Large-scale image classification networks for embedded systems
This repository contains several classification models on MXNet/Gluon, PyTorch, Chainer, Keras, and TensorFlow, with scripts for training/validating/converting models. All models are designed for using with ImageNet-1k dataset.
List of 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')
- ResAttNet ('Residual Attention Network for Image Classification')
- PyramidNet ('Deep Pyramidal Residual Networks')
- DiracNetV2 ('DiracNets: Training Very Deep Neural Networks Without Skip-Connections')
- 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')
- 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
For Gluon way
To use only Gluon 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
For PyTorch way
To use only PyTorch models in your project, simply install the pytorchcv package with torch (>=0.4.1 is recommended):
pip install pytorchcv torch>=0.4.0
To enable/disable different hardware supports such as GPUs, check out PyTorch installation instructions.
For Chainer way
To use only Chainer models in your project, simply install the chainercv2 package:
pip install chainercv2
For Keras way
To use only Keras models in your project, simply install the kerascv package with mxnet:
pip install kerascv 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 kerascv mxnet-cu92>=1.2.1
After installation change the value of the field image_data_format to channels_first in the file ~/.keras/keras.json.
For TensorFlow way
To use only TensorFlow models in your project, simply install the tensorflowcv package with tensorflow-gpu:
pip install tensorflowcv tensorflow-gpu>=1.11.0
To enable/disable different hardware supports, check out TensorFlow installation instructions.
Note that the models use NCHW data format. The current version of TensorFlow cannot work with them on CPU.
For research
To use the repository for training/validation/converting models:
git clone git@github.com:osmr/imgclsmob.git
pip install -r requirements.txt
Usage
For Gluon way
Example of using the pretrained ResNet-18 model on Gluon:
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)
For PyTorch way
Example of using the pretrained ResNet-18 model on PyTorch:
from pytorchcv.model_provider import get_model as ptcv_get_model
import torch
from torch.autograd import Variable
net = ptcv_get_model("resnet18", pretrained=True)
x = Variable(torch.randn(1, 3, 224, 224))
y = net(x)
For Chainer way
Example of using the pretrained ResNet-18 model on Chainer:
from chainercv2.model_provider import get_model as chcv2_get_model
import numpy as np
net = chcv2_get_model("resnet18", pretrained=True)
x = np.zeros((1, 3, 224, 224), np.float32)
y = net(x)
For Keras way
Example of using the pretrained ResNet-18 model on Keras:
from kerascv.model_provider import get_model as kecv_get_model
import numpy as np
net = kecv_get_model("resnet18", pretrained=True)
x = np.zeros((1, 3, 224, 224), np.float32)
y = net.predict(x)
For TensorFlow way
Example of using the pretrained ResNet-18 model on TensorFlow:
from tensorflowcv.model_provider import get_model as tfcv_get_model
from tensorflowcv.model_provider import init_variables_from_state_dict as tfcv_init_variables_from_state_dict
import tensorflow as tf
import numpy as np
net = tfcv_get_model("resnet18", pretrained=True)
x = tf.placeholder(dtype=tf.float32, shape=(None, 3, 224, 224), name='xx')
y_net = net(x)
with tf.Session() as sess:
tfcv_init_variables_from_state_dict(sess=sess, state_dict=net.state_dict)
x_value = np.zeros((1, 3, 224, 224), np.float32)
y = sess.run(y_net, feed_dict={x: x_value})
Pretrained models
Some remarks:
- All pretrained models can be downloaded automatically during use (use the parameter
pretrained). - Top1/Top5 are the standard 1-crop Top-1/Top-5 errors (in percents) on the validation subset of the ImageNet1k dataset.
- 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.
- All models require the same ordinary normalization.
- FLOPs/2 is the number of FLOPs divided by two to be similar to the number of MACs.
For Gluon
| 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) |
| 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) |
| 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 v1.1 | 39.83 | 17.84 | 1,235,496 | 352.02M | Training (log) |
| 1.0-SqNxt-23 | 45.78 | 21.55 | 724,056 | 287.28M | 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) |
| 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) | 31.17 | 11.41 | 3,368,128 | 312.00M | From clavichord93/MENet (log) |
| 352-MENet-12x1 (g=8) | 34.70 | 13.75 | 2,272,872 | 157.35M | From clavichord93/MENet (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 | 56.19 | 31.38 | 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 | 35.51 | 14.64 | 1,964,736 | 100.13M | From dmlc/gluon-cv (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) |
For PyTorch
| Model | Top1 | Top5 | Params | FLOPs/2 | Remarks |
|---|---|---|---|---|---|
| AlexNet | 43.48 | 20.93 | 61,100,840 | 714.83M | From dmlc/gluon-cv (log) |
| VGG-11 | 30.98 | 11.37 | 132,863,336 | 7,615.87M | From dmlc/gluon-cv (log) |
| VGG-13 | 30.07 | 10.75 | 133,047,848 | 11,317.65M | From dmlc/gluon-cv (log) |
| VGG-16 | 27.15 | 8.92 | 138,357,544 | 15,480.10M | From dmlc/gluon-cv (log) |
| VGG-19 | 26.19 | 8.39 | 143,667,240 | 19,642.55M | From dmlc/gluon-cv (log) |
| BN-VGG-11b | 29.63 | 10.19 | 132,868,840 | 7,630.72M | From dmlc/gluon-cv (log) |
| BN-VGG-13b | 28.41 | 9.63 | 133,053,736 | 11,342.14M | From dmlc/gluon-cv (log) |
| BN-VGG-16b | 27.19 | 8.74 | 138,365,992 | 15,507.20M | From dmlc/gluon-cv (log) |
| BN-VGG-19b | 26.06 | 8.40 | 143,678,248 | 19,672.26M | From dmlc/gluon-cv (log) |
| BN-Inception | 25.39 | 8.04 | 11,295,240 | 2,048.06M | From Cadene/pretrained...pytorch (log) |
| ResNet-10 | 37.46 | 15.85 | 5,418,792 | 894.04M | Converted from GL model (log) |
| ResNet-12 | 36.18 | 14.80 | 5,492,776 | 1,126.25M | Converted from GL model (log) |
| ResNet-14 | 33.17 | 12.71 | 5,788,200 | 1,357.94M | Converted from GL model (log) |
| ResNet-16 | 30.90 | 11.38 | 6,968,872 | 1,589.34M | Converted from GL model (log) |
| ResNet-18 x0.25 | 49.50 | 24.83 | 831,096 | 137.32M | Converted from GL model (log) |
| ResNet-18 x0.5 | 37.04 | 15.38 | 3,055,880 | 486.49M | Converted from GL model (log) |
| ResNet-18 x0.75 | 33.61 | 12.85 | 6,675,352 | 1,047.53M | Converted from GL model (log) |
| ResNet-18 | 28.53 | 9.82 | 11,689,512 | 1,820.41M | Converted from GL model (log) |
| ResNet-34 | 25.66 | 8.18 | 21,797,672 | 3,672.68M | From dmlc/gluon-cv (log) |
| ResNet-50 | 22.96 | 6.58 | 25,557,032 | 3,877.95M | From dmlc/gluon-cv (log) |
| ResNet-50b | 22.61 | 6.45 | 25,557,032 | 4,110.48M | From dmlc/gluon-cv (log) |
| ResNet-101 | 21.90 | 6.22 | 44,549,160 | 7,597.95M | From dmlc/gluon-cv (log) |
| ResNet-101b | 20.88 | 5.61 | 44,549,160 | 7,830.48M | From dmlc/gluon-cv (log) |
| ResNet-152 | 21.01 | 5.50 | 60,192,808 | 11,321.85M | From dmlc/gluon-cv (log) |
| ResNet-152b | 20.56 | 5.34 | 60,192,808 | 11,554.38M | From dmlc/gluon-cv (log) |
| PreResNet-18 | 28.43 | 9.72 | 11,687,848 | 1,820.56M | Converted from GL model (log) |
| PreResNet-34 | 26.23 | 8.41 | 21,796,008 | 3,672.83M | From dmlc/gluon-cv (log) |
| PreResNet-50 | 23.70 | 6.85 | 25,549,480 | 3,875.44M | From dmlc/gluon-cv (log) |
| PreResNet-50b | 23.33 | 6.87 | 25,549,480 | 4,107.97M | From dmlc/gluon-cv (log) |
| PreResNet-101 | 21.74 | 5.91 | 44,541,608 | 7,595.44M | From dmlc/gluon-cv (log) |
| PreResNet-101b | 21.95 | 6.03 | 44,541,608 | 7,827.97M | From dmlc/gluon-cv (log) |
| PreResNet-152 | 20.94 | 5.55 | 60,185,256 | 11,319.34M | From dmlc/gluon-cv (log) |
| PreResNet-152b | 21.34 | 5.91 | 60,185,256 | 11,551.87M | From dmlc/gluon-cv (log) |
| PreResNet-200b | 21.33 | 5.88 | 64,666,280 | 15,068.63M | From tornadomeet/ResNet (log) |
| ResNeXt-101 (32x4d) | 21.81 | 6.11 | 44,177,704 | 8,003.45M | From Cadene/pretrained...pytorch (log) |
| ResNeXt-101 (64x4d) | 21.04 | 5.75 | 83,455,272 | 15,500.27M | From Cadene/pretrained...pytorch (log) |
| SE-ResNet-50 | 22.47 | 6.40 | 28,088,024 | 3,880.49M | From Cadene/pretrained...pytorch (log) |
| SE-ResNet-101 | 21.88 | 5.89 | 49,326,872 | 7,602.76M | From Cadene/pretrained...pytorch (log) |
| SE-ResNet-152 | 21.48 | 5.76 | 66,821,848 | 11,328.52M | From Cadene/pretrained...pytorch (log) |
| SE-ResNeXt-50 (32x4d) | 21.00 | 5.54 | 27,559,896 | 4,258.40M | From Cadene/pretrained...pytorch (log) |
| SE-ResNeXt-101 (32x4d) | 19.96 | 5.05 | 48,955,416 | 8,008.26M | From Cadene/pretrained...pytorch (log) |
| SENet-154 | 18.62 | 4.61 | 115,088,984 | 20,745.78M | From Cadene/pretrained...pytorch (log) |
| IBN-ResNet-50 | 22.76 | 6.41 | 25,557,032 | 4,110.48M | From XingangPan/IBN-Net (log) |
| IBN-ResNet-101 | 21.29 | 5.61 | 44,549,160 | 7,830.48M | From XingangPan/IBN-Net (log) |
| IBN(b)-ResNet-50 | 23.64 | 6.86 | 25,558,568 | 4,112.89M | From XingangPan/IBN-Net (log) |
| IBN-ResNeXt-101 (32x4d) | 20.88 | 5.42 | 44,177,704 | 8,003.45M | From XingangPan/IBN-Net (log) |
| IBN-DenseNet-121 | 24.47 | 7.25 | 7,978,856 | 2,872.13M | From XingangPan/IBN-Net (log) |
| IBN-DenseNet-169 | 23.25 | 6.51 | 14,149,480 | 3,403.89M | From XingangPan/IBN-Net (log) |
| AirNet50-1x64d (r=2) | 21.84 | 5.90 | 27,425,864 | 4,772.11M | From soeaver/AirNet-PyTorch (log) |
| AirNet50-1x64d (r=16) | 22.11 | 6.19 | 25,714,952 | 4,399.97M | From soeaver/AirNet-PyTorch (log) |
| AirNeXt50-32x4d (r=2) | 20.87 | 5.51 | 27,604,296 | 5,339.58M | From soeaver/AirNet-PyTorch (log) |
| BAM-ResNet-50 | 23.14 | 6.58 | 25,915,099 | 4,196.09M | From Jongchan/attention-module (log) |
| CBAM-ResNet-50 | 22.38 | 6.05 | 28,089,624 | 4,116.97M | From Jongchan/attention-module (log) |
| PyramidNet-101 (a=360) | 21.98 | 6.20 | 42,455,070 | 8,743.54M | From dyhan0920/Pyramid...PyTorch (log) |
| DiracNetV2-18 | 31.47 | 11.70 | 11,511,784 | 1,796.62M | From szagoruyko/diracnets (log) |
| DiracNetV2-34 | 28.75 | 9.93 | 21,616,232 | 3,646.93M | From szagoruyko/diracnets (log) |
| DenseNet-121 | 25.57 | 8.03 | 7,978,856 | 2,872.13M | From dmlc/gluon-cv (log) |
| DenseNet-161 | 22.86 | 6.44 | 28,681,000 | 7,793.16M | From dmlc/gluon-cv (log) |
| DenseNet-169 | 24.40 | 7.19 | 14,149,480 | 3,403.89M | From dmlc/gluon-cv (log) |
| DenseNet-201 | 23.10 | 6.63 | 20,013,928 | 4,347.15M | From dmlc/gluon-cv (log) |
| CondenseNet-74 (C=G=4) | 26.25 | 8.28 | 4,773,944 | 546.06M | From ShichenLiu/CondenseNet (log) |
| CondenseNet-74 (C=G=8) | 28.93 | 10.06 | 2,935,416 | 291.52M | From ShichenLiu/CondenseNet (log) |
| PeleeNet | 31.81 | 11.51 | 2,802,248 | 514.87M | Converted from GL model (log) |
| WRN-50-2 | 22.53 | 6.41 | 68,849,128 | 11,405.42M | From szagoruyko/functional-zoo (log) |
| DRN-C-26 | 24.86 | 7.55 | 21,126,584 | 16,993.90M | From fyu/drn (log) |
| DRN-C-42 | 22.94 | 6.57 | 31,234,744 | 25,093.75M | From fyu/drn (log) |
| DRN-C-58 | 21.73 | 6.01 | 40,542,008 | 32,489.94M | From fyu/drn (log) |
| DRN-D-22 | 25.80 | 8.23 | 16,393,752 | 13,051.33M | From fyu/drn (log) |
| DRN-D-38 | 23.79 | 6.95 | 26,501,912 | 21,151.19M | From fyu/drn (log) |
| DRN-D-54 | 21.22 | 5.86 | 35,809,176 | 28,547.38M | From fyu/drn (log) |
| DRN-D-105 | 20.62 | 5.48 | 54,801,304 | 43,442.43M | From fyu/drn (log) |
| DPN-68 | 24.17 | 7.27 | 12,611,602 | 2,351.84M | From Cadene/pretrained...pytorch (log) |
| DPN-98 | 20.81 | 5.53 | 61,570,728 | 11,716.51M | From Cadene/pretrained...pytorch (log) |
| DPN-131 | 20.54 | 5.48 | 79,254,504 | 16,076.15M | From Cadene/pretrained...pytorch (log) |
| DarkNet Tiny | 40.74 | 17.84 | 1,042,104 | 500.85M | Converted from GL model (log) |
| DarkNet Ref | 38.58 | 17.18 | 7,319,416 | 367.59M | Converted from GL model (log) |
| DarkNet-53 | 21.75 | 5.64 | 41,609,928 | 7,133.86M | From dmlc/gluon-cv (log) |
| SqueezeNet v1.0 | 39.29 | 17.66 | 1,248,424 | 823.67M | Converted from GL model (log) |
| SqueezeNet v1.1 | 39.31 | 17.72 | 1,235,496 | 352.02M | Converted from GL model (log) |
| SqueezeResNet v1.1 | 40.09 | 18.21 | 1,235,496 | 352.02M | Converted from GL model (log) |
| 1.0-SqNxt-23 | 46.33 | 21.96 | 724,056 | 287.28M | Converted from GL model (log) |
| ShuffleNet x0.25 (g=1) | 62.44 | 37.29 | 209,746 | 12.35M | Converted from GL model (log) |
| ShuffleNet x0.25 (g=3) | 61.74 | 36.53 | 305,902 | 13.09M | Converted from GL model (log) |
| ShuffleNetV2 x0.5 | 40.99 | 18.65 | 1,366,792 | 43.31M | Converted from GL model (log) |
| ShuffleNetV2 x1.0 | 31.44 | 11.63 | 2,278,604 | 149.72M | Converted from GL model (log) |
| ShuffleNetV2 x1.5 | 32.82 | 12.69 | 4,406,098 | 320.77M | Converted from GL model (log) |
| ShuffleNetV2 x2.0 | 32.45 | 12.49 | 7,601,686 | 595.84M | Converted from GL model (log) |
| ShuffleNetV2b x0.5 | 40.29 | 18.22 | 1,366,792 | 43.31M | Converted from GL model (log) |
| ShuffleNetV2b x1.0 | 30.62 | 11.25 | 2,279,760 | 150.62M | Converted from GL model (log) |
| ShuffleNetV2c x0.5 | 40.31 | 18.51 | 1,366,792 | 43.31M | From tensorpack/tensorpack (log) |
| ShuffleNetV2c x1.0 | 30.98 | 11.61 | 2,279,760 | 150.62M | From tensorpack/tensorpack (log) |
| 108-MENet-8x1 (g=3) | 43.94 | 20.76 | 654,516 | 42.68M | Converted from GL model (log) |
| 128-MENet-8x1 (g=4) | 42.43 | 19.59 | 750,796 | 45.98M | Converted from GL model (log) |
| 160-MENet-8x1 (g=8) | 43.84 | 20.84 | 850,120 | 45.63M | Converted from GL model (log) |
| 228-MENet-12x1 (g=3) | 34.11 | 13.16 | 1,806,568 | 152.93M | Converted from GL model (log) |
| 256-MENet-12x1 (g=4) | 32.65 | 12.52 | 1,888,240 | 150.65M | Converted from GL model (log) |
| 348-MENet-12x1 (g=3) | 30.10 | 10.92 | 3,368,128 | 312.00M | From clavichord93/MENet (log) |
| 352-MENet-12x1 (g=8) | 33.31 | 13.08 | 2,272,872 | 157.35M | From clavichord93/MENet (log) |
| 456-MENet-24x1 (g=3) | 28.40 | 9.93 | 5,304,784 | 567.90M | From clavichord93/MENet (log) |
| MobileNet x0.25 | 46.26 | 22.49 | 470,072 | 44.09M | Converted from GL model (log) |
| MobileNet x0.5 | 34.15 | 13.55 | 1,331,592 | 155.42M | Converted from GL model (log) |
| MobileNet x0.75 | 30.14 | 10.76 | 2,585,560 | 333.99M | Converted from GL model (log) |
| MobileNet x1.0 | 26.61 | 8.95 | 4,231,976 | 579.80M | Converted from GL model (log) |
| FD-MobileNet x0.25 | 55.77 | 31.32 | 383,160 | 12.95M | From clavichord93/FD-MobileNet (log) |
| FD-MobileNet x0.5 | 43.13 | 20.15 | 993,928 | 41.84M | Converted from GL model (log) |
| FD-MobileNet x0.75 | 38.42 | 16.41 | 1,833,304 | 86.68M | Converted from GL model (log) |
| FD-MobileNet x1.0 | 34.23 | 13.38 | 2,901,288 | 147.46M | Converted from GL model (log) |
| MobileNetV2 x0.25 | 48.34 | 24.51 | 1,516,392 | 34.24M | Converted from GL model (log) |
| MobileNetV2 x0.5 | 36.54 | 15.19 | 1,964,736 | 100.13M | From dmlc/gluon-cv (log) |
| MobileNetV2 x0.75 | 31.89 | 11.76 | 2,627,592 | 198.50M | From dmlc/gluon-cv (log) |
| MobileNetV2 x1.0 | 29.31 | 10.39 | 3,504,960 | 329.36M | From dmlc/gluon-cv (log) |
| IGCV3 x0.25 | 53.70 | 28.71 | 1,534,020 | 41.29M | Converted from GL model (log) |
| IGCV3 x0.5 | 39.75 | 17.32 | 1,985,528 | 111.12M | Converted from GL model (log) |
| IGCV3 x1.0 | 28.40 | 9.84 | 3,491,688 | 340.79M | From homles11/IGCV3 (log) |
| MnasNet | 31.58 | 11.74 | 4,308,816 | 317.67M | From zeusees/Mnasnet...Model (log) |
| DARTS | 26.70 | 8.74 | 4,718,752 | 539.86M | From quark0/darts (log) |
| Xception | 20.97 | 5.49 | 22,855,952 | 8,403.63M | From Cadene/pretrained...pytorch (log) |
| InceptionV3 | 21.12 | 5.65 | 23,834,568 | 5,743.06M | From dmlc/gluon-cv (log) |
| InceptionV4 | 20.64 | 5.29 | 42,679,816 | 12,304.93M | From Cadene/pretrained...pytorch (log) |
| InceptionResNetV2 | 19.93 | 4.90 | 55,843,464 | 13,188.64M | From Cadene/pretrained...pytorch (log) |
| PolyNet | 19.10 | 4.52 | 95,366,600 | 34,821.34M | From Cadene/pretrained...pytorch (log) |
| NASNet-A 4@1056 | 25.68 | 8.16 | 5,289,978 | 584.90M | From Cadene/pretrained...pytorch (log) |
| NASNet-A 6@4032 | 18.14 | 4.21 | 88,753,150 | 23,976.44M | From Cadene/pretrained...pytorch (log) |
| PNASNet-5-Large | 17.88 | 4.28 | 86,057,668 | 25,140.77M | From Cadene/pretrained...pytorch (log) |
For Chainer
| Model | Top1 | Top5 | Params | FLOPs/2 | Remarks |
|---|---|---|---|---|---|
| AlexNet | 44.08 | 21.32 | 61,100,840 | 714.83M | From dmlc/gluon-cv (log) |
| VGG-11 | 31.89 | 11.79 | 132,863,336 | 7,615.87M | From dmlc/gluon-cv (log) |
| VGG-13 | 31.06 | 11.16 | 133,047,848 | 11,317.65M | From dmlc/gluon-cv (log) |
| VGG-16 | 26.75 | 8.70 | 138,357,544 | 15,480.10M | From dmlc/gluon-cv (log) |
| VGG-19 | 25.86 | 8.23 | 143,667,240 | 19,642.55M | From dmlc/gluon-cv (log) |
| BN-VGG-11b | 30.37 | 10.60 | 132,868,840 | 7,630.72M | From dmlc/gluon-cv (log) |
| BN-VGG-13b | 29.45 | 10.19 | 133,053,736 | 11,342.14M | From dmlc/gluon-cv (log) |
| BN-VGG-16b | 26.89 | 8.63 | 138,365,992 | 15,507.20M | From dmlc/gluon-cv (log) |
| BN-VGG-19b | 25.65 | 8.16 | 143,678,248 | 19,672.26M | From dmlc/gluon-cv (log) |
| BN-Inception | 25.08 | 7.78 | 11,295,240 | 2,048.06M | From Cadene/pretrained...pytorch (log) |
| ResNet-10 | 37.12 | 15.49 | 5,418,792 | 894.04M | Converted from GL model (log) |
| ResNet-12 | 35.86 | 14.48 | 5,492,776 | 1,126.25M | Converted from GL model (log) |
| ResNet-14 | 32.84 | 12.42 | 5,788,200 | 1,357.94M | Converted from GL model (log) |
| ResNet-16 | 30.66 | 11.07 | 6,968,872 | 1,589.34M | Converted from GL model (log) |
| ResNet-18 x0.25 | 49.08 | 24.48 | 831,096 | 137.32M | Converted from GL model (log) |
| ResNet-18 x0.5 | 36.55 | 14.99 | 3,055,880 | 486.49M | Converted from GL model (log) |
| ResNet-18 x0.75 | 33.27 | 12.56 | 6,675,352 | 1,047.53M | Converted from GL model (log) |
| ResNet-18 | 28.08 | 9.59 | 11,689,512 | 1,820.41M | Converted from GL model (log) |
| ResNet-34 | 25.35 | 7.95 | 21,797,672 | 3,672.68M | From dmlc/gluon-cv (log) |
| ResNet-50 | 22.61 | 6.41 | 25,557,032 | 3,877.95M | From dmlc/gluon-cv (log) |
| ResNet-50b | 22.34 | 6.18 | 25,557,032 | 4,110.48M | From dmlc/gluon-cv (log) |
| ResNet-101 | 21.65 | 6.01 | 44,549,160 | 7,597.95M | From dmlc/gluon-cv (log) |
| ResNet-101b | 20.79 | 5.40 | 44,549,160 | 7,830.48M | From dmlc/gluon-cv (log) |
| ResNet-152 | 20.74 | 5.35 | 60,192,808 | 11,321.85M | From dmlc/gluon-cv (log) |
| ResNet-152b | 20.29 | 5.27 | 60,192,808 | 11,554.38M | From dmlc/gluon-cv (log) |
| PreResNet-18 | 28.17 | 9.54 | 11,687,848 | 1,820.56M | Converted from GL model (log) |
| PreResNet-34 | 25.89 | 8.12 | 21,796,008 | 3,672.83M | From dmlc/gluon-cv (log) |
| PreResNet-50 | 23.36 | 6.69 | 25,549,480 | 3,875.44M | From dmlc/gluon-cv (log) |
| PreResNet-50b | 23.08 | 6.67 | 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 | 21.61 | 5.87 | 44,541,608 | 7,827.97M | From dmlc/gluon-cv (log) |
| PreResNet-152 | 20.73 | 5.30 | 60,185,256 | 11,319.34M | From dmlc/gluon-cv (log) |
| PreResNet-152b | 20.88 | 5.66 | 60,185,256 | 11,551.87M | From dmlc/gluon-cv (log) |
| PreResNet-200b | 21.03 | 5.60 | 64,666,280 | 15,068.63M | From tornadomeet/ResNet (log) |
| ResNeXt-101 (32x4d) | 21.11 | 5.69 | 44,177,704 | 8,003.45M | From Cadene/pretrained...pytorch (log) |
| ResNeXt-101 (64x4d) | 20.57 | 5.43 | 83,455,272 | 15,500.27M | From Cadene/pretrained...pytorch (log) |
| SE-ResNet-50 | 22.53 | 6.41 | 28,088,024 | 3,880.49M | From Cadene/pretrained...pytorch (log) |
| SE-ResNet-101 | 21.90 | 5.88 | 49,326,872 | 7,602.76M | From Cadene/pretrained...pytorch (log) |
| SE-ResNet-152 | 21.46 | 5.77 | 66,821,848 | 11,328.52M | From Cadene/pretrained...pytorch (log) |
| SE-ResNeXt-50 (32x4d) | 21.04 | 5.58 | 27,559,896 | 4,258.40M | From Cadene/pretrained...pytorch (log) |
| SE-ResNeXt-101 (32x4d) | 19.99 | 5.01 | 48,955,416 | 8,008.26M | From Cadene/pretrained...pytorch (log) |
| SENet-154 | 18.79 | 4.63 | 115,088,984 | 20,745.78M | From Cadene/pretrained...pytorch (log) |
| AirNet50-1x64d (r=2) | 22.46 | 6.20 | 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) | 21.50 | 5.73 | 27,604,296 | 5,339.58M | From soeaver/AirNet-PyTorch (log) |
| BAM-ResNet-50 | 23.71 | 6.97 | 25,915,099 | 4,196.09M | From Jongchan/attention-module (log) |
| CBAM-ResNet-50 | 22.99 | 6.40 | 28,089,624 | 4,116.97M | From Jongchan/attention-module (log) |
| PyramidNet-101 (a=360) | 22.66 | 6.49 | 42,455,070 | 8,743.54M | From dyhan0920/Pyramid...PyTorch (log) |
| DiracNetV2-18 | 30.60 | 11.13 | 11,511,784 | 1,796.62M | From szagoruyko/diracnets (log) |
| DiracNetV2-34 | 27.90 | 9.48 | 21,616,232 | 3,646.93M | From szagoruyko/diracnets (log) |
| DenseNet-121 | 25.04 | 7.79 | 7,978,856 | 2,872.13M | From dmlc/gluon-cv (log) |
| DenseNet-161 | 22.36 | 6.20 | 28,681,000 | 7,793.16M | From dmlc/gluon-cv (log) |
| DenseNet-169 | 23.85 | 6.86 | 14,149,480 | 3,403.89M | From dmlc/gluon-cv (log) |
| DenseNet-201 | 22.64 | 6.29 | 20,013,928 | 4,347.15M | From dmlc/gluon-cv (log) |
| CondenseNet-74 (C=G=4) | 26.81 | 8.61 | 4,773,944 | 546.06M | From ShichenLiu/CondenseNet (log) |
| CondenseNet-74 (C=G=8) | 29.74 | 10.43 | 2,935,416 | 291.52M | From ShichenLiu/CondenseNet (log) |
| PeleeNet | 31.61 | 11.27 | 2,802,248 | 514.87M | Converted from GL model (log) |
| WRN-50-2 | 22.06 | 6.13 | 68,849,128 | 11,405.42M | From szagoruyko/functional-zoo (log) |
| DRN-C-26 | 25.68 | 7.88 | 21,126,584 | 16,993.90M | From fyu/drn (log) |
| DRN-C-42 | 23.72 | 6.93 | 31,234,744 | 25,093.75M | From fyu/drn (log) |
| DRN-C-58 | 22.35 | 6.29 | 40,542,008 | 32,489.94M | From fyu/drn (log) |
| DRN-D-22 | 26.65 | 8.50 | 16,393,752 | 13,051.33M | From fyu/drn (log) |
| DRN-D-38 | 24.53 | 7.36 | 26,501,912 | 21,151.19M | From fyu/drn (log) |
| DRN-D-54 | 22.08 | 6.23 | 35,809,176 | 28,547.38M | From fyu/drn (log) |
| DRN-D-105 | 21.32 | 5.84 | 54,801,304 | 43,442.43M | From fyu/drn (log) |
| DPN-68 | 23.61 | 7.01 | 12,611,602 | 2,351.84M | From Cadene/pretrained...pytorch (log) |
| DPN-98 | 20.80 | 5.53 | 61,570,728 | 11,716.51M | From Cadene/pretrained...pytorch (log) |
| DPN-131 | 20.04 | 5.23 | 79,254,504 | 16,076.15M | From Cadene/pretrained...pytorch (log) |
| DarkNet Tiny | 40.33 | 17.46 | 1,042,104 | 500.85M | Converted from GL model (log) |
| DarkNet Ref | 38.09 | 16.71 | 7,319,416 | 367.59M | Converted from GL model (log) |
| DarkNet-53 | 21.41 | 5.56 | 41,609,928 | 7,133.86M | From dmlc/gluon-cv (log) |
| SqueezeNet v1.0 | 38.76 | 17.38 | 1,248,424 | 823.67M | Converted from GL model (log) |
| SqueezeNet v1.1 | 39.13 | 17.40 | 1,235,496 | 352.02M | Converted from GL model (log) |
| SqueezeResNet v1.1 | 39.85 | 17.87 | 1,235,496 | 352.02M | Converted from GL model (log) |
| 1.0-SqNxt-23 | 46.55 | 22.13 | 724,056 | 287.28M | Converted from GL model (log) |
| ShuffleNet x0.25 (g=1) | 62.04 | 36.81 | 209,746 | 12.35M | Converted from GL model (log) |
| ShuffleNet x0.25 (g=3) | 61.30 | 36.16 | 305,902 | 13.09M | Converted from GL model (log) |
| ShuffleNetV2 x0.5 | 43.45 | 20.73 | 1,366,792 | 43.31M | Converted from GL model (log) |
| ShuffleNetV2 x1.0 | 33.39 | 12.98 | 2,278,604 | 149.72M | Converted from GL model (log) |
| ShuffleNetV2 x1.5 | 33.96 | 13.37 | 4,406,098 | 320.77M | Converted from GL model (log) |
| ShuffleNetV2 x2.0 | 33.21 | 13.03 | 7,601,686 | 595.84M | Converted from GL model (log) |
| ShuffleNetV2b x0.5 | 39.78 | 17.87 | 1,366,792 | 43.31M | Converted from GL model (log) |
| ShuffleNetV2b x1.0 | 30.36 | 11.00 | 2,279,760 | 150.62M | Converted from GL model (log) |
| ShuffleNetV2c x0.5 | 39.82 | 18.14 | 1,366,792 | 43.31M | From tensorpack/tensorpack (log) |
| ShuffleNetV2c x1.0 | 30.74 | 11.37 | 2,279,760 | 150.62M | From tensorpack/tensorpack (log) |
| 108-MENet-8x1 (g=3) | 43.67 | 20.42 | 654,516 | 42.68M | Converted from GL model (log) |
| 128-MENet-8x1 (g=4) | 42.07 | 19.19 | 750,796 | 45.98M | Converted from GL model (log) |
| 160-MENet-8x1 (g=8) | 43.54 | 20.42 | 850,120 | 45.63M | Converted from GL model (log) |
| 228-MENet-12x1 (g=3) | 33.86 | 13.01 | 1,806,568 | 152.93M | Converted from GL model (log) |
| 256-MENet-12x1 (g=4) | 32.30 | 12.18 | 1,888,240 | 150.65M | Converted from GL model (log) |
| 348-MENet-12x1 (g=3) | 31.14 | 11.40 | 3,368,128 | 312.00M | From clavichord93/MENet (log) |
| 352-MENet-12x1 (g=8) | 34.62 | 13.68 | 2,272,872 | 157.35M | From clavichord93/MENet (log) |
| 456-MENet-24x1 (g=3) | 29.55 | 10.39 | 5,304,784 | 567.90M | From clavichord93/MENet (log) |
| MobileNet x0.25 | 45.85 | 22.16 | 470,072 | 44.09M | Converted from GL model (log) |
| MobileNet x0.5 | 33.89 | 13.37 | 1,331,592 | 155.42M | Converted from GL model (log) |
| MobileNet x0.75 | 29.86 | 10.53 | 2,585,560 | 333.99M | Converted from GL model (log) |
| MobileNet x1.0 | 26.47 | 8.66 | 4,231,976 | 579.80M | Converted from GL model (log) |
| FD-MobileNet x0.25 | 56.11 | 31.45 | 383,160 | 12.95M | Converted from GL model (log) |
| FD-MobileNet x0.5 | 42.68 | 19.76 | 993,928 | 41.84M | Converted from GL model (log) |
| FD-MobileNet x0.75 | 37.94 | 15.99 | 1,833,304 | 86.68M | Converted from GL model (log) |
| FD-MobileNet x1.0 | 33.90 | 13.16 | 2,901,288 | 147.46M | Converted from GL model (log) |
| MobileNetV2 x0.25 | 48.10 | 24.11 | 1,516,392 | 34.24M | Converted from GL model (log) |
| MobileNetV2 x0.5 | 35.96 | 14.98 | 1,964,736 | 100.13M | From dmlc/gluon-cv (log) |
| MobileNetV2 x0.75 | 31.28 | 11.48 | 2,627,592 | 198.50M | From dmlc/gluon-cv (log) |
| MobileNetV2 x1.0 | 28.87 | 10.05 | 3,504,960 | 329.36M | From dmlc/gluon-cv (log) |
| IGCV3 x0.25 | 53.36 | 28.28 | 1,534,020 | 41.29M | Converted from GL model (log) |
| IGCV3 x0.5 | 39.36 | 17.04 | 1,985,528 | 111.12M | Converted from GL model (log) |
| IGCV3 x1.0 | 28.20 | 9.55 | 3,491,688 | 340.79M | From homles11/IGCV3 (log) |
| MnasNet | 31.27 | 11.44 | 4,308,816 | 317.67M | From zeusees/Mnasnet...Model (log) |
| DARTS | 27.29 | 8.97 | 4,718,752 | 539.86M | From quark0/darts (log) |
| Xception | 21.04 | 5.47 | 22,855,952 | 8,403.63M | From Cadene/pretrained...pytorch (log) |
| InceptionV3 | 21.11 | 5.61 | 23,834,568 | 5,743.06M | From dmlc/gluon-cv (log) |
| InceptionV4 | 20.62 | 5.26 | 42,679,816 | 12,304.93M | From Cadene/pretrained...pytorch (log) |
| InceptionResNetV2 | 19.93 | 4.92 | 55,843,464 | 13,188.64M | From Cadene/pretrained...pytorch (log) |
| PolyNet | 19.08 | 4.50 | 95,366,600 | 34,821.34M | From Cadene/pretrained...pytorch (log) |
| NASNet-A 4@1056 | 25.36 | 7.96 | 5,289,978 | 584.90M | From Cadene/pretrained...pytorch (log) |
| NASNet-A 6@4032 | 18.17 | 4.22 | 88,753,150 | 23,976.44M | From Cadene/pretrained...pytorch (log) |
| PNASNet-5-Large | 17.90 | 4.26 | 86,057,668 | 25,140.77M | From Cadene/pretrained...pytorch (log) |
For Keras
| Model | Top1 | Top5 | Params | FLOPs/2 | Remarks |
|---|---|---|---|---|---|
| AlexNet | 44.10 | 21.26 | 61,100,840 | 714.83M | From dmlc/gluon-cv (log) |
| VGG-11 | 31.90 | 11.75 | 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 | 15,507.20M | From dmlc/gluon-cv (log) |
| VGG-19 | 25.87 | 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 | 26.88 | 8.65 | 138,365,992 | 15,507.20M | From dmlc/gluon-cv (log) |
| BN-VGG-19b | 25.65 | 8.14 | 143,678,248 | 19,672.26M | From dmlc/gluon-cv (log) |
| ResNet-10 | 37.09 | 15.54 | 5,418,792 | 894.04M | Converted from GL model (log) |
| ResNet-12 | 35.86 | 14.45 | 5,492,776 | 1,126.25M | Converted from GL model (log) |
| ResNet-14 | 32.85 | 12.42 | 5,788,200 | 1,357.94M | Converted from GL model (log) |
| ResNet-16 | 30.67 | 11.09 | 6,968,872 | 1,589.34M | Converted from GL model (log) |
| ResNet-18 x0.25 | 49.14 | 24.45 | 831,096 | 137.32M | Converted from GL model (log) |
| ResNet-18 x0.5 | 36.54 | 14.96 | 3,055,880 | 486.49M | Converted from GL model (log) |
| ResNet-18 x0.75 | 33.24 | 12.54 | 6,675,352 | 1,047.53M | Converted from GL model (log) |
| ResNet-18 | 28.08 | 9.52 | 11,689,512 | 1,820.41M | Converted from GL model (log) |
| ResNet-34 | 25.32 | 7.92 | 21,797,672 | 3,672.68M | From dmlc/gluon-cv (log) |
| ResNet-50 | 22.63 | 6.41 | 25,557,032 | 3,877.95M | From dmlc/gluon-cv (log) |
| ResNet-50b | 22.31 | 6.18 | 25,557,032 | 4,110.48M | From dmlc/gluon-cv (log) |
| ResNet-101 | 21.64 | 5.99 | 44,549,160 | 7,597.95M | From dmlc/gluon-cv (log) |
| ResNet-101b | 20.78 | 5.39 | 44,549,160 | 7,830.48M | From dmlc/gluon-cv (log) |
| ResNet-152 | 20.74 | 5.35 | 60,192,808 | 11,321.85M | From dmlc/gluon-cv (log) |
| ResNet-152b | 20.30 | 5.25 | 60,192,808 | 11,554.38M | From dmlc/gluon-cv (log) |
| PreResNet-18 | 28.16 | 9.52 | 11,687,848 | 1,820.56M | Converted from GL model (log) |
| PreResNet-34 | 25.86 | 8.11 | 21,796,008 | 3,672.83M | From dmlc/gluon-cv (log) |
| PreResNet-50 | 23.38 | 6.68 | 25,549,480 | 3,875.44M | From dmlc/gluon-cv (log) |
| PreResNet-50b | 23.14 | 6.63 | 25,549,480 | 4,107.97M | From dmlc/gluon-cv (log) |
| PreResNet-101 | 21.43 | 5.75 | 44,541,608 | 7,595.44M | From dmlc/gluon-cv (log) |
| PreResNet-101b | 21.71 | 5.88 | 44,541,608 | 7,827.97M | From dmlc/gluon-cv (log) |
| PreResNet-152 | 20.69 | 5.31 | 60,185,256 | 11,319.34M | From dmlc/gluon-cv (log) |
| PreResNet-152b | 20.99 | 5.76 | 60,185,256 | 11,551.87M | From dmlc/gluon-cv (log) |
| PreResNet-200b | 21.09 | 5.64 | 64,666,280 | 15,068.63M | From tornadomeet/ResNet (log) |
| ResNeXt-101 (32x4d) | 21.30 | 5.78 | 44,177,704 | 8,003.45M | From Cadene/pretrained...pytorch (log) |
| ResNeXt-101 (64x4d) | 20.59 | 5.41 | 83,455,272 | 15,500.27M | From Cadene/pretrained...pytorch (log) |
| SE-ResNet-50 | 22.50 | 6.43 | 28,088,024 | 3,880.49M | From Cadene/pretrained...pytorch (log) |
| SE-ResNet-101 | 21.92 | 5.88 | 49,326,872 | 7,602.76M | From Cadene/pretrained...pytorch (log) |
| SE-ResNet-152 | 21.46 | 5.77 | 66,821,848 | 11,328.52M | From Cadene/pretrained...pytorch (log) |
| SE-ResNeXt-50 (32x4d) | 21.05 | 5.57 | 27,559,896 | 4,258.40M | From Cadene/pretrained...pytorch (log) |
| SE-ResNeXt-101 (32x4d) | 19.98 | 4.99 | 48,955,416 | 8,008.26M | From Cadene/pretrained...pytorch (log) |
| SENet-154 | 18.83 | 4.65 | 115,088,984 | 20,745.78M | From Cadene/pretrained...pytorch (log) |
| DenseNet-121 | 25.09 | 7.80 | 7,978,856 | 2,872.13M | From dmlc/gluon-cv (log) |
| DenseNet-161 | 22.39 | 6.18 | 28,681,000 | 7,793.16M | From dmlc/gluon-cv (log) |
| DenseNet-169 | 23.88 | 6.89 | 14,149,480 | 3,403.89M | From dmlc/gluon-cv (log) |
| DenseNet-201 | 22.69 | 6.35 | 20,013,928 | 4,347.15M | From dmlc/gluon-cv (log) |
| DarkNet Tiny | 40.31 | 17.46 | 1,042,104 | 500.85M | Converted from GL model (log) |
| DarkNet Ref | 37.99 | 16.68 | 7,319,416 | 367.59M | Converted from GL model (log) |
| DarkNet-53 | 21.43 | 5.56 | 41,609,928 | 7,133.86M | From dmlc/gluon-cv (log) |
| SqueezeNet v1.0 | 39.17 | 17.56 | 1,248,424 | 823.67M | Converted from GL model (log) |
| SqueezeNet v1.1 | 39.08 | 17.39 | 1,235,496 | 352.02M | Converted from GL model (log) |
| SqueezeResNet v1.1 | 39.82 | 17.84 | 1,235,496 | 352.02M | Converted from GL model (log) |
| 1.0-SqNxt-23 | 45.97 | 21.67 | 724,056 | 287.28M | Converted from GL model (log) |
| ShuffleNet x0.25 (g=1) | 62.00 | 36.76 | 209,746 | 12.35M | Converted from GL model (log) |
| ShuffleNet x0.25 (g=3) | 61.32 | 36.15 | 305,902 | 13.09M | Converted from GL model (log) |
| ShuffleNetV2 x0.5 | 40.76 | 18.40 | 1,366,792 | 43.31M | Converted from GL model (log) |
| ShuffleNetV2 x1.0 | 31.02 | 11.33 | 2,278,604 | 149.72M | Converted from GL model (log) |
| ShuffleNetV2 x1.5 | 32.46 | 12.47 | 4,406,098 | 320.77M | Converted from GL model (log) |
| ShuffleNetV2 x2.0 | 31.91 | 12.23 | 7,601,686 | 595.84M | Converted from GL model (log) |
| 108-MENet-8x1 (g=3) | 43.61 | 20.31 | 654,516 | 42.68M | Converted from GL model (log) |
| 128-MENet-8x1 (g=4) | 42.08 | 19.14 | 750,796 | 45.98M | Converted from GL model (log) |
| 160-MENet-8x1 (g=8) | 43.47 | 20.28 | 850,120 | 45.63M | Converted from GL model (log) |
| 228-MENet-12x1 (g=3) | 33.85 | 12.88 | 1,806,568 | 152.93M | Converted from GL model (log) |
| 256-MENet-12x1 (g=4) | 32.22 | 12.17 | 1,888,240 | 150.65M | Converted from GL model (log) |
| 348-MENet-12x1 (g=3) | 31.17 | 11.42 | 3,368,128 | 312.00M | From clavichord93/MENet (log) |
| 352-MENet-12x1 (g=8) | 34.69 | 13.75 | 2,272,872 | 157.35M | From clavichord93/MENet (log) |
| 456-MENet-24x1 (g=3) | 29.55 | 10.44 | 5,304,784 | 567.90M | From clavichord93/MENet (log) |
| MobileNet x0.25 | 45.80 | 22.17 | 470,072 | 44.09M | Converted from GL model (log) |
| MobileNet x0.5 | 33.94 | 13.30 | 1,331,592 | 155.42M | Converted from GL model (log) |
| MobileNet x0.75 | 29.85 | 10.51 | 2,585,560 | 333.99M | Converted from GL model (log) |
| MobileNet x1.0 | 26.43 | 8.66 | 4,231,976 | 579.80M | Converted from GL model (log) |
| FD-MobileNet x0.25 | 56.17 | 31.37 | 383,160 | 12.95M | Converted from GL model (log) |
| FD-MobileNet x0.5 | 42.61 | 19.69 | 993,928 | 41.84M | Converted from GL model (log) |
| FD-MobileNet x0.75 | 37.90 | 16.01 | 1,833,304 | 86.68M | Converted from GL model (log) |
| FD-MobileNet x1.0 | 33.80 | 13.12 | 2,901,288 | 147.46M | Converted from GL model (log) |
| MobileNetV2 x0.25 | 48.06 | 24.12 | 1,516,392 | 34.24M | Converted from GL model (log) |
| MobileNetV2 x0.5 | 35.51 | 14.65 | 1,964,736 | 100.13M | From dmlc/gluon-cv (log) |
| MobileNetV2 x0.75 | 30.81 | 11.26 | 2,627,592 | 198.50M | From dmlc/gluon-cv (log) |
| MobileNetV2 x1.0 | 28.50 | 9.90 | 3,504,960 | 329.36M | From dmlc/gluon-cv (log) |
| IGCV3 x0.25 | 53.41 | 28.29 | 1,534,020 | 41.29M | Converted from GL model (log) |
| IGCV3 x0.5 | 39.39 | 17.04 | 1,985,528 | 111.12M | Converted from GL model (log) |
| IGCV3 x1.0 | 28.21 | 9.55 | 3,491,688 | 340.79M | From homles11/IGCV3 (log) |
| MnasNet | 31.30 | 11.45 | 4,308,816 | 317.67M | From zeusees/Mnasnet...Model (log) |
For TensorFlow
| Model | Top1 | Top5 | Params | FLOPs/2 | Remarks |
|---|---|---|---|---|---|
| AlexNet | 44.07 | 21.32 | 61,100,840 | 714.83M | From dmlc/gluon-cv (log) |
| VGG-11 | 31.89 | 11.73 | 132,863,336 | 7,615.87M | From dmlc/gluon-cv (log) |
| VGG-13 | 31.03 | 11.15 | 133,047,848 | 11,317.65M | From dmlc/gluon-cv (log) |
| VGG-16 | 26.77 | 8.68 | 138,357,544 | 15,480.10M | From dmlc/gluon-cv (log) |
| VGG-19 | 25.93 | 8.23 | 143,667,240 | 19,642.55M | From dmlc/gluon-cv (log) |
| BN-VGG-11b | 30.34 | 10.58 | 132,868,840 | 7,630.72M | From dmlc/gluon-cv (log) |
| BN-VGG-13b | 29.47 | 10.15 | 133,053,736 | 11,342.14M | From dmlc/gluon-cv (log) |
| BN-VGG-16b | 26.83 | 8.66 | 138,365,992 | 15,507.20M | From dmlc/gluon-cv (log) |
| BN-VGG-19b | 25.62 | 8.17 | 143,678,248 | 19,672.26M | From dmlc/gluon-cv (log) |
| ResNet-10 | 37.11 | 15.52 | 5,418,792 | 894.04M | Converted from GL model (log) |
| ResNet-12 | 35.82 | 14.50 | 5,492,776 | 1,126.25M | Converted from GL model (log) |
| ResNet-14 | 32.83 | 12.45 | 5,788,200 | 1,357.94M | Converted from GL model (log) |
| ResNet-16 | 30.66 | 11.05 | 6,968,872 | 1,589.34M | Converted from GL model (log) |
| ResNet-18 x0.25 | 49.12 | 24.50 | 831,096 | 137.32M | Converted from GL model (log) |
| ResNet-18 x0.5 | 36.51 | 14.93 | 3,055,880 | 486.49M | Converted from GL model (log) |
| ResNet-18 x0.75 | 33.28 | 12.50 | 6,675,352 | 1,047.53M | Converted from GL model (log) |
| ResNet-18 | 28.16 | 9.56 | 11,689,512 | 1,820.41M | Converted from GL model (log) |
| ResNet-34 | 25.32 | 7.93 | 21,797,672 | 3,672.68M | From dmlc/gluon-cv (log) |
| ResNet-50 | 22.61 | 6.42 | 25,557,032 | 3,877.95M | From dmlc/gluon-cv (log) |
| ResNet-50b | 22.36 | 6.21 | 25,557,032 | 4,110.48M | From dmlc/gluon-cv (log) |
| ResNet-101 | 21.61 | 6.01 | 44,549,160 | 7,597.95M | From dmlc/gluon-cv (log) |
| ResNet-101b | 20.81 | 5.40 | 44,549,160 | 7,830.48M | From dmlc/gluon-cv (log) |
| ResNet-152 | 20.73 | 5.35 | 60,192,808 | 11,321.85M | From dmlc/gluon-cv (log) |
| ResNet-152b | 20.27 | 5.23 | 60,192,808 | 11,554.38M | From dmlc/gluon-cv (log) |
| PreResNet-18 | 28.21 | 9.49 | 11,687,848 | 1,820.56M | Converted from GL model (log) |
| PreResNet-34 | 25.82 | 8.08 | 21,796,008 | 3,672.83M | From dmlc/gluon-cv (log) |
| PreResNet-50 | 23.42 | 6.68 | 25,549,480 | 3,875.44M | From dmlc/gluon-cv (log) |
| PreResNet-50b | 23.12 | 6.61 | 25,549,480 | 4,107.97M | From dmlc/gluon-cv (log) |
| PreResNet-101 | 21.49 | 5.72 | 44,541,608 | 7,595.44M | From dmlc/gluon-cv (log) |
| PreResNet-101b | 21.70 | 5.91 | 44,541,608 | 7,827.97M | From dmlc/gluon-cv (log) |
| PreResNet-152 | 20.63 | 5.29 | 60,185,256 | 11,319.34M | From dmlc/gluon-cv (log) |
| PreResNet-152b | 20.95 | 5.76 | 60,185,256 | 11,551.87M | From dmlc/gluon-cv (log) |
| PreResNet-200b | 21.12 | 5.60 | 64,666,280 | 15,068.63M | From tornadomeet/ResNet (log) |
| ResNeXt-101 (32x4d) | 21.33 | 5.80 | 44,177,704 | 8,003.45M | From Cadene/pretrained...pytorch (log) |
| ResNeXt-101 (64x4d) | 20.59 | 5.43 | 83,455,272 | 15,500.27M | From Cadene/pretrained...pytorch (log) |
| SE-ResNet-50 | 22.53 | 6.43 | 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 | 5.78 | 66,821,848 | 11,328.52M | From Cadene/pretrained...pytorch (log) |
| SE-ResNeXt-50 (32x4d) | 21.01 | 5.53 | 27,559,896 | 4,258.40M | From Cadene/pretrained...pytorch (log) |
| SE-ResNeXt-101 (32x4d) | 19.99 | 4.97 | 48,955,416 | 8,008.26M | From Cadene/pretrained...pytorch (log) |
| SENet-154 | 18.77 | 4.63 | 115,088,984 | 20,745.78M | From Cadene/pretrained...pytorch (log) |
| DenseNet-121 | 25.16 | 7.82 | 7,978,856 | 2,872.13M | From dmlc/gluon-cv (log) |
| DenseNet-161 | 22.40 | 6.17 | 28,681,000 | 7,793.16M | From dmlc/gluon-cv (log) |
| DenseNet-169 | 23.93 | 6.87 | 14,149,480 | 3,403.89M | From dmlc/gluon-cv (log) |
| DenseNet-201 | 22.70 | 6.35 | 20,013,928 | 4,347.15M | From dmlc/gluon-cv (log) |
| DarkNet Tiny | 40.35 | 17.51 | 1,042,104 | 500.85M | Converted from GL model (log) |
| DarkNet Ref | 37.99 | 16.72 | 7,319,416 | 367.59M | Converted from GL model (log) |
| DarkNet-53 | 21.42 | 5.55 | 41,609,928 | 7,133.86M | From dmlc/gluon-cv (log) |
| SqueezeNet v1.0 | 39.18 | 17.58 | 1,248,424 | 823.67M | Converted from GL model (log) |
| SqueezeNet v1.1 | 39.14 | 17.39 | 1,235,496 | 352.02M | Converted from GL model (log) |
| SqueezeResNet v1.1 | 39.75 | 17.92 | 1,235,496 | 352.02M | Converted from GL model (log) |
| 1.0-SqNxt-23 | 48.14 | 23.51 | 724,056 | 287.28M | Converted from GL model (log) |
| ShuffleNet x0.25 (g=1) | 62.03 | 36.80 | 209,746 | 12.35M | Converted from GL model (log) |
| ShuffleNet x0.25 (g=3) | 61.33 | 36.17 | 305,902 | 13.09M | Converted from GL model (log) |
| ShuffleNetV2 x0.5 | 40.88 | 18.44 | 1,366,792 | 43.31M | Converted from GL model (log) |
| ShuffleNetV2 x1.0 | 31.02 | 11.31 | 2,278,604 | 149.72M | Converted from GL model (log) |
| ShuffleNetV2 x1.5 | 32.51 | 12.50 | 4,406,098 | 320.77M | Converted from GL model (log) |
| ShuffleNetV2 x2.0 | 31.99 | 12.26 | 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 | 30.40 | 11.04 | 2,279,760 | 150.62M | Converted from GL model (log) |
| ShuffleNetV2c x0.5 | 39.93 | 18.11 | 1,366,792 | 43.31M | From tensorpack/tensorpack (log) |
| ShuffleNetV2c x1.0 | 30.77 | 11.39 | 2,279,760 | 150.62M | From tensorpack/tensorpack (log) |
| 108-MENet-8x1 (g=3) | 43.67 | 20.32 | 654,516 | 42.68M | Converted from GL model (log) |
| 128-MENet-8x1 (g=4) | 42.04 | 19.15 | 750,796 | 45.98M | Converted from GL model (log) |
| 160-MENet-8x1 (g=8) | 43.53 | 20.28 | 850,120 | 45.63M | Converted from GL model (log) |
| 228-MENet-12x1 (g=3) | 33.85 | 12.92 | 1,806,568 | 152.93M | Converted from GL model (log) |
| 256-MENet-12x1 (g=4) | 32.19 | 12.19 | 1,888,240 | 150.65M | Converted from GL model (log) |
| 348-MENet-12x1 (g=3) | 31.19 | 11.41 | 3,368,128 | 312.00M | From clavichord93/MENet (log) |
| 352-MENet-12x1 (g=8) | 34.65 | 13.71 | 2,272,872 | 157.35M | From clavichord93/MENet (log) |
| 456-MENet-24x1 (g=3) | 29.56 | 10.46 | 5,304,784 | 567.90M | From clavichord93/MENet (log) |
| MobileNet x0.25 | 45.78 | 22.21 | 470,072 | 44.09M | Converted from GL model (log) |
| MobileNet x0.5 | 33.85 | 13.31 | 1,331,592 | 155.42M | Converted from GL model (log) |
| MobileNet x0.75 | 29.82 | 10.49 | 2,585,560 | 333.99M | Converted from GL model (log) |
| MobileNet x1.0 | 26.45 | 8.67 | 4,231,976 | 579.80M | Converted from GL model (log) |
| FD-MobileNet x0.25 | 56.08 | 31.44 | 383,160 | 12.95M | Converted from GL model (log) |
| FD-MobileNet x0.5 | 42.67 | 19.70 | 993,928 | 41.84M | Converted from GL model (log) |
| FD-MobileNet x0.75 | 37.95 | 16.02 | 1,833,304 | 86.68M | Converted from GL model (log) |
| FD-MobileNet x1.0 | 33.78 | 13.18 | 2,901,288 | 147.46M | Converted from GL model (log) |
| MobileNetV2 x0.25 | 48.18 | 24.16 | 1,516,392 | 34.24M | Converted from GL model (log) |
| MobileNetV2 x0.5 | 35.51 | 14.60 | 1,964,736 | 100.13M | From dmlc/gluon-cv (log) |
| MobileNetV2 x0.75 | 30.79 | 11.24 | 2,627,592 | 198.50M | From dmlc/gluon-cv (log) |
| MobileNetV2 x1.0 | 28.53 | 9.90 | 3,504,960 | 329.36M | From dmlc/gluon-cv (log) |
| IGCV3 x0.25 | 53.39 | 28.35 | 1,534,020 | 41.29M | Converted from GL model (log) |
| IGCV3 x0.5 | 39.38 | 17.05 | 1,985,528 | 111.12M | Converted from GL model (log) |
| IGCV3 x1.0 | 28.17 | 9.55 | 3,491,688 | 340.79M | From homles11/IGCV3 (log) |
| MnasNet | 31.29 | 11.44 | 4,308,816 | 317.67M | From zeusees/Mnasnet...Model (log) |
Pretrained models for CIFAR-10
| Model | Error, % | Params | FLOPs/2 | Gluon | PyTorch | Chainer | Remarks |
|---|---|---|---|---|---|---|---|
| ResNet-20 | 5.97 | 272,474 | 41.29M | log | log | log | Training |
| ResNet-56 | 4.52 | 855,770 | 127.06M | log | log | log | Training |
| ResNet-110 | 3.69 | 1,730,714 | 255.70M | log | log | log | Training |
| PreResNet-20 | 6.51 | 272,282 | 41.27M | log | log | log | Training |
| PreResNet-56 | 4.49 | 855,578 | 127.03M | log | log | log | Training |
| PreResNet-110 | 3.86 | 1,730,522 | 255.68M | log | log | log | Training |
| ResNeXt-29 (32x4d) | 3.45 | 4,775,754 | 780.55M | log | log | log | Training |
| ResNeXt-29 (16x64d) | 2.75 | 68,155,210 | 10,709.34M | log | log | log | From dmlc/gluon-cv |
| WRN-16-10 | 2.93 | 17,116,634 | 2,414.04M | log | log | log | Training |
| WRN-28-10 | 2.39 | 36,479,194 | 5,246.98M | log | log | log | Training |
| WRN-40-8 | 2.37 | 35,748,314 | 5,176.90M | log | log | log | Training |