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')
VGG/BN-VGG ('Very Deep Convolutional Networks for Large-Scale Image Recognition')
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')
WRN ('Wide Residual Networks')
DRN-C/DRN-D ('Dilated Residual Networks')
DPN ('Dual Path Networks')
DarkNet ('Darknet: Open source neural networks in c')
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 ordinary normalization.

For Gluon

Model	Top1	Top5	Params	FLOPs	Remarks
AlexNet	44.12	21.26	61,100,840	715.49M	From dmlc/gluon-cv (log)
VGG-11	31.91	11.76	132,863,336	7,622.65M	From dmlc/gluon-cv (log)
VGG-13	31.06	11.12	133,047,848	11,326.85M	From dmlc/gluon-cv (log)
VGG-16	26.78	8.69	138,357,544	15,489.95M	From dmlc/gluon-cv (log)
VGG-19	25.88	8.23	143,667,240	19,653.05M	From dmlc/gluon-cv (log)
BN-VGG-11b	30.34	10.57	132,868,840	7,622.65M	From dmlc/gluon-cv (log)
BN-VGG-13b	29.48	10.16	133,053,736	11,326.85M	From dmlc/gluon-cv (log)
BN-VGG-16b	26.89	8.65	138,365,992	15,489.95M	From dmlc/gluon-cv (log)
BN-VGG-19b	25.66	8.15	143,678,248	19,653.05M	From dmlc/gluon-cv (log)
ResNet-10	37.09	15.55	5,418,792	892.62M	Training (log)
ResNet-12	35.86	14.46	5,492,776	1,124.23M	Training (log)
ResNet-14	32.85	12.41	5,788,200	1,355.64M	Training (log)
ResNet-16	30.68	11.10	6,968,872	1,586.95M	Training (log)
ResNet-18 x0.25	49.16	24.45	831,096	136.64M	Training (log)
ResNet-18 x0.5	36.54	14.96	3,055,880	485.22M	Training (log)
ResNet-18 x0.75	33.25	12.54	6,675,352	1,045.75M	Training (log)
ResNet-18	29.13	9.94	11,689,512	1,818.21M	Training (log)
ResNet-34	25.34	7.92	21,797,672	3,669.16M	From dmlc/gluon-cv (log)
ResNet-50	23.50	6.87	25,557,032	3,868.96M	From dmlc/gluon-cv (log)
ResNet-50b	22.92	6.44	25,557,032	4,100.70M	From dmlc/gluon-cv (log)
ResNet-101	21.66	5.99	44,549,160	7,586.30M	From dmlc/gluon-cv (log)
ResNet-101b	21.18	5.60	44,549,160	7,818.04M	From dmlc/gluon-cv (log)
ResNet-152	21.01	5.61	60,192,808	11,304.85M	From dmlc/gluon-cv (log)
ResNet-152b	20.54	5.37	60,192,808	11,536.58M	From dmlc/gluon-cv (log)
PreResNet-18	28.72	9.88	11,687,848	1,818.41M	Training (log)
PreResNet-34	25.88	8.11	21,796,008	3,669.36M	From dmlc/gluon-cv (log)
PreResNet-50	23.39	6.68	25,549,480	3,869.16M	From dmlc/gluon-cv (log)
PreResNet-50b	23.16	6.64	25,549,480	4,100.90M	From dmlc/gluon-cv (log)
PreResNet-101	21.45	5.75	44,541,608	7,586.50M	From dmlc/gluon-cv (log)
PreResNet-101b	21.73	5.88	44,541,608	7,818.24M	From dmlc/gluon-cv (log)
PreResNet-152	20.70	5.32	60,185,256	11,305.05M	From dmlc/gluon-cv (log)
PreResNet-152b	21.00	5.75	60,185,256	11,536.78M	From dmlc/gluon-cv (log)
PreResNet-200b	21.10	5.64	64,666,280	15,040.27M	From tornadomeet/ResNet (log)
ResNeXt-101 (32x4d)	21.32	5.79	44,177,704	7,991.62M	From Cadene/pretrained...pytorch (log)
ResNeXt-101 (64x4d)	20.60	5.41	83,455,272	15,491.88M	From Cadene/pretrained...pytorch (log)
SE-ResNet-50	22.51	6.44	28,088,024	3,877.01M	From Cadene/pretrained...pytorch (log)
SE-ResNet-101	21.92	5.89	49,326,872	7,600.01M	From Cadene/pretrained...pytorch (log)
SE-ResNet-152	21.48	5.77	66,821,848	11,324.62M	From Cadene/pretrained...pytorch (log)
SE-ResNeXt-50 (32x4d)	21.06	5.58	27,559,896	4,253.33M	From Cadene/pretrained...pytorch (log)
SE-ResNeXt-101 (32x4d)	19.99	5.00	48,955,416	8,005.33M	From Cadene/pretrained...pytorch (log)
SENet-154	18.84	4.65	115,088,984	20,742.40M	From Cadene/pretrained...pytorch (log)
IBN-ResNet-50	23.56	6.68	25,557,032	4,100.70M	From XingangPan/IBN-Net (log)
IBN-ResNet-101	21.89	5.87	44,549,160	7,818.04M	From XingangPan/IBN-Net (log)
IBN(b)-ResNet-50	23.91	6.97	25,558,568	4,100.70M	From XingangPan/IBN-Net (log)
IBN-ResNeXt-101 (32x4d)	21.43	5.62	44,177,704	7,991.62M	From XingangPan/IBN-Net (log)
IBN-DenseNet-121	24.98	7.47	7,978,856	2,852.39M	From XingangPan/IBN-Net (log)
IBN-DenseNet-169	23.78	6.82	14,149,480	3,381.48M	From XingangPan/IBN-Net (log)
AirNet50-1x64d (r=2)	22.48	6.21	27,425,864	4,757.77M	From soeaver/AirNet-PyTorch (log)
AirNet50-1x64d (r=16)	22.91	6.46	25,714,952	4,385.54M	From soeaver/AirNet-PyTorch (log)
BAM-ResNet-50	23.68	6.96	25,915,099	4,211.08M	From Jongchan/attention-module (log)
CBAM-ResNet-50	23.02	6.38	28,089,624	4,118.19M	From Jongchan/attention-module (log)
PyramidNet-101 (a=360)	22.72	6.52	42,455,070	8,706.81M	From dyhan0920/Pyramid...PyTorch (log)
DiracNetV2-18	30.61	11.17	11,511,784	1,798.43M	From szagoruyko/diracnets (log)
DiracNetV2-34	27.93	9.46	21,616,232	3,649.37M	From szagoruyko/diracnets (log)
DenseNet-121	25.11	7.80	7,978,856	2,852.39M	From dmlc/gluon-cv (log)
DenseNet-161	22.40	6.18	28,681,000	7,761.25M	From dmlc/gluon-cv (log)
DenseNet-169	23.89	6.89	14,149,480	3,381.48M	From dmlc/gluon-cv (log)
DenseNet-201	22.71	6.36	20,013,928	4,318.75M	From dmlc/gluon-cv (log)
CondenseNet-74 (C=G=4)	26.82	8.64	4,773,944	533.64M	From ShichenLiu/CondenseNet (log)
CondenseNet-74 (C=G=8)	29.76	10.49	2,935,416	278.55M	From ShichenLiu/CondenseNet (log)
WRN-50-2	22.15	6.12	68,849,128	11,412.82M	From szagoruyko/functional-zoo (log)
DRN-C-26	25.68	7.89	21,126,584	20,838.70M	From fyu/drn (log)
DRN-C-42	23.80	6.92	31,234,744	31,236.97M	From fyu/drn (log)
DRN-C-58	22.35	6.27	40,542,008	36,862.32M	From fyu/drn (log)
DRN-D-22	26.67	8.52	16,393,752	16,626.00M	From fyu/drn (log)
DRN-D-38	24.51	7.36	26,501,912	27,024.27M	From fyu/drn (log)
DRN-D-54	22.05	6.27	35,809,176	32,649.62M	From fyu/drn (log)
DRN-D-105	21.31	5.81	54,801,304	48,682.11M	From fyu/drn (log)
DPN-68	23.57	7.00	12,611,602	2,338.71M	From Cadene/pretrained...pytorch (log)
DPN-98	20.23	5.28	61,570,728	11,702.80M	From Cadene/pretrained...pytorch (log)
DPN-131	20.03	5.22	79,254,504	16,056.22M	From Cadene/pretrained...pytorch (log)
DarkNet Tiny	40.31	17.46	1,042,104	496.34M	Training (log)
DarkNet Ref	38.00	16.68	7,319,416	365.55M	Training (log)
SqueezeNet v1.0	38.73	17.34	1,248,424	828.30M	Training (log)
SqueezeNet v1.1	39.09	17.39	1,235,496	354.88M	Training (log)
SqueezeResNet v1.1	39.83	17.84	1,235,496	354.88M	Training (log)
ShuffleNetV2 x0.5	40.61	18.30	1,366,792	42.34M	Training (log)
ShuffleNetV2b x0.5	40.98	18.56	1,366,792	42.37M	Training (log)
ShuffleNetV2c x0.5	39.87	18.11	1,366,792	42.37M	From tensorpack/tensorpack (log)
ShuffleNetV2 x1.0	33.76	13.22	2,278,604	147.92M	Training (log)
ShuffleNetV2c x1.0	30.74	11.38	2,279,760	148.85M	From tensorpack/tensorpack (log)
ShuffleNetV2 x1.5	32.38	12.37	4,406,098	318.61M	Training (log)
ShuffleNetV2 x2.0	32.04	12.10	7,601,686	593.66M	Training (log)
108-MENet-8x1 (g=3)	43.62	20.30	654,516	40.64M	Training (log)
128-MENet-8x1 (g=4)	42.10	19.13	750,796	43.58M	Training (log)
228-MENet-12x1 (g=3)	33.86	12.89	1,806,568	148.93M	Training (log)
256-MENet-12x1 (g=4)	34.49	13.90	1,888,240	146.11M	From clavichord93/MENet (log)
348-MENet-12x1 (g=3)	31.17	11.41	3,368,128	306.31M	From clavichord93/MENet (log)
352-MENet-12x1 (g=8)	34.70	13.75	2,272,872	151.03M	From clavichord93/MENet (log)
456-MENet-24x1 (g=3)	29.57	10.43	5,304,784	560.72M	From clavichord93/MENet (log)
MobileNet x0.25	45.78	22.18	470,072	42.30M	Training (log)
MobileNet x0.5	36.12	14.81	1,331,592	152.04M	Training (log)
MobileNet x0.75	29.85	10.51	2,585,560	329.22M	Training (log)
MobileNet x1.0	29.25	10.03	4,231,976	573.83M	From dmlc/gluon-cv (log)
FD-MobileNet x0.25	56.19	31.38	383,160	12.44M	Training (log)
FD-MobileNet x0.5	42.62	19.69	993,928	40.93M	Training (log)
FD-MobileNet x1.0	34.42	13.73	2,901,288	146.08M	Training (log)
MobileNetV2 x0.25	48.89	25.24	1,516,392	32.22M	From dmlc/gluon-cv (log)
MobileNetV2 x0.5	35.51	14.64	1,964,736	95.62M	From dmlc/gluon-cv (log)
MobileNetV2 x0.75	30.82	11.26	2,627,592	191.61M	From dmlc/gluon-cv (log)
MobileNetV2 x1.0	28.51	9.90	3,504,960	320.19M	From dmlc/gluon-cv (log)
IGCV3	28.22	9.54	3,491,688	323.81M	From homles11/IGCV3 (log)
MnasNet	31.32	11.44	4,308,816	310.75M	From zeusees/Mnasnet...Model (log)
DARTS	27.23	8.97	4,718,752	537.64M	From quark0/darts (log)
Xception	20.99	5.56	22,855,952	8,385.86M	From Cadene/pretrained...pytorch (log)
InceptionV3	21.22	5.59	23,834,568	5,746.72M	From dmlc/gluon-cv (log)
InceptionV4	20.60	5.25	42,679,816	12,314.17M	From Cadene/pretrained...pytorch (log)
InceptionResNetV2	19.96	4.94	55,843,464	13,189.58M	From Cadene/pretrained...pytorch (log)
PolyNet	19.09	4.53	95,366,600	34,768.84M	From Cadene/pretrained...pytorch (log)
NASNet-A 4@1056	25.37	7.95	5,289,978	587.29M	From Cadene/pretrained...pytorch (log)
NASNet-A 6@4032	18.17	4.24	88,753,150	24,021.18M	From Cadene/pretrained...pytorch (log)
PNASNet-5-Large	17.90	4.28	86,057,668	25,169.47M	From Cadene/pretrained...pytorch (log)

For PyTorch

Model	Top1	Top5	Params	FLOPs	Remarks
AlexNet	43.48	20.93	61,100,840	715.49M	From dmlc/gluon-cv (log)
VGG-11	30.98	11.37	132,863,336	7,622.65M	From dmlc/gluon-cv (log)
VGG-13	30.07	10.75	133,047,848	11,326.85M	From dmlc/gluon-cv (log)
VGG-16	27.15	8.92	138,357,544	15,489.95M	From dmlc/gluon-cv (log)
VGG-19	26.19	8.39	143,667,240	19,653.05M	From dmlc/gluon-cv (log)
BN-VGG-11b	29.63	10.19	132,868,840	7,622.65M	From dmlc/gluon-cv (log)
BN-VGG-13b	28.41	9.63	133,053,736	11,326.85M	From dmlc/gluon-cv (log)
BN-VGG-16b	27.19	8.74	138,365,992	15,489.95M	From dmlc/gluon-cv (log)
BN-VGG-19b	26.06	8.40	143,678,248	19,653.05M	From dmlc/gluon-cv (log)
ResNet-10	37.46	15.85	5,418,792	892.62M	Converted from GL model (log)
ResNet-12	36.18	14.80	5,492,776	1,124.23M	Converted from GL model (log)
ResNet-14	33.17	12.71	5,788,200	1,355.64M	Converted from GL model (log)
ResNet-16	30.90	11.38	6,968,872	1,586.95M	Converted from GL model (log)
ResNet-18 x0.25	49.50	24.83	831,096	136.64M	Converted from GL model (log)
ResNet-18 x0.5	37.04	15.38	3,055,880	485.22M	Converted from GL model (log)
ResNet-18 x0.75	33.61	12.85	6,675,352	1,045.75M	Converted from GL model (log)
ResNet-18	29.52	10.21	11,689,512	1,818.21M	Converted from GL model (log)
ResNet-34	25.66	8.18	21,797,672	3,669.16M	From dmlc/gluon-cv (log)
ResNet-50	23.79	7.05	25,557,032	3,868.96M	From dmlc/gluon-cv (log)
ResNet-50b	23.05	6.65	25,557,032	4,100.70M	From dmlc/gluon-cv (log)
ResNet-101	21.90	6.22	44,549,160	7,586.30M	From dmlc/gluon-cv (log)
ResNet-101b	21.45	5.81	44,549,160	7,818.04M	From dmlc/gluon-cv (log)
ResNet-152	21.26	5.82	60,192,808	11,304.85M	From dmlc/gluon-cv (log)
ResNet-152b	20.74	5.50	60,192,808	11,536.58M	From dmlc/gluon-cv (log)
PreResNet-18	29.09	10.18	11,687,848	1,818.41M	Converted from GL model (log)
PreResNet-34	26.23	8.41	21,796,008	3,669.36M	From dmlc/gluon-cv (log)
PreResNet-50	23.70	6.85	25,549,480	3,869.16M	From dmlc/gluon-cv (log)
PreResNet-50b	23.33	6.87	25,549,480	4,100.90M	From dmlc/gluon-cv (log)
PreResNet-101	21.74	5.91	44,541,608	7,586.50M	From dmlc/gluon-cv (log)
PreResNet-101b	21.95	6.03	44,541,608	7,818.24M	From dmlc/gluon-cv (log)
PreResNet-152	20.94	5.55	60,185,256	11,305.05M	From dmlc/gluon-cv (log)
PreResNet-152b	21.34	5.91	60,185,256	11,536.78M	From dmlc/gluon-cv (log)
PreResNet-200b	21.33	5.88	64,666,280	15,040.27M	From tornadomeet/ResNet (log)
ResNeXt-101 (32x4d)	21.81	6.11	44,177,704	7,991.62M	From Cadene/pretrained...pytorch (log)
ResNeXt-101 (64x4d)	21.04	5.75	83,455,272	15,491.88M	From Cadene/pretrained...pytorch (log)
SE-ResNet-50	22.47	6.40	28,088,024	3,877.01M	From Cadene/pretrained...pytorch (log)
SE-ResNet-101	21.88	5.89	49,326,872	7,600.01M	From Cadene/pretrained...pytorch (log)
SE-ResNet-152	21.48	5.76	66,821,848	11,324.62M	From Cadene/pretrained...pytorch (log)
SE-ResNeXt-50 (32x4d)	21.00	5.54	27,559,896	4,253.33M	From Cadene/pretrained...pytorch (log)
SE-ResNeXt-101 (32x4d)	19.96	5.05	48,955,416	8,005.33M	From Cadene/pretrained...pytorch (log)
SENet-154	18.62	4.61	115,088,984	20,742.40M	From Cadene/pretrained...pytorch (log)
IBN-ResNet-50	22.76	6.41	25,557,032	4,100.70M	From XingangPan/IBN-Net (log)
IBN-ResNet-101	21.29	5.61	44,549,160	7,818.04M	From XingangPan/IBN-Net (log)
IBN(b)-ResNet-50	23.64	6.86	25,558,568	4,100.70M	From XingangPan/IBN-Net (log)
IBN-ResNeXt-101 (32x4d)	20.88	5.42	44,177,704	7,991.62M	From XingangPan/IBN-Net (log)
IBN-DenseNet-121	24.47	7.25	7,978,856	2,852.39M	From XingangPan/IBN-Net (log)
IBN-DenseNet-169	23.25	6.51	14,149,480	3,381.48M	From XingangPan/IBN-Net (log)
AirNet50-1x64d (r=2)	21.84	5.90	27,425,864	4,757.77M	From soeaver/AirNet-PyTorch (log)
AirNet50-1x64d (r=16)	22.11	6.19	25,714,952	4,385.54M	From soeaver/AirNet-PyTorch (log)
AirNeXt50-32x4d (r=2)	20.87	5.51	27,604,296	5,321.18M	From soeaver/AirNet-PyTorch (log)
BAM-ResNet-50	23.14	6.58	25,915,099	4,211.08M	From Jongchan/attention-module (log)
CBAM-ResNet-50	22.38	6.05	28,089,624	4,118.19M	From Jongchan/attention-module (log)
PyramidNet-101 (a=360)	21.98	6.20	42,455,070	8,706.81M	From dyhan0920/Pyramid...PyTorch (log)
DiracNetV2-18	31.47	11.70	11,511,784	1,798.43M	From szagoruyko/diracnets (log)
DiracNetV2-34	28.75	9.93	21,616,232	3,649.37M	From szagoruyko/diracnets (log)
DenseNet-121	25.57	8.03	7,978,856	2,852.39M	From dmlc/gluon-cv (log)
DenseNet-161	22.86	6.44	28,681,000	7,761.25M	From dmlc/gluon-cv (log)
DenseNet-169	24.40	7.19	14,149,480	3,381.48M	From dmlc/gluon-cv (log)
DenseNet-201	23.10	6.63	20,013,928	4,318.75M	From dmlc/gluon-cv (log)
CondenseNet-74 (C=G=4)	26.25	8.28	4,773,944	533.64M	From ShichenLiu/CondenseNet (log)
CondenseNet-74 (C=G=8)	28.93	10.06	2,935,416	278.55M	From ShichenLiu/CondenseNet (log)
WRN-50-2	22.53	6.41	68,849,128	11,412.82M	From szagoruyko/functional-zoo (log)
DRN-C-26	24.86	7.55	21,126,584	20,838.70M	From fyu/drn (log)
DRN-C-42	22.94	6.57	31,234,744	31,236.97M	From fyu/drn (log)
DRN-C-58	21.73	6.01	40,542,008	36,862.32M	From fyu/drn (log)
DRN-D-22	25.80	8.23	16,393,752	16,626.00M	From fyu/drn (log)
DRN-D-38	23.79	6.95	26,501,912	27,024.27M	From fyu/drn (log)
DRN-D-54	21.22	5.86	35,809,176	32,649.62M	From fyu/drn (log)
DRN-D-105	20.62	5.48	54,801,304	48,682.11M	From fyu/drn (log)
DPN-68	24.17	7.27	12,611,602	2,338.71M	From Cadene/pretrained...pytorch (log)
DPN-98	20.81	5.53	61,570,728	11,702.80M	From Cadene/pretrained...pytorch (log)
DPN-131	20.54	5.48	79,254,504	16,056.22M	From Cadene/pretrained...pytorch (log)
DarkNet Tiny	40.74	17.84	1,042,104	496.34M	Converted from GL model (log)
DarkNet Ref	38.58	17.18	7,319,416	365.55M	Converted from GL model (log)
SqueezeNet v1.0	39.29	17.66	1,248,424	828.30M	Converted from GL model (log)
SqueezeNet v1.1	39.31	17.72	1,235,496	354.88M	Converted from GL model (log)
SqueezeResNet v1.1	40.09	18.21	1,235,496	354.88M	Converted from GL model (log)
ShuffleNetV2 x0.5	40.99	18.65	1,366,792	42.34M	Converted from GL model (log)
ShuffleNetV2b x0.5	41.41	19.07	1,366,792	42.37M	Converted from GL model (log)
ShuffleNetV2c x0.5	40.31	18.51	1,366,792	42.37M	From tensorpack/tensorpack (log)
ShuffleNetV2 x1.0	34.26	13.54	2,278,604	147.92M	Converted from GL model (log)
ShuffleNetV2c x1.0	30.98	11.61	2,279,760	148.85M	From tensorpack/tensorpack (log)
ShuffleNetV2 x1.5	32.82	12.69	4,406,098	318.61M	Converted from GL model (log)
ShuffleNetV2 x2.0	32.45	12.49	7,601,686	593.66M	Converted from GL model (log)
108-MENet-8x1 (g=3)	43.94	20.76	654,516	40.64M	Converted from GL model (log)
128-MENet-8x1 (g=4)	42.43	19.59	750,796	43.58M	Converted from GL model (log)
228-MENet-12x1 (g=3)	34.11	13.16	1,806,568	148.93M	Converted from GL model (log)
256-MENet-12x1 (g=4)	33.41	13.26	1,888,240	146.11M	From clavichord93/MENet (log)
348-MENet-12x1 (g=3)	30.10	10.92	3,368,128	306.31M	From clavichord93/MENet (log)
352-MENet-12x1 (g=8)	33.31	13.08	2,272,872	151.03M	From clavichord93/MENet (log)
456-MENet-24x1 (g=3)	28.40	9.93	5,304,784	560.72M	From clavichord93/MENet (log)
MobileNet x0.25	46.26	22.49	470,072	42.30M	Converted from GL model (log)
MobileNet x0.5	36.30	15.14	1,331,592	152.04M	Converted from GL model (log)
MobileNet x0.75	30.14	10.76	2,585,560	329.22M	Converted from GL model (log)
MobileNet x1.0	29.86	10.36	4,231,976	573.83M	From dmlc/gluon-cv (log)
FD-MobileNet x0.25	55.77	31.32	383,160	12.44M	From clavichord93/FD-MobileNet (log)
FD-MobileNet x0.5	43.13	20.15	993,928	40.93M	Converted from GL model (log)
FD-MobileNet x1.0	34.70	14.05	2,901,288	146.08M	From clavichord93/FD-MobileNet (log)
MobileNetV2 x0.25	49.72	25.87	1,516,392	32.22M	From dmlc/gluon-cv (log)
MobileNetV2 x0.5	36.54	15.19	1,964,736	95.62M	From dmlc/gluon-cv (log)
MobileNetV2 x0.75	31.89	11.76	2,627,592	191.61M	From dmlc/gluon-cv (log)
MobileNetV2 x1.0	29.31	10.39	3,504,960	320.19M	From dmlc/gluon-cv (log)
IGCV3	28.40	9.84	3,491,688	323.81M	From homles11/IGCV3 (log)
MnasNet	31.58	11.74	4,308,816	310.75M	From zeusees/Mnasnet...Model (log)
DARTS	26.70	8.74	4,718,752	537.64M	From quark0/darts (log)
Xception	20.97	5.49	22,855,952	8,385.86M	From Cadene/pretrained...pytorch (log)
InceptionV3	21.12	5.65	23,834,568	5,746.72M	From dmlc/gluon-cv (log)
InceptionV4	20.64	5.29	42,679,816	12,314.17M	From Cadene/pretrained...pytorch (log)
InceptionResNetV2	19.93	4.90	55,843,464	13,189.58M	From Cadene/pretrained...pytorch (log)
PolyNet	19.10	4.52	95,366,600	34,768.84M	From Cadene/pretrained...pytorch (log)
NASNet-A 4@1056	25.68	8.16	5,289,978	587.29M	From Cadene/pretrained...pytorch (log)
NASNet-A 6@4032	18.14	4.21	88,753,150	24,021.18M	From Cadene/pretrained...pytorch (log)
PNASNet-5-Large	17.88	4.28	86,057,668	25,169.47M	From Cadene/pretrained...pytorch (log)

For Chainer

Model	Top1	Top5	Params	FLOPs	Remarks
AlexNet	44.08	21.32	61,100,840	715.49M	From dmlc/gluon-cv (log)
VGG-11	31.89	11.79	132,863,336	7,622.65M	From dmlc/gluon-cv (log)
VGG-13	31.06	11.16	133,047,848	11,326.85M	From dmlc/gluon-cv (log)
VGG-16	26.75	8.70	138,357,544	15,489.95M	From dmlc/gluon-cv (log)
VGG-19	25.86	8.23	143,667,240	19,653.05M	From dmlc/gluon-cv (log)
BN-VGG-11b	30.37	10.60	132,868,840	7,622.65M	From dmlc/gluon-cv (log)
BN-VGG-13b	29.45	10.19	133,053,736	11,326.85M	From dmlc/gluon-cv (log)
BN-VGG-16b	26.89	8.63	138,365,992	15,489.95M	From dmlc/gluon-cv (log)
BN-VGG-19b	25.65	8.16	143,678,248	19,653.05M	From dmlc/gluon-cv (log)
ResNet-10	37.12	15.49	5,418,792	892.62M	Converted from GL model (log)
ResNet-12	35.86	14.48	5,492,776	1,124.23M	Converted from GL model (log)
ResNet-14	32.84	12.42	5,788,200	1,355.64M	Converted from GL model (log)
ResNet-16	30.66	11.07	6,968,872	1,586.95M	Converted from GL model (log)
ResNet-18 x0.25	49.08	24.48	831,096	136.64M	Converted from GL model (log)
ResNet-18 x0.5	36.55	14.99	3,055,880	485.22M	Converted from GL model (log)
ResNet-18 x0.75	33.27	12.56	6,675,352	1,045.75M	Converted from GL model (log)
ResNet-18	29.08	9.97	11,689,512	1,818.21M	Converted from GL model (log)
ResNet-34	25.35	7.95	21,797,672	3,669.16M	From dmlc/gluon-cv (log)
ResNet-50	23.50	6.83	25,557,032	3,868.96M	From dmlc/gluon-cv (log)
ResNet-50b	22.93	6.46	25,557,032	4,100.70M	From dmlc/gluon-cv (log)
ResNet-101	21.65	6.01	44,549,160	7,586.30M	From dmlc/gluon-cv (log)
ResNet-101b	21.16	5.59	44,549,160	7,818.04M	From dmlc/gluon-cv (log)
ResNet-152	21.07	5.67	60,192,808	11,304.85M	From dmlc/gluon-cv (log)
ResNet-152b	20.44	5.39	60,192,808	11,536.58M	From dmlc/gluon-cv (log)
PreResNet-18	28.66	9.92	11,687,848	1,818.41M	Converted from GL model (log)
PreResNet-34	25.89	8.12	21,796,008	3,669.36M	From dmlc/gluon-cv (log)
PreResNet-50	23.36	6.69	25,549,480	3,869.16M	From dmlc/gluon-cv (log)
PreResNet-50b	23.08	6.67	25,549,480	4,100.90M	From dmlc/gluon-cv (log)
PreResNet-101	21.45	5.75	44,541,608	7,586.50M	From dmlc/gluon-cv (log)
PreResNet-101b	21.61	5.87	44,541,608	7,818.24M	From dmlc/gluon-cv (log)
PreResNet-152	20.73	5.30	60,185,256	11,305.05M	From dmlc/gluon-cv (log)
PreResNet-152b	20.88	5.66	60,185,256	11,536.78M	From dmlc/gluon-cv (log)
PreResNet-200b	21.03	5.60	64,666,280	15,040.27M	From tornadomeet/ResNet (log)
ResNeXt-101 (32x4d)	21.11	5.69	44,177,704	7,991.62M	From Cadene/pretrained...pytorch (log)
ResNeXt-101 (64x4d)	20.57	5.43	83,455,272	15,491.88M	From Cadene/pretrained...pytorch (log)
SE-ResNet-50	22.53	6.41	28,088,024	3,877.01M	From Cadene/pretrained...pytorch (log)
SE-ResNet-101	21.90	5.88	49,326,872	7,600.01M	From Cadene/pretrained...pytorch (log)
SE-ResNet-152	21.46	5.77	66,821,848	11,324.62M	From Cadene/pretrained...pytorch (log)
SE-ResNeXt-50 (32x4d)	21.04	5.58	27,559,896	4,253.33M	From Cadene/pretrained...pytorch (log)
SE-ResNeXt-101 (32x4d)	19.99	5.01	48,955,416	8,005.33M	From Cadene/pretrained...pytorch (log)
SENet-154	18.79	4.63	115,088,984	20,742.40M	From Cadene/pretrained...pytorch (log)
AirNet50-1x64d (r=2)	22.46	6.20	27,425,864	4,757.77M	From soeaver/AirNet-PyTorch (log)
AirNet50-1x64d (r=16)	22.89	6.50	25,714,952	4,385.54M	From soeaver/AirNet-PyTorch (log)
AirNeXt50-32x4d (r=2)	21.50	5.73	27,604,296	5,321.18M	From soeaver/AirNet-PyTorch (log)
BAM-ResNet-50	23.71	6.97	25,915,099	4,211.08M	From Jongchan/attention-module (log)
CBAM-ResNet-50	22.99	6.40	28,089,624	4,118.19M	From Jongchan/attention-module (log)
PyramidNet-101 (a=360)	22.66	6.49	42,455,070	8,706.81M	From dyhan0920/Pyramid...PyTorch (log)
DiracNetV2-18	30.60	11.13	11,511,784	1,798.43M	From szagoruyko/diracnets (log)
DiracNetV2-34	27.90	9.48	21,616,232	3,649.37M	From szagoruyko/diracnets (log)
DenseNet-121	25.04	7.79	7,978,856	2,852.39M	From dmlc/gluon-cv (log)
DenseNet-161	22.36	6.20	28,681,000	7,761.25M	From dmlc/gluon-cv (log)
DenseNet-169	23.85	6.86	14,149,480	3,381.48M	From dmlc/gluon-cv (log)
DenseNet-201	22.64	6.29	20,013,928	4,318.75M	From dmlc/gluon-cv (log)
CondenseNet-74 (C=G=4)	26.81	8.61	4,773,944	533.64M	From ShichenLiu/CondenseNet (log)
CondenseNet-74 (C=G=8)	29.74	10.43	2,935,416	278.55M	From ShichenLiu/CondenseNet (log)
WRN-50-2	22.06	6.13	68,849,128	11,412.82M	From szagoruyko/functional-zoo (log)
DRN-C-26	25.68	7.88	21,126,584	20,838.70M	From fyu/drn (log)
DRN-C-42	23.72	6.93	31,234,744	31,236.97M	From fyu/drn (log)
DRN-C-58	22.35	6.29	40,542,008	36,862.32M	From fyu/drn (log)
DRN-D-22	26.65	8.50	16,393,752	16,626.00M	From fyu/drn (log)
DRN-D-38	24.53	7.36	26,501,912	27,024.27M	From fyu/drn (log)
DRN-D-54	22.08	6.23	35,809,176	32,649.62M	From fyu/drn (log)
DRN-D-105	21.32	5.84	54,801,304	48,682.11M	From fyu/drn (log)
DPN-68	23.61	7.01	12,611,602	2,338.71M	From Cadene/pretrained...pytorch (log)
DPN-98	20.80	5.53	61,570,728	11,702.80M	From Cadene/pretrained...pytorch (log)
DPN-131	20.04	5.23	79,254,504	16,056.22M	From Cadene/pretrained...pytorch (log)
DarkNet Tiny	40.33	17.46	1,042,104	496.34M	Converted from GL model (log)
DarkNet Ref	38.09	16.71	7,319,416	365.55M	Converted from GL model (log)
SqueezeNet v1.0	38.76	17.38	1,248,424	828.30M	Converted from GL model (log)
SqueezeNet v1.1	39.13	17.40	1,235,496	354.88M	Converted from GL model (log)
SqueezeResNet v1.1	39.85	17.87	1,235,496	354.88M	Converted from GL model (log)
ShuffleNetV2 x0.5	43.45	20.73	1,366,792	42.34M	Converted from GL model (log)
ShuffleNetV2b x0.5	40.95	18.56	1,366,792	42.37M	Converted from GL model (log)
ShuffleNetV2c x0.5	39.82	18.14	1,366,792	42.37M	From tensorpack/tensorpack (log)
ShuffleNetV2 x1.0	35.69	14.71	2,278,604	147.92M	Converted from GL model (log)
ShuffleNetV2c x1.0	30.74	11.37	2,279,760	148.85M	From tensorpack/tensorpack (log)
ShuffleNetV2 x1.5	33.96	13.37	4,406,098	318.61M	Converted from GL model (log)
ShuffleNetV2 x2.0	33.21	13.03	7,601,686	593.66M	Converted from GL model (log)
108-MENet-8x1 (g=3)	43.67	20.42	654,516	40.64M	Converted from GL model (log)
128-MENet-8x1 (g=4)	42.07	19.19	750,796	43.58M	Converted from GL model (log)
228-MENet-12x1 (g=3)	33.86	13.01	1,806,568	148.93M	Converted from GL model (log)
256-MENet-12x1 (g=4)	34.44	13.91	1,888,240	146.11M	From clavichord93/MENet (log)
348-MENet-12x1 (g=3)	31.14	11.40	3,368,128	306.31M	From clavichord93/MENet (log)
352-MENet-12x1 (g=8)	34.62	13.68	2,272,872	151.03M	From clavichord93/MENet (log)
456-MENet-24x1 (g=3)	29.55	10.39	5,304,784	560.72M	From clavichord93/MENet (log)
MobileNet x0.25	45.85	22.16	470,072	42.30M	Converted from GL model (log)
MobileNet x0.5	36.15	14.86	1,331,592	152.04M	Converted from GL model (log)
MobileNet x0.75	29.86	10.53	2,585,560	329.22M	Converted from GL model (log)
MobileNet x1.0	29.71	10.31	4,231,976	573.83M	From dmlc/gluon-cv (log)
FD-MobileNet x0.25	56.11	31.45	383,160	12.44M	Converted from GL model (log)
FD-MobileNet x0.5	42.68	19.76	993,928	40.93M	Converted from GL model (log)
FD-MobileNet x1.0	34.44	13.74	2,901,288	146.08M	Converted from GL model (log)
MobileNetV2 x0.25	49.11	25.49	1,516,392	32.22M	From dmlc/gluon-cv (log)
MobileNetV2 x0.5	35.96	14.98	1,964,736	95.62M	From dmlc/gluon-cv (log)
MobileNetV2 x0.75	31.28	11.48	2,627,592	191.61M	From dmlc/gluon-cv (log)
MobileNetV2 x1.0	28.87	10.05	3,504,960	320.19M	From dmlc/gluon-cv (log)
IGCV3	28.20	9.55	3,491,688	323.81M	From homles11/IGCV3 (log)
MnasNet	31.27	11.44	4,308,816	310.75M	From zeusees/Mnasnet...Model (log)
DARTS	27.29	8.97	4,718,752	537.64M	From quark0/darts (log)
Xception	21.04	5.47	22,855,952	8,385.86M	From Cadene/pretrained...pytorch (log)
InceptionV3	21.11	5.61	23,834,568	5,746.72M	From dmlc/gluon-cv (log)
InceptionV4	20.62	5.26	42,679,816	12,314.17M	From Cadene/pretrained...pytorch (log)
InceptionResNetV2	19.93	4.92	55,843,464	13,189.58M	From Cadene/pretrained...pytorch (log)
PolyNet	19.08	4.50	95,366,600	34,768.84M	From Cadene/pretrained...pytorch (log)
NASNet-A 4@1056	25.36	7.96	5,289,978	587.29M	From Cadene/pretrained...pytorch (log)
NASNet-A 6@4032	18.17	4.22	88,753,150	24,021.18M	From Cadene/pretrained...pytorch (log)
PNASNet-5-Large	17.90	4.26	86,057,668	25,169.47M	From Cadene/pretrained...pytorch (log)

For Keras

Model	Top1	Top5	Params	FLOPs	Remarks
AlexNet	44.10	21.26	61,100,840	715.49M	From dmlc/gluon-cv (log)
VGG-11	31.90	11.75	132,863,336	7,622.65M	From dmlc/gluon-cv (log)
VGG-13	31.06	11.12	133,047,848	11,326.85M	From dmlc/gluon-cv (log)
VGG-16	26.78	8.69	138,357,544	15,489.95M	From dmlc/gluon-cv (log)
VGG-19	25.87	8.23	143,667,240	19,653.05M	From dmlc/gluon-cv (log)
BN-VGG-11b	30.34	10.57	132,868,840	7,622.65M	From dmlc/gluon-cv (log)
BN-VGG-13b	29.48	10.16	133,053,736	11,326.85M	From dmlc/gluon-cv (log)
BN-VGG-16b	26.88	8.65	138,365,992	15,489.95M	From dmlc/gluon-cv (log)
BN-VGG-19b	25.65	8.14	143,678,248	19,653.05M	From dmlc/gluon-cv (log)
ResNet-10	37.09	15.54	5,418,792	892.62M	Converted from GL model (log)
ResNet-12	35.86	14.45	5,492,776	1,124.23M	Converted from GL model (log)
ResNet-14	32.85	12.42	5,788,200	1,355.64M	Converted from GL model (log)
ResNet-16	30.67	11.09	6,968,872	1,586.95M	Converted from GL model (log)
ResNet-18 x0.25	49.14	24.45	831,096	136.64M	Converted from GL model (log)
ResNet-18 x0.5	36.54	14.96	3,055,880	485.22M	Converted from GL model (log)
ResNet-18 x0.75	33.24	12.54	6,675,352	1,045.75M	Converted from GL model (log)
ResNet-18	29.13	9.94	11,689,512	1,818.21M	Converted from GL model (log)
ResNet-34	25.32	7.92	21,797,672	3,669.16M	From dmlc/gluon-cv (log)
ResNet-50	23.49	6.87	25,557,032	3,868.96M	From dmlc/gluon-cv (log)
ResNet-50b	22.90	6.44	25,557,032	4,100.70M	From dmlc/gluon-cv (log)
ResNet-101	21.64	5.99	44,549,160	7,586.30M	From dmlc/gluon-cv (log)
ResNet-101b	21.17	5.60	44,549,160	7,818.04M	From dmlc/gluon-cv (log)
ResNet-152	21.00	5.61	60,192,808	11,304.85M	From dmlc/gluon-cv (log)
ResNet-152b	20.53	5.37	60,192,808	11,536.58M	From dmlc/gluon-cv (log)
PreResNet-18	28.72	9.88	11,687,848	1,818.41M	Converted from GL model (log)
PreResNet-34	25.86	8.11	21,796,008	3,669.36M	From dmlc/gluon-cv (log)
PreResNet-50	23.38	6.68	25,549,480	3,869.16M	From dmlc/gluon-cv (log)
PreResNet-50b	23.14	6.63	25,549,480	4,100.90M	From dmlc/gluon-cv (log)
PreResNet-101	21.43	5.75	44,541,608	7,586.50M	From dmlc/gluon-cv (log)
PreResNet-101b	21.71	5.88	44,541,608	7,818.24M	From dmlc/gluon-cv (log)
PreResNet-152	20.69	5.31	60,185,256	11,305.05M	From dmlc/gluon-cv (log)
PreResNet-152b	20.99	5.76	60,185,256	11,536.78M	From dmlc/gluon-cv (log)
PreResNet-200b	21.09	5.64	64,666,280	15,040.27M	From tornadomeet/ResNet (log)
ResNeXt-101 (32x4d)	21.30	5.78	44,177,704	7,991.62M	From Cadene/pretrained...pytorch (log)
ResNeXt-101 (64x4d)	20.59	5.41	83,455,272	15,491.88M	From Cadene/pretrained...pytorch (log)
SE-ResNet-50	22.50	6.43	28,088,024	3,877.01M	From Cadene/pretrained...pytorch (log)
SE-ResNet-101	21.92	5.88	49,326,872	7,600.01M	From Cadene/pretrained...pytorch (log)
SE-ResNet-152	21.46	5.77	66,821,848	11,324.62M	From Cadene/pretrained...pytorch (log)
SE-ResNeXt-50 (32x4d)	21.05	5.57	27,559,896	4,253.33M	From Cadene/pretrained...pytorch (log)
SE-ResNeXt-101 (32x4d)	19.98	4.99	48,955,416	8,005.33M	From Cadene/pretrained...pytorch (log)
SENet-154	18.83	4.65	115,088,984	20,742.40M	From Cadene/pretrained...pytorch (log)
DenseNet-121	25.09	7.80	7,978,856	2,852.39M	From dmlc/gluon-cv (log)
DenseNet-161	22.39	6.18	28,681,000	7,761.25M	From dmlc/gluon-cv (log)
DenseNet-169	23.88	6.89	14,149,480	3,381.48M	From dmlc/gluon-cv (log)
DenseNet-201	22.69	6.35	20,013,928	4,318.75M	From dmlc/gluon-cv (log)
DarkNet Tiny	40.31	17.46	1,042,104	496.34M	Converted from GL model (log)
DarkNet Ref	37.99	16.68	7,319,416	365.55M	Converted from GL model (log)
SqueezeNet v1.0	39.17	17.56	1,248,424	828.30M	Converted from GL model (log)
SqueezeNet v1.1	39.08	17.39	1,235,496	354.88M	Converted from GL model (log)
SqueezeResNet v1.1	39.82	17.84	1,235,496	354.88M	Converted from GL model (log)
ShuffleNetV2 x0.5	40.76	18.40	1,366,792	42.34M	Converted from GL model (log)
ShuffleNetV2 x1.0	33.79	13.38	2,278,604	147.92M	Converted from GL model (log)
ShuffleNetV2 x1.5	32.46	12.47	4,406,098	318.61M	Converted from GL model (log)
ShuffleNetV2 x2.0	31.91	12.23	7,601,686	593.66M	Converted from GL model (log)
108-MENet-8x1 (g=3)	43.61	20.31	654,516	40.64M	Converted from GL model (log)
128-MENet-8x1 (g=4)	42.08	19.14	750,796	43.58M	Converted from GL model (log)
228-MENet-12x1 (g=3)	33.85	12.88	1,806,568	148.93M	Converted from GL model (log)
256-MENet-12x1 (g=4)	34.48	13.91	1,888,240	146.11M	From clavichord93/MENet (log)
348-MENet-12x1 (g=3)	31.17	11.42	3,368,128	306.31M	From clavichord93/MENet (log)
352-MENet-12x1 (g=8)	34.69	13.75	2,272,872	151.03M	From clavichord93/MENet (log)
456-MENet-24x1 (g=3)	29.55	10.44	5,304,784	560.72M	From clavichord93/MENet (log)
MobileNet x0.25	45.80	22.17	470,072	42.30M	Converted from GL model (log)
MobileNet x0.5	36.11	14.81	1,331,592	152.04M	Converted from GL model (log)
MobileNet x0.75	29.85	10.51	2,585,560	329.22M	Converted from GL model (log)
MobileNet x1.0	29.24	10.03	4,231,976	573.83M	From dmlc/gluon-cv (log)
FD-MobileNet x0.25	56.17	31.37	383,160	12.44M	Converted from GL model (log)
FD-MobileNet x0.5	42.61	19.69	993,928	40.93M	Converted from GL model (log)
FD-MobileNet x1.0	34.42	13.74	2,901,288	146.08M	Converted from GL model (log)
MobileNetV2 x0.25	48.86	25.24	1,516,392	32.22M	From dmlc/gluon-cv (log)
MobileNetV2 x0.5	35.51	14.65	1,964,736	95.62M	From dmlc/gluon-cv (log)
MobileNetV2 x0.75	30.81	11.26	2,627,592	191.61M	From dmlc/gluon-cv (log)
MobileNetV2 x1.0	28.50	9.90	3,504,960	320.19M	From dmlc/gluon-cv (log)
IGCV3	28.21	9.55	3,491,688	323.81M	From homles11/IGCV3 (log)
MnasNet	31.30	11.45	4,308,816	310.75M	From zeusees/Mnasnet...Model (log)

For TensorFlow

Model	Top1	Top5	Params	FLOPs	Remarks
AlexNet	44.07	21.32	61,100,840	715.49M	From dmlc/gluon-cv (log)
VGG-11	31.89	11.73	132,863,336	7,622.65M	From dmlc/gluon-cv (log)
VGG-13	31.03	11.15	133,047,848	11,326.85M	From dmlc/gluon-cv (log)
VGG-16	26.77	8.68	138,357,544	15,489.95M	From dmlc/gluon-cv (log)
VGG-19	25.93	8.23	143,667,240	19,653.05M	From dmlc/gluon-cv (log)
BN-VGG-11b	30.34	10.58	132,868,840	7,622.65M	From dmlc/gluon-cv (log)
BN-VGG-13b	29.47	10.15	133,053,736	11,326.85M	From dmlc/gluon-cv (log)
BN-VGG-16b	26.83	8.66	138,365,992	15,489.95M	From dmlc/gluon-cv (log)
BN-VGG-19b	25.62	8.17	143,678,248	19,653.05M	From dmlc/gluon-cv (log)
ResNet-10	37.11	15.52	5,418,792	892.62M	Converted from GL model (log)
ResNet-12	35.82	14.50	5,492,776	1,124.23M	Converted from GL model (log)
ResNet-14	32.83	12.45	5,788,200	1,355.64M	Converted from GL model (log)
ResNet-16	30.66	11.05	6,968,872	1,586.95M	Converted from GL model (log)
ResNet-18 x0.25	49.12	24.50	831,096	136.64M	Converted from GL model (log)
ResNet-18 x0.5	36.51	14.93	3,055,880	485.22M	Converted from GL model (log)
ResNet-18 x0.75	33.28	12.50	6,675,352	1,045.75M	Converted from GL model (log)
ResNet-18	29.10	9.99	11,689,512	1,818.21M	Converted from GL model (log)
ResNet-34	25.32	7.93	21,797,672	3,669.16M	From dmlc/gluon-cv (log)
ResNet-50	23.48	6.87	25,557,032	3,868.96M	From dmlc/gluon-cv (log)
ResNet-50b	22.97	6.48	25,557,032	4,100.70M	From dmlc/gluon-cv (log)
ResNet-101	21.61	6.01	44,549,160	7,586.30M	From dmlc/gluon-cv (log)
ResNet-101b	21.22	5.57	44,549,160	7,818.04M	From dmlc/gluon-cv (log)
ResNet-152	20.99	5.59	60,192,808	11,304.85M	From dmlc/gluon-cv (log)
ResNet-152b	20.55	5.35	60,192,808	11,536.58M	From dmlc/gluon-cv (log)
PreResNet-18	28.75	9.88	11,687,848	1,818.41M	Converted from GL model (log)
PreResNet-34	25.82	8.08	21,796,008	3,669.36M	From dmlc/gluon-cv (log)
PreResNet-50	23.42	6.68	25,549,480	3,869.16M	From dmlc/gluon-cv (log)
PreResNet-50b	23.12	6.61	25,549,480	4,100.90M	From dmlc/gluon-cv (log)
PreResNet-101	21.49	5.72	44,541,608	7,586.50M	From dmlc/gluon-cv (log)
PreResNet-101b	21.70	5.91	44,541,608	7,818.24M	From dmlc/gluon-cv (log)
PreResNet-152	20.63	5.29	60,185,256	11,305.05M	From dmlc/gluon-cv (log)
PreResNet-152b	20.95	5.76	60,185,256	11,536.78M	From dmlc/gluon-cv (log)
PreResNet-200b	21.12	5.60	64,666,280	15,040.27M	From tornadomeet/ResNet (log)
ResNeXt-101 (32x4d)	21.33	5.80	44,177,704	7,991.62M	From Cadene/pretrained...pytorch (log)
ResNeXt-101 (64x4d)	20.59	5.43	83,455,272	15,491.88M	From Cadene/pretrained...pytorch (log)
SE-ResNet-50	22.53	6.43	28,088,024	3,877.01M	From Cadene/pretrained...pytorch (log)
SE-ResNet-101	21.92	5.89	49,326,872	7,600.01M	From Cadene/pretrained...pytorch (log)
SE-ResNet-152	21.48	5.78	66,821,848	11,324.62M	From Cadene/pretrained...pytorch (log)
SE-ResNeXt-50 (32x4d)	21.01	5.53	27,559,896	4,253.33M	From Cadene/pretrained...pytorch (log)
SE-ResNeXt-101 (32x4d)	19.99	4.97	48,955,416	8,005.33M	From Cadene/pretrained...pytorch (log)
SENet-154	18.77	4.63	115,088,984	20,742.40M	From Cadene/pretrained...pytorch (log)
DenseNet-121	25.16	7.82	7,978,856	2,852.39M	From dmlc/gluon-cv (log)
DenseNet-161	22.40	6.17	28,681,000	7,761.25M	From dmlc/gluon-cv (log)
DenseNet-169	23.93	6.87	14,149,480	3,381.48M	From dmlc/gluon-cv (log)
DenseNet-201	22.70	6.35	20,013,928	4,318.75M	From dmlc/gluon-cv (log)
DarkNet Tiny	40.35	17.51	1,042,104	496.34M	Converted from GL model (log)
DarkNet Ref	37.99	16.72	7,319,416	365.55M	Converted from GL model (log)
SqueezeNet v1.0	39.18	17.58	1,248,424	828.30M	Converted from GL model (log)
SqueezeNet v1.1	39.14	17.39	1,235,496	354.88M	Converted from GL model (log)
SqueezeResNet v1.1	39.75	17.92	1,235,496	354.88M	Converted from GL model (log)
ShuffleNetV2 x0.5	40.88	18.44	1,366,792	42.34M	Converted from GL model (log)
ShuffleNetV2b x0.5	41.03	18.59	1,366,792	42.37M	Converted from GL model (log)
ShuffleNetV2c x0.5	39.93	18.11	1,366,792	42.37M	From tensorpack/tensorpack (log)
ShuffleNetV2 x1.0	33.81	13.40	2,278,604	147.92M	Converted from GL model (log)
ShuffleNetV2c x1.0	30.77	11.39	2,279,760	148.85M	From tensorpack/tensorpack (log)
ShuffleNetV2 x1.5	32.51	12.50	4,406,098	318.61M	Converted from GL model (log)
ShuffleNetV2 x2.0	31.99	12.26	7,601,686	593.66M	Converted from GL model (log)
108-MENet-8x1 (g=3)	43.67	20.32	654,516	40.64M	Converted from GL model (log)
128-MENet-8x1 (g=4)	42.04	19.15	750,796	43.58M	Converted from GL model (log)
228-MENet-12x1 (g=3)	33.85	12.92	1,806,568	148.93M	Converted from GL model (log)
256-MENet-12x1 (g=4)	34.48	13.95	1,888,240	146.11M	From clavichord93/MENet (log)
348-MENet-12x1 (g=3)	31.19	11.41	3,368,128	306.31M	From clavichord93/MENet (log)
352-MENet-12x1 (g=8)	34.65	13.71	2,272,872	151.03M	From clavichord93/MENet (log)
456-MENet-24x1 (g=3)	29.56	10.46	5,304,784	560.72M	From clavichord93/MENet (log)
MobileNet x0.25	45.78	22.21	470,072	42.30M	Converted from GL model (log)
MobileNet x0.5	36.18	14.84	1,331,592	152.04M	Converted from GL model (log)
MobileNet x0.75	29.82	10.49	2,585,560	329.22M	Converted from GL model (log)
MobileNet x1.0	29.30	10.04	4,231,976	573.83M	From dmlc/gluon-cv (log)
FD-MobileNet x0.25	56.08	31.44	383,160	12.44M	Converted from GL model (log)
FD-MobileNet x0.5	42.67	19.70	993,928	40.93M	Converted from GL model (log)
FD-MobileNet x1.0	34.47	13.74	2,901,288	146.08M	Converted from GL model (log)
MobileNetV2 x0.25	48.87	25.26	1,516,392	32.22M	From dmlc/gluon-cv (log)
MobileNetV2 x0.5	35.51	14.60	1,964,736	95.62M	From dmlc/gluon-cv (log)
MobileNetV2 x0.75	30.79	11.24	2,627,592	191.61M	From dmlc/gluon-cv (log)
MobileNetV2 x1.0	28.53	9.90	3,504,960	320.19M	From dmlc/gluon-cv (log)
IGCV3	28.17	9.55	3,491,688	323.81M	From homles11/IGCV3 (log)
MnasNet	31.29	11.44	4,308,816	310.75M	From zeusees/Mnasnet...Model (log)

Name		Name	Last commit message	Last commit date
Latest commit History 931 Commits
chainer_		chainer_
common		common
datasets		datasets
gluon		gluon
keras_		keras_
pytorch		pytorch
tensorflow_		tensorflow_
tests		tests
.gitignore		.gitignore
.travis.yml		.travis.yml
LICENSE		LICENSE
README.md		README.md
__init__.py		__init__.py
convert_models.py		convert_models.py
eval_ch.py		eval_ch.py
eval_gl.py		eval_gl.py
eval_gl_khpa.py		eval_gl_khpa.py
eval_ke.py		eval_ke.py
eval_pt.py		eval_pt.py
eval_tf.py		eval_tf.py
requirements.txt		requirements.txt
train_ch.py		train_ch.py
train_gl.py		train_gl.py
train_gl_khpa.py		train_gl_khpa.py
train_ke.py		train_ke.py
train_pt.py		train_pt.py
train_tf.py		train_tf.py

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Repository files navigation

Large-scale image classification networks for embedded systems

List of models

Installation

For Gluon way

For PyTorch way

For Chainer way

For Keras way

For TensorFlow way

For research

Usage

For Gluon way

For PyTorch way

For Chainer way

For Keras way

For TensorFlow way

Pretrained models

For Gluon

For PyTorch

For Chainer

For Keras

For TensorFlow

About

Releases

Packages

Languages

License

earhian/imgclsmob

Folders and files

Latest commit

History

Repository files navigation

Large-scale image classification networks for embedded systems

List of models

Installation

For Gluon way

For PyTorch way

For Chainer way

For Keras way

For TensorFlow way

For research

Usage

For Gluon way

For PyTorch way

For Chainer way

For Keras way

For TensorFlow way

Pretrained models

For Gluon

For PyTorch

For Chainer

For Keras

For TensorFlow

About

Resources

License

Stars

Watchers

Forks

Releases

Packages 0

Languages

Packages