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Image classification models for Keras

Project description

Large-scale image classification models on Keras with MXNet backend

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This is a collection of large-scale image classification models. Many of them are pretrained on ImageNet-1K dataset and loaded automatically during use. All pretrained models require the same ordinary normalization. Scripts for training/evaluating/converting models are in the imgclsmob repo.

List of implemented models

Installation

To use the 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 image_data_format field to channels_first in the file ~/.keras/keras.json. Also check that the backend field is set to mxnet.

Usage

Example of using the pretrained ResNet-18 model:

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)

Pretrained models

Some remarks:

  • Top1/Top5 are the standard 1-crop Top-1/Top-5 errors (in percents) on the validation subset of the ImageNet-1K dataset.
  • FLOPs/2 is the number of FLOPs divided by two to be similar to the number of MACs.
  • Remark Converted from GL model means that the model was trained on MXNet/Gluon and then converted to 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 42.28 18.62 724,056 287.28M Converted from GL model (log)
1.0-SqNxt-23v5 40.38 17.57 921,816 285.82M 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)
ShuffleNet x0.5 (g=3) 43.82 20.60 718,324 41.70M 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 35.63 14.43 1,964,736 100.13M Converted from GL model (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)

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