model (log) |
DLA-60 | 23.83 | 7.11 | 22,036,632 | 4,255.49M | From ucbdrive/dla (log) |
DLA-X-60 | 22.46 | 6.21 | 17,352,344 | 3,543.68M | From ucbdrive/dla (log) |
DLA-X-60-C | 30.66 | 10.75 | 1,319,832 | 596.06M | Converted from GL model (log) |
DLA-102 | 22.84 | 6.43 | 33,268,888 | 7,190.95M | From ucbdrive/dla (log) |
DLA-X-102 | 21.95 | 6.02 | 26,309,272 | 5,884.94M | From ucbdrive/dla (log) |
DLA-X2-102 | 21.11 | 5.53 | 41,282,200 | 9,340.61M | From ucbdrive/dla (log) |
DLA-169 | 21.97 | 5.90 | 53,389,720 | 11,593.20M | From ucbdrive/dla (log) |
HRNet-W18 Small V1 | 28.43 | 9.74 | 13,187,464 | 1,614.87M | From HRNet/HRNet...ation (log) |
HRNet-W18 Small V2 | 25.72 | 8.05 | 15,597,464 | 2,618.54M | From HRNet/HRNet...ation (log) |
HRNetV2-W18 | 24.02 | 6.86 | 21,299,004 | 4,322.66M | From HRNet/HRNet...ation (log) |
HRNetV2-W30 | 22.31 | 6.06 | 37,712,220 | 8,156.14M | From HRNet/HRNet...ation (log) |
HRNetV2-W32 | 22.32 | 6.07 | 41,232,680 | 8,973.31M | From HRNet/HRNet...ation (log) |
HRNetV2-W40 | 21.71 | 5.73 | 57,557,160 | 12,751.34M | From HRNet/HRNet...ation (log) |
HRNetV2-W44 | 21.74 | 5.95 | 67,064,984 | 14,945.95M | From HRNet/HRNet...ation (log) |
HRNetV2-W48 | 21.42 | 5.81 | 77,469,864 | 17,344.29M | From HRNet/HRNet...ation (log) |
HRNetV2-W64 | 21.10 | 5.53 | 128,059,944 | 28,974.95M | From HRNet/HRNet...ation (log) |
SelecSLS-42b | 23.28 | 6.76 | 32,458,248 | 2,980.62M | From rwightman/pyt...models (log) |
SelecSLS-60 | 22.45 | 6.30 | 30,670,768 | 3,591.78M | From rwightman/pyt...models (log) |
SelecSLS-60b | 21.89 | 6.04 | 32,774,064 | 3,629.14M | From rwightman/pyt...models (log) |
SqueezeNet v1.0 | 39.23 | 17.60 | 1,248,424 | 823.67M | Converted from GL model (log) |
SqueezeNet v1.1 | 39.12 | 17.42 | 1,235,496 | 352.02M | Converted from GL model (log) |
SqueezeResNet v1.0 | 39.38 | 17.83 | 1,248,424 | 823.67M | Converted from GL model (log) |
SqueezeResNet v1.1 | 39.85 | 17.89 | 1,235,496 | 352.02M | Converted from GL model (log) |
1.0-SqNxt-23 | 42.31 | 18.61 | 724,056 | 287.28M | Converted from GL model (log) |
1.0-SqNxt-23v5 | 40.44 | 17.62 | 921,816 | 285.82M | Converted from GL model (log) |
1.5-SqNxt-23 | 34.62 | 13.34 | 1,511,824 | 552.39M | Converted from GL model (log) |
1.5-SqNxt-23v5 | 33.55 | 12.84 | 1,953,616 | 550.97M | Converted from GL model (log) |
2.0-SqNxt-23 | 30.12 | 10.69 | 2,583,752 | 898.48M | Converted from GL model (log) |
2.0-SqNxt-23v5 | 29.40 | 10.26 | 3,366,344 | 897.60M | Converted from GL model (log) |
ShuffleNet x0.25 (g=1) | 62.05 | 36.81 | 209,746 | 12.35M | Converted from GL model (log) |
ShuffleNet x0.25 (g=3) | 61.31 | 36.18 | 305,902 | 13.09M | Converted from GL model (log) |
ShuffleNet x0.5 (g=1) | 46.25 | 22.36 | 534,484 | 41.16M | Converted from GL model (log) |
ShuffleNet x0.5 (g=3) | 43.84 | 20.59 | 718,324 | 41.70M | Converted from GL model (log) |
ShuffleNet x0.75 (g=1) | 39.24 | 16.79 | 975,214 | 86.42M | Converted from GL model (log) |
ShuffleNet x0.75 (g=3) | 37.80 | 16.11 | 1,238,266 | 85.82M | Converted from GL model (log) |
ShuffleNet x1.0 (g=1) | 34.48 | 13.48 | 1,531,936 | 148.13M | Converted from GL model (log) |
ShuffleNet x1.0 (g=2) | 33.95 | 13.33 | 1,733,848 | 147.60M | Converted from GL model (log) |
ShuffleNet x1.0 (g=3) | 33.93 | 13.32 | 1,865,728 | 145.46M | Converted from GL model (log) |
ShuffleNet x1.0 (g=4) | 33.88 | 13.13 | 1,968,344 | 143.33M | Converted from GL model (log) |
ShuffleNet x1.0 (g=8) | 33.71 | 13.22 | 2,434,768 | 150.76M | Converted from GL model (log) |
ShuffleNetV2 x0.5 | 40.75 | 18.43 | 1,366,792 | 43.31M | Converted from GL model (log) |
ShuffleNetV2 x1.0 | 31.00 | 11.35 | 2,278,604 | 149.72M | Converted from GL model (log) |
ShuffleNetV2 x1.5 | 27.41 | 9.23 | 4,406,098 | 320.77M | Converted from GL model (log) |
ShuffleNetV2 x2.0 | 25.83 | 8.21 | 7,601,686 | 595.84M | Converted from GL model (log) |
ShuffleNetV2b x0.5 | 39.80 | 17.84 | 1,366,792 | 43.31M | Converted from GL model (log) |
ShuffleNetV2b x1.0 | 30.36 | 11.04 | 2,279,760 | 150.62M | Converted from GL model (log) |
ShuffleNetV2b x1.5 | 26.90 | 8.77 | 4,410,194 | 323.98M | Converted from GL model (log) |
ShuffleNetV2b x2.0 | 25.24 | 8.08 | 7,611,290 | 603.37M | Converted from GL model (log) |
108-MENet-8x1 (g=3) | 43.64 | 20.39 | 654,516 | 42.68M | Converted from GL model (log) |
128-MENet-8x1 (g=4) | 42.04 | 19.18 | 750,796 | 45.98M | Converted from GL model (log) |
160-MENet-8x1 (g=8) | 43.48 | 20.34 | 850,120 | 45.63M | Converted from GL model (log) |
228-MENet-12x1 (g=3) | 33.80 | 12.91 | 1,806,568 | 152.93M | Converted from GL model (log) |
256-MENet-12x1 (g=4) | 32.28 | 12.17 | 1,888,240 | 150.65M | Converted from GL model (log) |
348-MENet-12x1 (g=3) | 27.81 | 9.37 | 3,368,128 | 312.00M | Converted from GL model (log) |
352-MENet-12x1 (g=8) | 31.33 | 11.67 | 2,272,872 | 157.35M | Converted from GL model (log) |
456-MENet-24x1 (g=3) | 25.02 | 7.79 | 5,304,784 | 567.90M | Converted from GL model (log) |
MobileNet x0.25 | 45.84 | 22.13 | 470,072 | 44.09M | Converted from GL model (log) |
MobileNet x0.5 | 33.86 | 13.33 | 1,331,592 | 155.42M | Converted from GL model (log) |
MobileNet x0.75 | 29.88 | 10.51 | 2,585,560 | 333.99M | Converted from GL model (log) |
MobileNet x1.0 | 26.45 | 8.66 | 4,231,976 | 579.80M | Converted from GL model (log) |
FD-MobileNet x0.25 | 55.42 | 30.62 | 383,160 | 12.95M | Converted from GL model (log) |
FD-MobileNet x0.5 | 42.66 | 19.77 | 993,928 | 41.84M | Converted from GL model (log) |
FD-MobileNet x0.75 | 37.97 | 15.97 | 1,833,304 | 86.68M | Converted from GL model (log) |
FD-MobileNet x1.0 | 33.90 | 13.12 | 2,901,288 | 147.46M | Converted from GL model (log) |
MobileNetV2 x0.25 | 48.10 | 24.13 | 1,516,392 | 34.24M | Converted from GL model (log) |
MobileNetV2 x0.5 | 35.62 | 14.46 | 1,964,736 | 100.13M | Converted from GL model (log) |
MobileNetV2 x0.75 | 29.75 | 10.44 | 2,627,592 | 198.50M | Converted from GL model (log) |
MobileNetV2 x1.0 | 26.80 | 8.63 | 3,504,960 | 329.36M | Converted from GL model (log) |
MobileNetV3 L/224/1.0 | 24.65 | 7.69 | 5,481,752 | 226.80M | From dmlc/gluon-cv (log) |
IGCV3 x0.25 | 53.38 | 28.28 | 1,534,020 | 41.29M | Converted from GL model (log) |
IGCV3 x0.5 | 39.36 | 17.01 | 1,985,528 | 111.12M | Converted from GL model (log) |
IGCV3 x0.75 | 30.74 | 11.00 | 2,638,084 | 210.95M | Converted from GL model (log) |
IGCV3 x1.0 | 27.70 | 8.99 | 3,491,688 | 340.79M | Converted from GL model (log) |
MnasNet-B1 | 25.72 | 8.02 | 4,383,312 | 326.30M | From rwightman/pyt...models (log) |
MnasNet-A1 | 25.02 | 7.56 | 3,887,038 | 326.07M | From rwightman/pyt...models (log) |
ProxylessNAS CPU | 24.77 | 7.51 | 4,361,648 | 459.96M | Converted from GL model (log) |
ProxylessNAS GPU | 24.65 | 7.26 | 7,119,848 | 476.08M | Converted from GL model (log) |
ProxylessNAS Mobile | 25.29 | 7.83 | 4,080,512 | 332.46M | Converted from GL model (log) |
ProxylessNAS Mob-14 | 22.93 | 6.53 | 6,857,568 | 597.10M | Converted from GL model (log) |
FBNet-Cb | 25.44 | 7.84 | 5,572,200 | 399.26M | From rwightman/pyt...models (log) |
Xception | 21.14 | 5.58 | 22,855,952 | 8,403.63M | From Cadene/pretrained...pytorch (log) |
InceptionV3 | 21.11 | 5.63 | 23,834,568 | 5,743.06M | From dmlc/gluon-cv (log) |
InceptionV4 | 20.78 | 5.41 | 42,679,816 | 12,304.93M | From Cadene/pretrained...pytorch (log) |
InceptionResNetV2 | 20.00 | 4.95 | 55,843,464 | 13,188.64M | From Cadene/pretrained...pytorch (log) |
PolyNet | 19.09 | 4.51 | 95,366,600 | 34,821.34M | From Cadene/pretrained...pytorch (log) |
NASNet-A 4@1056 | 25.83 | 8.33 | 5,289,978 | 584.90M | From Cadene/pretrained...pytorch (log) |
NASNet-A 6@4032 | 18.24 | 4.27 | 88,753,150 | 23,976.44M | From Cadene/pretrained...pytorch (log) |
PNASNet-5-Large | 18.02 | 4.27 | 86,057,668 | 25,140.77M | From Cadene/pretrained...pytorch (log) |
SPNASNet | 26.97 | 8.73 | 4,421,616 | 346.73M | From rwightman/pyt...models (log) |
EfficientNet-B0 | 24.49 | 7.25 | 5,288,548 | 413.13M | Converted from GL model (log) |
EfficientNet-B1 | 22.93 | 6.30 | 7,794,184 | 730.44M | Converted from GL model (log) |
EfficientNet-B0b | 23.05 | 6.68 | 5,288,548 | 413.13M | From rwightman/pyt...models (log) |
EfficientNet-B1b | 21.17 | 5.77 | 7,794,184 | 730.44M | From rwightman/pyt...models (log) |
EfficientNet-B2b | 20.22 | 5.30 | 9,109,994 | 1,049.29M | From rwightman/pyt...models (log) |
EfficientNet-B3b | 19.14 | 4.69 | 12,233,232 | 1,923.98M | From rwightman/pyt...models (log) |
EfficientNet-B4b | 17.52 | 3.99 | 19,341,616 | 4,597.56M | From rwightman/pyt...models (log) |
EfficientNet-B5b | 16.43 | 3.43 | 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 | 15.85 | 3.15 | 66,347,960 | 38,949.07M | From rwightman/pyt...models (log) |
MixNet-S | 24.34 | 7.37 | 4,134,606 | 260.26M | From rwightman/pyt...models (log) |
MixNet-M | 23.29 | 6.79 | 5,014,382 | 366.05M | From rwightman/pyt...models (log) |
MixNet-L | 21.57 | 6.01 | 7,329,252 | 590.45M | From rwightman/pyt...models (log) |
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