Metadata-Version: 2.1
Name: gluoncv2
Version: 0.0.39
Summary: Image classification models for Gluon
Home-page: https://github.com/osmr/imgclsmob
Author: Oleg Sémery
Author-email: osemery@gmail.com
License: MIT
Keywords: machine-learning deep-learning neuralnetwork image-classification imagenet mxnet gluon vgg resnet pyramidnet diracnet densenet condensenet wrn drn dpn darknet fishnet espnetv2 xdensnet squeezenet squeezenext shufflenet menet mobilenet igcv3 mnasnet darts xception inception polynet nasnet pnasnet ror
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Description-Content-Type: text/markdown
Requires-Dist: numpy

# Image classification models on MXNet/Gluon

[![PyPI](https://img.shields.io/pypi/v/gluoncv2.svg)](https://pypi.python.org/pypi/gluoncv2)
[![Downloads](https://pepy.tech/badge/gluoncv2)](https://pepy.tech/project/gluoncv2)

This is a collection of image classification models. Many of them are pretrained on ImageNet-1K and CIFAR-10/100
datasets and loaded automatically during use. All pretrained models require the same ordinary normalization.
Scripts for training/evaluating/converting models are in the [`imgclsmob`](https://github.com/osmr/imgclsmob) repo.

## List of implemented models

- AlexNet (['One weird trick for parallelizing convolutional neural networks'](https://arxiv.org/abs/1404.5997))
- ZFNet (['Visualizing and Understanding Convolutional Networks'](https://arxiv.org/abs/1311.2901))
- VGG/BN-VGG (['Very Deep Convolutional Networks for Large-Scale Image Recognition'](https://arxiv.org/abs/1409.1556))
- BN-Inception (['Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift'](https://arxiv.org/abs/1502.03167))
- ResNet (['Deep Residual Learning for Image Recognition'](https://arxiv.org/abs/1512.03385))
- PreResNet (['Identity Mappings in Deep Residual Networks'](https://arxiv.org/abs/1603.05027))
- ResNeXt (['Aggregated Residual Transformations for Deep Neural Networks'](http://arxiv.org/abs/1611.05431))
- SENet/SE-ResNet/SE-PreResNet/SE-ResNeXt (['Squeeze-and-Excitation Networks'](https://arxiv.org/abs/1709.01507))
- IBN-ResNet/IBN-ResNeXt/IBN-DenseNet (['Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net'](https://arxiv.org/abs/1807.09441))
- AirNet/AirNeXt (['Attention Inspiring Receptive-Fields Network for Learning Invariant Representations'](https://ieeexplore.ieee.org/document/8510896))
- BAM-ResNet (['BAM: Bottleneck Attention Module'](https://arxiv.org/abs/1807.06514))
- CBAM-ResNet (['CBAM: Convolutional Block Attention Module'](https://arxiv.org/abs/1807.06521))
- ResAttNet (['Residual Attention Network for Image Classification'](https://arxiv.org/abs/1704.06904))
- PyramidNet (['Deep Pyramidal Residual Networks'](https://arxiv.org/abs/1610.02915))
- DiracNetV2 (['DiracNets: Training Very Deep Neural Networks Without Skip-Connections'](https://arxiv.org/abs/1706.00388))
- ShaResNet (['ShaResNet: reducing residual network parameter number by sharing weights'](https://arxiv.org/abs/1702.08782))
- CRU-Net (['Sharing Residual Units Through Collective Tensor Factorization To Improve Deep Neural Networks'](https://www.ijcai.org/proceedings/2018/88))
- DenseNet (['Densely Connected Convolutional Networks'](https://arxiv.org/abs/1608.06993))
- CondenseNet (['CondenseNet: An Efficient DenseNet using Learned Group Convolutions'](https://arxiv.org/abs/1711.09224))
- SparseNet (['Sparsely Aggregated Convolutional Networks'](https://arxiv.org/abs/1801.05895))
- PeleeNet (['Pelee: A Real-Time Object Detection System on Mobile Devices'](https://arxiv.org/abs/1804.06882))
- WRN (['Wide Residual Networks'](https://arxiv.org/abs/1605.07146))
- DRN-C/DRN-D (['Dilated Residual Networks'](https://arxiv.org/abs/1705.09914))
- DPN (['Dual Path Networks'](https://arxiv.org/abs/1707.01629))
- DarkNet Ref/Tiny/19 (['Darknet: Open source neural networks in c'](https://github.com/pjreddie/darknet))
- DarkNet-53 (['YOLOv3: An Incremental Improvement'](https://arxiv.org/abs/1804.02767))
- ChannelNet (['ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions'](https://arxiv.org/abs/1809.01330))
- DLA (['Deep Layer Aggregation'](https://arxiv.org/abs/1707.06484))
- MSDNet (['Multi-Scale Dense Networks for Resource Efficient Image Classification'](https://arxiv.org/abs/1703.09844))
- FishNet (['FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction'](http://papers.nips.cc/paper/7356-fishnet-a-versatile-backbone-for-image-region-and-pixel-level-prediction.pdf))
- ESPNetv2 (['ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network'](https://arxiv.org/abs/1811.11431))
- X-DenseNet (['Deep Expander Networks: Efficient Deep Networks from Graph Theory'](https://arxiv.org/abs/1711.08757))
- SqueezeNet/SqueezeResNet (['SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size'](https://arxiv.org/abs/1602.07360))
- SqueezeNext (['SqueezeNext: Hardware-Aware Neural Network Design'](https://arxiv.org/abs/1803.10615))
- ShuffleNet (['ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices'](https://arxiv.org/abs/1707.01083))
- ShuffleNetV2 (['ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design'](https://arxiv.org/abs/1807.11164))
- MENet (['Merging and Evolution: Improving Convolutional Neural Networks for Mobile Applications'](https://arxiv.org/abs/1803.09127))
- MobileNet (['MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications'](https://arxiv.org/abs/1704.04861))
- FD-MobileNet (['FD-MobileNet: Improved MobileNet with A Fast Downsampling Strategy'](https://arxiv.org/abs/1802.03750))
- MobileNetV2 (['MobileNetV2: Inverted Residuals and Linear Bottlenecks'](https://arxiv.org/abs/1801.04381))
- IGCV3 (['IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks'](https://arxiv.org/abs/1806.00178))
- MnasNet (['MnasNet: Platform-Aware Neural Architecture Search for Mobile'](https://arxiv.org/abs/1807.11626))
- DARTS (['DARTS: Differentiable Architecture Search'](https://arxiv.org/abs/1806.09055))
- Xception (['Xception: Deep Learning with Depthwise Separable Convolutions'](https://arxiv.org/abs/1610.02357))
- InceptionV3 (['Rethinking the Inception Architecture for Computer Vision'](https://arxiv.org/abs/1512.00567))
- InceptionV4/InceptionResNetV2 (['Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning'](https://arxiv.org/abs/1602.07261))
- PolyNet (['PolyNet: A Pursuit of Structural Diversity in Very Deep Networks'](https://arxiv.org/abs/1611.05725))
- NASNet (['Learning Transferable Architectures for Scalable Image Recognition'](https://arxiv.org/abs/1707.07012))
- PNASNet (['Progressive Neural Architecture Search'](https://arxiv.org/abs/1712.00559))
- NIN (['Network In Network'](https://arxiv.org/abs/1312.4400))
- RoR-3 (['Residual Networks of Residual Networks: Multilevel Residual Networks'](https://arxiv.org/abs/1608.02908))
- RiR (['Resnet in Resnet: Generalizing Residual Architectures'](https://arxiv.org/abs/1603.08029))
- ResDrop-ResNet (['Deep Networks with Stochastic Depth'](https://arxiv.org/abs/1603.09382))
- Shake-Shake-ResNet (['Shake-Shake regularization'](https://arxiv.org/abs/1705.07485))
- ShakeDrop-ResNet (['ShakeDrop Regularization for Deep Residual Learning'](https://arxiv.org/abs/1802.02375))
- FractalNet (['FractalNet: Ultra-Deep Neural Networks without Residuals'](https://arxiv.org/abs/1605.07648))
- iSQRT-COV-ResNet (['Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization'](https://arxiv.org/abs/1712.01034))

## Installation

To use the models in your project, simply install the `gluoncv2` package with `mxnet`:
```
pip install gluoncv2 mxnet>=1.2.1
```
To enable different hardware supports such as GPUs, check out [MXNet variants](https://pypi.org/project/mxnet).
For example, you can install with CUDA-9.2 supported MXNet:
```
pip install gluoncv2 mxnet-cu92>=1.2.1
```

## Usage

Example of using a pretrained ResNet-18 model:
```
from gluoncv2.model_provider import get_model as glcv2_get_model
import mxnet as mx

net = glcv2_get_model("resnet18", pretrained=True)
x = mx.nd.zeros((1, 3, 224, 224), ctx=mx.cpu())
y = net(x)
```

## Pretrained models

### Imagenet-1K

Some remarks:
- Top1/Top5 are the standard 1-crop Top-1/Top-5 errors (in percents) on the validation subset of the ImageNet-1K dataset.
- FLOPs/2 is the number of FLOPs divided by two to be similar to the number of MACs.
- ResNet/PreResNet with b-suffix is a version of the networks with the stride in the second convolution of the
bottleneck block. Respectively a network without b-suffix has the stride in the first convolution.
- ResNet/PreResNet models do not use biases in convolutions at all.
- CondenseNet models are only so-called converted versions.
- ShuffleNetV2 and ShuffleNetV2b are different implementations of the same architecture.

| Model | Top1 | Top5 | Params | FLOPs/2 | Remarks |
| --- | ---: | ---: | ---: | ---: | --- |
| AlexNet | 44.12 | 21.26 | 61,100,840 | 714.83M | From [dmlc/gluon-cv] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.108/alexnet-2126-9cb87ebd.params.log)) |
| VGG-11 | 31.91 | 11.76 | 132,863,336 | 7,615.87M | From [dmlc/gluon-cv] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.109/vgg11-1176-95dd287d.params.log)) |
| VGG-13 | 31.06 | 11.12 | 133,047,848 | 11,317.65M | From [dmlc/gluon-cv] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.109/vgg13-1112-a0db3c6c.params.log)) |
| VGG-16 | 26.78 | 8.69 | 138,357,544 | 15,480.10M | From [dmlc/gluon-cv] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.109/vgg16-0869-57a2556f.params.log)) |
| VGG-19 | 25.88 | 8.23 | 143,667,240 | 19,642.55M | From [dmlc/gluon-cv] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.109/vgg19-0823-0e2a1e0a.params.log)) |
| BN-VGG-11b | 30.34 | 10.57 | 132,868,840 | 7,630.72M | From [dmlc/gluon-cv] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.110/bn_vgg11b-1057-b2d8f382.params.log)) |
| BN-VGG-13b | 29.48 | 10.16 | 133,053,736 | 11,342.14M | From [dmlc/gluon-cv] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.110/bn_vgg13b-1016-f384ff52.params.log)) |
| BN-VGG-16b | 26.89 | 8.65 | 138,365,992 | 15,507.20M | From [dmlc/gluon-cv] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.110/bn_vgg16b-0865-b5e33db8.params.log)) |
| BN-VGG-19b | 25.66 | 8.15 | 143,678,248 | 19,672.26M | From [dmlc/gluon-cv] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.110/bn_vgg19b-0815-3a0e43e6.params.log)) |
| BN-Inception | 25.09 | 7.76 | 11,295,240 | 2,048.06M | From [Cadene/pretrained...pytorch] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.139/bninception-0776-8314001b.params.log)) |
| ResNet-10 | 37.09 | 15.55 | 5,418,792 | 894.04M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.1/resnet10-1555-cfb0a76d.params.log)) |
| ResNet-12 | 35.86 | 14.46 | 5,492,776 | 1,126.25M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.30/resnet12-1446-9ce715b0.params.log)) |
| ResNet-14 | 32.85 | 12.41 | 5,788,200 | 1,357.94M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.40/resnet14-1241-a8955ff3.params.log)) |
| ResNet-16 | 30.68 | 11.10 | 6,968,872 | 1,589.34M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.41/resnet16-1110-1be996d1.params.log)) |
| ResNet-18 x0.25 | 49.16 | 24.45 | 831,096 | 137.32M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.47/resnet18_wd4-2445-28d15cf4.params.log)) |
| ResNet-18 x0.5 | 36.54 | 14.96 | 3,055,880 | 486.49M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.46/resnet18_wd2-1496-d839c509.params.log)) |
| ResNet-18 x0.75 | 33.25 | 12.54 | 6,675,352 | 1,047.53M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.18/resnet18_w3d4-1254-d6548612.params.log)) |
| ResNet-18 | 28.09 | 9.51 | 11,689,512 | 1,820.41M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.153/resnet18-0951-98a2545b.params.log)) |
| ResNet-34 | 25.34 | 7.92 | 21,797,672 | 3,672.68M | From [dmlc/gluon-cv] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.1/resnet34-0792-5b875f49.params.log)) |
| ResNet-50 | 22.65 | 6.41 | 25,557,032 | 3,877.95M | From [dmlc/gluon-cv] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.147/resnet50-0641-1eaa883b.params.log)) |
| ResNet-50b | 22.32 | 6.18 | 25,557,032 | 4,110.48M | From [dmlc/gluon-cv] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.146/resnet50b-0618-8e2541fb.params.log)) |
| ResNet-101 | 21.66 | 5.99 | 44,549,160 | 7,597.95M | From [dmlc/gluon-cv] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.1/resnet101-0599-a6d3a5f4.params.log)) |
| ResNet-101b | 20.79 | 5.39 | 44,549,160 | 7,830.48M | From [dmlc/gluon-cv] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.145/resnet101b-0539-7406d858.params.log)) |
| ResNet-152 | 20.76 | 5.35 | 60,192,808 | 11,321.85M | From [dmlc/gluon-cv] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.144/resnet152-0535-bbdd7ed1.params.log)) |
| ResNet-152b | 20.31 | 5.25 | 60,192,808 | 11,554.38M | From [dmlc/gluon-cv] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.143/resnet152b-0525-6f30d0d9.params.log)) |
| PreResNet-18 | 28.16 | 9.51 | 11,687,848 | 1,820.56M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.140/preresnet18-0951-71279a0b.params.log)) |
| PreResNet-34 | 25.88 | 8.11 | 21,796,008 | 3,672.83M | From [dmlc/gluon-cv] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.2/preresnet34-0811-f8fe98a2.params.log)) |
| PreResNet-50 | 23.39 | 6.68 | 25,549,480 | 3,875.44M | From [dmlc/gluon-cv] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.2/preresnet50-0668-4940c94b.params.log)) |
| PreResNet-50b | 23.16 | 6.64 | 25,549,480 | 4,107.97M | From [dmlc/gluon-cv] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.2/preresnet50b-0664-2fcfddb1.params.log)) |
| PreResNet-101 | 21.45 | 5.75 | 44,541,608 | 7,595.44M | From [dmlc/gluon-cv] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.2/preresnet101-0575-e2887e53.params.log)) |
| PreResNet-101b | 21.73 | 5.88 | 44,541,608 | 7,827.97M | From [dmlc/gluon-cv] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.2/preresnet101b-0588-1015145a.params.log)) |
| PreResNet-152 | 20.70 | 5.32 | 60,185,256 | 11,319.34M | From [dmlc/gluon-cv] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.14/preresnet152-0532-31505f71.params.log)) |
| PreResNet-152b | 21.00 | 5.75 | 60,185,256 | 11,551.87M | From [dmlc/gluon-cv] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.2/preresnet152b-0575-dc303191.params.log)) |
| PreResNet-200b | 21.10 | 5.64 | 64,666,280 | 15,068.63M | From [tornadomeet/ResNet] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.45/preresnet200b-0564-38f849a6.params.log)) |
| PreResNet-269b | 20.71 | 5.56 | 102,065,832 | 20,101.11M | From [soeaver/mxnet-model] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.239/preresnet269b-0556-f386e3e7.params.log)) |
| ResNeXt-101 (32x4d) | 21.32 | 5.79 | 44,177,704 | 8,003.45M | From [Cadene/pretrained...pytorch] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.10/resnext101_32x4d-0579-9afbfdbc.params.log)) |
| ResNeXt-101 (64x4d) | 20.60 | 5.41 | 83,455,272 | 15,500.27M | From [Cadene/pretrained...pytorch] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.10/resnext101_64x4d-0541-0d4fd87b.params.log)) |
| SE-ResNet-50 | 22.51 | 6.44 | 28,088,024 | 3,880.49M | From [Cadene/pretrained...pytorch] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.11/seresnet50-0644-10954a84.params.log)) |
| SE-ResNet-101 | 21.92 | 5.89 | 49,326,872 | 7,602.76M | From [Cadene/pretrained...pytorch] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.11/seresnet101-0589-4c10238d.params.log)) |
| SE-ResNet-152 | 21.48 | 5.77 | 66,821,848 | 11,328.52M | From [Cadene/pretrained...pytorch] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.11/seresnet152-0577-de6f099d.params.log)) |
| SE-ResNeXt-50 (32x4d) | 21.06 | 5.58 | 27,559,896 | 4,258.40M | From [Cadene/pretrained...pytorch] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.12/seresnext50_32x4d-0558-a49f8fb0.params.log)) |
| SE-ResNeXt-101 (32x4d) | 19.99 | 5.00 | 48,955,416 | 8,008.26M | From [Cadene/pretrained...pytorch] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.12/seresnext101_32x4d-0500-cf161260.params.log)) |
| SENet-154 | 18.84 | 4.65 | 115,088,984 | 20,745.78M | From [Cadene/pretrained...pytorch] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.13/senet154-0465-dd244507.params.log)) |
| IBN-ResNet-50 | 23.56 | 6.68 | 25,557,032 | 4,110.48M | From [XingangPan/IBN-Net] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.127/ibn_resnet50-0668-db527596.params.log)) |
| IBN-ResNet-101 | 21.89 | 5.87 | 44,549,160 | 7,830.48M | From [XingangPan/IBN-Net] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.127/ibn_resnet101-0587-946e7f10.params.log)) |
| IBN(b)-ResNet-50 | 23.91 | 6.97 | 25,558,568 | 4,112.89M | From [XingangPan/IBN-Net] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.127/ibnb_resnet50-0697-0aea51d2.params.log)) |
| IBN-ResNeXt-101 (32x4d) | 21.43 | 5.62 | 44,177,704 | 8,003.45M | From [XingangPan/IBN-Net] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.127/ibn_resnext101_32x4d-0562-05ddba79.params.log)) |
| IBN-DenseNet-121 | 24.98 | 7.47 | 7,978,856 | 2,872.13M | From [XingangPan/IBN-Net] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.127/ibn_densenet121-0747-1434d379.params.log)) |
| IBN-DenseNet-169 | 23.78 | 6.82 | 14,149,480 | 3,403.89M | From [XingangPan/IBN-Net] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.127/ibn_densenet169-0682-6d7c48c5.params.log)) |
| AirNet50-1x64d (r=2) | 22.48 | 6.21 | 27,425,864 | 4,772.11M | From [soeaver/AirNet-PyTorch] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.120/airnet50_1x64d_r2-0621-347358cc.params.log)) |
| AirNet50-1x64d (r=16) | 22.91 | 6.46 | 25,714,952 | 4,399.97M | From [soeaver/AirNet-PyTorch] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.120/airnet50_1x64d_r16-0646-0b847b99.params.log)) |
| AirNeXt50-32x4d (r=2) | 21.51 | 5.75 | 27,604,296 | 5,339.58M | From [soeaver/AirNet-PyTorch] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.120/airnext50_32x4d_r2-0575-ab104fb5.params.log)) |
| BAM-ResNet-50 | 23.68 | 6.96 | 25,915,099 | 4,196.09M | From [Jongchan/attention-module] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.124/bam_resnet50-0696-7e573b61.params.log)) |
| CBAM-ResNet-50 | 23.02 | 6.38 | 28,089,624 | 4,116.97M | From [Jongchan/attention-module] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.125/cbam_resnet50-0638-78be5665.params.log)) |
| PyramidNet-101 (a=360) | 22.72 | 6.52 | 42,455,070 | 8,743.54M | From [dyhan0920/Pyramid...PyTorch] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.104/pyramidnet101_a360-0652-08d5a5d1.params.log)) |
| DiracNetV2-18 | 30.61 | 11.17 | 11,511,784 | 1,796.62M | From [szagoruyko/diracnets] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.111/diracnet18v2-1117-27601f6f.params.log)) |
| DiracNetV2-34 | 27.93 | 9.46 | 21,616,232 | 3,646.93M | From [szagoruyko/diracnets] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.111/diracnet34v2-0946-1faa6f12.params.log)) |
| CRU-Net-56 | 25.72 | 8.25 | 25,609,384 | 5,660.66M | From [cypw/CRU-Net] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.197/crunet56-0825-ad16523b.params.log)) |
| DenseNet-121 | 25.11 | 7.80 | 7,978,856 | 2,872.13M | From [dmlc/gluon-cv] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.3/densenet121-0780-49b72d04.params.log)) |
| DenseNet-161 | 22.40 | 6.18 | 28,681,000 | 7,793.16M | From [dmlc/gluon-cv] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.3/densenet161-0618-52e30516.params.log)) |
| DenseNet-169 | 23.89 | 6.89 | 14,149,480 | 3,403.89M | From [dmlc/gluon-cv] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.3/densenet169-0689-281ec06b.params.log)) |
| DenseNet-201 | 22.71 | 6.36 | 20,013,928 | 4,347.15M | From [dmlc/gluon-cv] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.3/densenet201-0636-65b5d389.params.log)) |
| CondenseNet-74 (C=G=4) | 26.82 | 8.64 | 4,773,944 | 546.06M | From [ShichenLiu/CondenseNet] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.4/condensenet74_c4_g4-0864-cde68fa2.params.log)) |
| CondenseNet-74 (C=G=8) | 29.76 | 10.49 | 2,935,416 | 291.52M | From [ShichenLiu/CondenseNet] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.4/condensenet74_c8_g8-1049-4cf4a08e.params.log)) |
| PeleeNet | 31.71 | 11.25 | 2,802,248 | 514.87M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.141/peleenet-1125-38d4fb24.params.log)) |
| WRN-50-2 | 22.15 | 6.12 | 68,849,128 | 11,405.42M | From [szagoruyko/functional-zoo] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.113/wrn50_2-0612-f8013e68.params.log)) |
| DRN-C-26 | 25.68 | 7.89 | 21,126,584 | 16,993.90M | From [fyu/drn] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.116/drnc26-0789-ee56ffab.params.log)) |
| DRN-C-42 | 23.80 | 6.92 | 31,234,744 | 25,093.75M | From [fyu/drn] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.116/drnc42-0692-f89c26d6.params.log)) |
| DRN-C-58 | 22.35 | 6.27 | 40,542,008 | 32,489.94M | From [fyu/drn] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.116/drnc58-0627-44cbf15c.params.log)) |
| DRN-D-22 | 26.67 | 8.52 | 16,393,752 | 13,051.33M | From [fyu/drn] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.116/drnd22-0852-08574752.params.log)) |
| DRN-D-38 | 24.51 | 7.36 | 26,501,912 | 21,151.19M | From [fyu/drn] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.116/drnd38-0736-c7d53bc0.params.log)) |
| DRN-D-54 | 22.05 | 6.27 | 35,809,176 | 28,547.38M | From [fyu/drn] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.116/drnd54-0627-87d44c87.params.log)) |
| DRN-D-105 | 21.31 | 5.81 | 54,801,304 | 43,442.43M | From [fyu/drn] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.116/drnd105-0581-ab12d662.params.log)) |
| DPN-68 | 23.57 | 7.00 | 12,611,602 | 2,351.84M | From [Cadene/pretrained...pytorch] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.17/dpn68-0700-3114719d.params.log)) |
| DPN-98 | 20.23 | 5.28 | 61,570,728 | 11,716.51M | From [Cadene/pretrained...pytorch] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.17/dpn98-0528-fa5d6fca.params.log)) |
| DPN-131 | 20.03 | 5.22 | 79,254,504 | 16,076.15M | From [Cadene/pretrained...pytorch] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.17/dpn131-0522-35ac2f82.params.log)) |
| DarkNet Tiny | 40.31 | 17.46 | 1,042,104 | 500.85M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.69/darknet_tiny-1746-16501793.params.log)) |
| DarkNet Ref | 38.00 | 16.68 | 7,319,416 | 367.59M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.64/darknet_ref-1668-3011b4e1.params.log)) |
| DarkNet-53 | 21.44 | 5.56 | 41,609,928 | 7,133.86M | From [dmlc/gluon-cv] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.150/darknet53-0556-e9486353.params.log)) |
| DLA-34 | 26.14 | 8.21 | 15,742,104 | 3,071.37M | From [ucbdrive/dla] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.202/dla34-0821-1127fa0a.params.log)) |
| DLA-46-C | 36.79 | 14.70 | 1,301,400 | 585.45M | From [ucbdrive/dla] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.202/dla46c-1470-bae8b513.params.log)) |
| DLA-X-46-C | 35.58 | 13.98 | 1,068,440 | 546.72M | From [ucbdrive/dla] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.202/dla46xc-1398-28deb1fc.params.log)) |
| DLA-60 | 23.84 | 7.08 | 22,036,632 | 4,255.49M | From [ucbdrive/dla] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.202/dla60-0708-954571d6.params.log)) |
| DLA-X-60 | 22.48 | 6.21 | 17,352,344 | 3,543.68M | From [ucbdrive/dla] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.202/dla60x-0621-35774214.params.log)) |
| DLA-X-60-C | 33.52 | 12.41 | 1,319,832 | 596.06M | From [ucbdrive/dla] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.202/dla60xc-1241-338c6241.params.log)) |
| DLA-102 | 22.87 | 6.44 | 33,268,888 | 7,190.95M | From [ucbdrive/dla] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.202/dla102-0644-cadbb1cc.params.log)) |
| DLA-X-102 | 21.97 | 6.02 | 26,309,272 | 5,884.94M | From [ucbdrive/dla] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.202/dla102x-0602-193568a7.params.log)) |
| DLA-X2-102 | 21.12 | 5.53 | 41,282,200 | 9,340.61M | From [ucbdrive/dla] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.202/dla102x2-0553-30c8f409.params.log)) |
| DLA-169 | 21.95 | 5.87 | 53,389,720 | 11,593.20M | From [ucbdrive/dla] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.202/dla169-0587-4f3e6a6e.params.log)) |
| FishNet-150 | 22.85 | 6.38 | 24,959,400 | 6,435.02M | From [kevin-ssy/FishNet] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.168/fishnet150-0638-5cbd08ec.params.log)) |
| ESPNetv2 x0.5 | 43.61 | 21.07 | 1,241,332 | 35.36M | From [sacmehta/ESPNetv2] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.238/espnetv2_wd2-2107-f2e17f0a.params.log)) |
| ESPNetv2 x1.0 | 35.33 | 14.27 | 1,670,072 | 98.09M | From [sacmehta/ESPNetv2] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.238/espnetv2_w1-1427-538f31fb.params.log)) |
| ESPNetv2 x1.25 | 33.14 | 12.73 | 1,965,440 | 138.18M | From [sacmehta/ESPNetv2] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.238/espnetv2_w5d4-1273-b119ad9e.params.log)) |
| ESPNetv2 x1.5 | 32.04 | 11.94 | 2,314,856 | 185.77M | From [sacmehta/ESPNetv2] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.238/espnetv2_w3d2-1194-3804a850.params.log)) |
| ESPNetv2 x2.0 | 28.91 | 9.94 | 3,498,136 | 306.93M | From [sacmehta/ESPNetv2] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.238/espnetv2_w2-0994-c212d81a.params.log)) |
| SqueezeNet v1.0 | 38.73 | 17.34 | 1,248,424 | 823.67M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.128/squeezenet_v1_0-1734-e6f8b0e8.params.log)) |
| SqueezeNet v1.1 | 39.09 | 17.39 | 1,235,496 | 352.02M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.88/squeezenet_v1_1-1739-d7a1483a.params.log)) |
| SqueezeResNet v1.0 | 39.32 | 17.67 | 1,248,424 | 823.67M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.178/squeezeresnet_v1_0-1767-66474b9b.params.log)) |
| SqueezeResNet v1.1 | 39.83 | 17.84 | 1,235,496 | 352.02M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.70/squeezeresnet_v1_1-1784-26064b82.params.log)) |
| 1.0-SqNxt-23 | 42.25 | 18.66 | 724,056 | 287.28M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.171/sqnxt23_w1-1866-73b700c4.params.log)) |
| 1.0-SqNxt-23v5 | 40.43 | 17.43 | 921,816 | 285.82M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.172/sqnxt23v5_w1-1743-7a83722e.params.log)) |
| 1.5-SqNxt-23 | 34.46 | 13.21 | 1,511,824 | 552.39M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.210/sqnxt23_w3d2-1321-4d733bcd.params.log)) |
| 1.5-SqNxt-23v5 | 33.48 | 12.68 | 1,953,616 | 550.97M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.212/sqnxt23v5_w3d2-1268-4f98bbd3.params.log)) |
| 2.0-SqNxt-23 | 30.24 | 10.63 | 2,583,752 | 898.48M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.240/sqnxt23_w2-1063-95d9b55a.params.log)) |
| 2.0-SqNxt-23v5 | 29.27 | 10.24 | 3,366,344 | 897.60M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.216/sqnxt23v5_w2-1024-707246f3.params.log)) |
| ShuffleNet x0.25 (g=1) | 62.00 | 36.77 | 209,746 | 12.35M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.134/shufflenet_g1_wd4-3677-ee58f368.params.log)) |
| ShuffleNet x0.25 (g=3) | 61.34 | 36.17 | 305,902 | 13.09M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.135/shufflenet_g3_wd4-3617-bd08e3ed.params.log)) |
| ShuffleNet x0.5 (g=1) | 46.22 | 22.38 | 534,484 | 41.16M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.174/shufflenet_g1_wd2-2238-f77dcd18.params.log)) |
| ShuffleNet x0.5 (g=3) | 43.83 | 20.60 | 718,324 | 41.70M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.167/shufflenet_g3_wd2-2060-ea6737a5.params.log)) |
| ShuffleNet x0.75 (g=1) | 39.25 | 16.75 | 975,214 | 86.42M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.218/shufflenet_g1_w3d4-1675-2f1530aa.params.log)) |
| ShuffleNet x0.75 (g=3) | 37.81 | 16.09 | 1,238,266 | 85.82M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.219/shufflenet_g3_w3d4-1609-e008e926.params.log)) |
| ShuffleNet x1.0 (g=1) | 34.41 | 13.50 | 1,531,936 | 148.13M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.223/shufflenet_g1_w1-1350-01934ee8.params.log)) |
| ShuffleNet x1.0 (g=2) | 33.98 | 13.32 | 1,733,848 | 147.60M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.241/shufflenet_g2_w1-1332-f5a1479f.params.log)) |
| ShuffleNet x1.0 (g=3) | 33.96 | 13.29 | 1,865,728 | 145.46M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.244/shufflenet_g3_w1-1329-ac58d62c.params.log)) |
| ShuffleNet x1.0 (g=4) | 33.84 | 13.10 | 1,968,344 | 143.33M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.245/shufflenet_g4_w1-1310-73c039eb.params.log)) |
| ShuffleNetV2 x0.5 | 40.61 | 18.30 | 1,366,792 | 43.31M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.90/shufflenetv2_wd2-1830-156953de.params.log)) |
| ShuffleNetV2 x1.0 | 30.94 | 11.23 | 2,278,604 | 149.72M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.133/shufflenetv2_w1-1123-27435039.params.log)) |
| ShuffleNetV2 x1.5 | 32.38 | 12.37 | 4,406,098 | 320.77M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.65/shufflenetv2_w3d2-1237-08c01388.params.log)) |
| ShuffleNetV2 x2.0 | 32.04 | 12.10 | 7,601,686 | 595.84M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.84/shufflenetv2_w2-1210-544b55d9.params.log)) |
| ShuffleNetV2b x0.5 | 39.81 | 17.82 | 1,366,792 | 43.31M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.157/shufflenetv2b_wd2-1782-845a9c43.params.log)) |
| ShuffleNetV2b x1.0 | 30.39 | 11.01 | 2,279,760 | 150.62M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.161/shufflenetv2b_w1-1101-f679702f.params.log)) |
| ShuffleNetV2b x1.5 | 26.90 | 8.79 | 4,410,194 | 323.98M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.203/shufflenetv2b_w3d2-0879-4022da3a.params.log)) |
| ShuffleNetV2b x2.0 | 25.20 | 8.10 | 7,611,290 | 603.37M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.242/shufflenetv2b_w2-0810-7429df75.params.log)) |
| 108-MENet-8x1 (g=3) | 43.62 | 20.30 | 654,516 | 42.68M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.89/menet108_8x1_g3-2030-aa07f925.params.log)) |
| 128-MENet-8x1 (g=4) | 42.10 | 19.13 | 750,796 | 45.98M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.103/menet128_8x1_g4-1913-0c890a76.params.log)) |
| 128-MENet-8x1 (g=4) | 42.10 | 19.13 | 750,796 | 45.98M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.103/menet128_8x1_g4-1913-0c890a76.params.log)) |
| 160-MENet-8x1 (g=8) | 43.47 | 20.28 | 850,120 | 45.63M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.154/menet160_8x1_g8-2028-4f28279a.params.log)) |
| 256-MENet-12x1 (g=4) | 32.23 | 12.16 | 1,888,240 | 150.65M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.152/menet256_12x1_g4-1216-7caf63d1.params.log)) |
| 348-MENet-12x1 (g=3) | 27.85 | 9.36 | 3,368,128 | 312.00M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.173/menet348_12x1_g3-0936-62c72b0b.params.log)) |
| 352-MENet-12x1 (g=8) | 31.30 | 11.67 | 2,272,872 | 157.35M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.198/menet352_12x1_g8-1167-5892fea4.params.log)) |
| 456-MENet-24x1 (g=3) | 25.02 | 7.80 | 5,304,784 | 567.90M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.237/menet456_24x1_g3-0780-7a89b32c.params.log)) |
| MobileNet x0.25 | 45.78 | 22.18 | 470,072 | 44.09M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.62/mobilenet_wd4-2218-3185cdd2.params.log)) |
| MobileNet x0.5 | 33.94 | 13.30 | 1,331,592 | 155.42M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.156/mobilenet_wd2-1330-94f13ae1.params.log)) |
| MobileNet x0.75 | 29.85 | 10.51 | 2,585,560 | 333.99M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.130/mobilenet_w3d4-1051-6361d4b4.params.log)) |
| MobileNet x1.0 | 26.43 | 8.65 | 4,231,976 | 579.80M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.155/mobilenet_w1-0865-eafd91e9.params.log)) |
| FD-MobileNet x0.25 | 55.44 | 30.53 | 383,160 | 12.95M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.177/fdmobilenet_wd4-3053-d4f18e5b.params.log)) |
| FD-MobileNet x0.5 | 42.62 | 19.69 | 993,928 | 41.84M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.83/fdmobilenet_wd2-1969-242b9fa8.params.log)) |
| FD-MobileNet x0.75 | 37.91 | 16.01 | 1,833,304 | 86.68M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.159/fdmobilenet_w3d4-1601-cb10c3e1.params.log)) |
| FD-MobileNet x1.0 | 33.80 | 13.12 | 2,901,288 | 147.46M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.162/fdmobilenet_w1-1312-95fa0092.params.log)) |
| MobileNetV2 x0.25 | 48.08 | 24.12 | 1,516,392 | 34.24M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.137/mobilenetv2_wd4-2412-d92b5b2d.params.log)) |
| MobileNetV2 x0.5 | 35.63 | 14.42 | 1,964,736 | 100.13M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.170/mobilenetv2_wd2-1442-d7c586c7.params.log)) |
| MobileNetV2 x0.75 | 29.78 | 10.44 | 2,627,592 | 198.50M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.230/mobilenetv2_w3d4-1044-768454f4.params.log)) |
| MobileNetV2 x1.0 | 26.77 | 8.64 | 3,504,960 | 329.36M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.213/mobilenetv2_w1-0864-6e58b1cb.params.log)) |
| IGCV3 x0.25 | 53.43 | 28.30 | 1,534,020 | 41.29M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.142/igcv3_wd4-2830-71abf6e0.params.log)) |
| IGCV3 x0.5 | 39.41 | 17.03 | 1,985,528 | 111.12M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.132/igcv3_wd2-1703-145b7089.params.log)) |
| IGCV3 x0.75 | 30.71 | 10.96 | 2,638,084 | 210.95M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.207/igcv3_w3d4-1096-3c7c86fc.params.log)) |
| IGCV3 x1.0 | 27.73 | 9.00 | 3,491,688 | 340.79M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.243/igcv3_w1-0900-e2c3da1c.params.log)) |
| MnasNet | 31.32 | 11.44 | 4,308,816 | 317.67M | From [zeusees/Mnasnet...Model] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.117/mnasnet-1144-c972fec0.params.log)) |
| DARTS | 27.23 | 8.97 | 4,718,752 | 539.86M | From [quark0/darts] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.118/darts-0897-aafd6452.params.log)) |
| Xception | 20.99 | 5.56 | 22,855,952 | 8,403.63M | From [Cadene/pretrained...pytorch] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.115/xception-0556-bd2c1684.params.log)) |
| InceptionV3 | 21.22 | 5.59 | 23,834,568 | 5,743.06M | From [dmlc/gluon-cv] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.92/inceptionv3-0559-6c087967.params.log)) |
| InceptionV4 | 20.60 | 5.25 | 42,679,816 | 12,304.93M | From [Cadene/pretrained...pytorch] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.105/inceptionv4-0525-f7aa9536.params.log)) |
| InceptionResNetV2 | 19.96 | 4.94 | 55,843,464 | 13,188.64M | From [Cadene/pretrained...pytorch] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.107/inceptionresnetv2-0494-3328f7fa.params.log)) |
| PolyNet | 19.09 | 4.53 | 95,366,600 | 34,821.34M | From [Cadene/pretrained...pytorch] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.96/polynet-0453-74280314.params.log)) |
| NASNet-A 4@1056 | 25.37 | 7.95 | 5,289,978 | 584.90M | From [Cadene/pretrained...pytorch] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.97/nasnet_4a1056-0795-5c78908e.params.log)) |
| NASNet-A 6@4032 | 18.17 | 4.24 | 88,753,150 | 23,976.44M | From [Cadene/pretrained...pytorch] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.101/nasnet_6a4032-0424-73cca5fe.params.log)) |
| PNASNet-5-Large | 17.90 | 4.28 | 86,057,668 | 25,140.77M | From [Cadene/pretrained...pytorch] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.114/pnasnet5large-0428-998a548f.params.log)) |

### CIFAR-10

Some remarks:
- Testing subset is used for validation purpose.
- `Features` means feature extractor output size.

| Model | Error, % | Features | Params | FLOPs/2 | Remarks |
| --- | ---: | ---: |  ---: | ---: | --- |
| NIN | 7.43 | 192 | 966,986 | 222.97M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.175/nin_cifar10-0743-9696dc1a.params.log)) |
| ResNet-20 | 5.97 | 64 | 272,474 | 41.29M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.163/resnet20_cifar10-0597-13c5ab19.params.log)) |
| ResNet-56 | 4.52 | 64 | 855,770 | 127.06M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.163/resnet56_cifar10-0452-a73e63e9.params.log)) |
| ResNet-110 | 3.69 | 64 | 1,730,714 | 255.70M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.163/resnet110_cifar10-0369-f89f1c4d.params.log)) |
| ResNet-164(BN) | 3.68 | 256 | 1,704,154 | 255.31M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.179/resnet164bn_cifar10-0368-e7941eee.params.log)) |
| ResNet-1001 | 3.28 | 256 | 10,328,602 | 1,536.40M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.201/resnet1001_cifar10-0328-bb979d53.params.log)) |
| ResNet-1202 | 3.53 | 64 | 19,424,026 | 2,857.17M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.214/resnet1202_cifar10-0353-377510a6.params.log)) |
| PreResNet-20 | 6.51 | 64 | 272,282 | 41.27M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.164/preresnet20_cifar10-0651-daa89573.params.log)) |
| PreResNet-56 | 4.49 | 64 | 855,578 | 127.03M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.164/preresnet56_cifar10-0449-cb37cb9d.params.log)) |
| PreResNet-110 | 3.86 | 64 | 1,730,522 | 255.68M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.164/preresnet110_cifar10-0386-d6d4b7bd.params.log)) |
| PreResNet-164(BN) | 3.64 | 256 | 1,703,258 | 255.08M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.196/preresnet164bn_cifar10-0364-7ecf30cb.params.log)) |
| PreResNet-1001 | 2.65 | 256 | 10,327,706 | 1,536.18M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.209/preresnet1001_cifar10-0265-50507ff7.params.log)) |
| ResNeXt-29 (32x4d) | 3.15 | 1024 | 4,775,754 | 780.55M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.169/resnext29_32x4d_cifar10-0315-c8a1beda.params.log)) |
| ResNeXt-29 (16x64d) | 2.41 | 1024 | 68,155,210 | 10,709.34M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.176/resnext29_16x64d_cifar10-0241-76b97a4d.params.log)) |
| PyramidNet-110 (a=48) | 3.72 | 64 | 1,772,706 | 408.37M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.184/pyramidnet110_a48_cifar10-0372-35b94d05.params.log)) |
| PyramidNet-110 (a=84) | 2.98 | 100 | 3,904,446 | 778.15M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.185/pyramidnet110_a84_cifar10-0298-81710d7a.params.log)) |
| PyramidNet-110 (a=270) | 2.51 | 286 | 28,485,477 | 4,730.60M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.194/pyramidnet110_a270_cifar10-0251-1e769ce5.params.log)) |
| DenseNet-40 (k=12) | 5.61 | 258 | 599,050 | 210.80M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.193/densenet40_k12_cifar10-0561-28dc0035.params.log)) |
| DenseNet-BC-40 (k=12) | 6.43 | 132 | 176,122 | 74.89M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.231/densenet40_k12_bc_cifar10-0643-7fdeda31.params.log)) |
| DenseNet-BC-40 (k=24) | 4.52 | 264 | 690,346 | 293.09M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.220/densenet40_k24_bc_cifar10-0452-13fa807e.params.log)) |
| DenseNet-BC-40 (k=36) | 4.04 | 396 | 1,542,682 | 654.60M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.224/densenet40_k36_bc_cifar10-0404-4c154567.params.log)) |
| DenseNet-100 (k=12) | 3.66 | 678 | 4,068,490 | 1,353.55M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.205/densenet100_k12_cifar10-0366-4e371ccb.params.log)) |
| DenseNet-BC-100 (k=12) | 4.16 | 342 | 769,162 | 298.45M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.189/densenet100_k12_bc_cifar10-0416-6685d1f4.params.log)) |
| X-DenseNet-BC-40-2 (k=24) | 5.31 | 264 | 690,346 | 293.09M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.226/xdensenet40_2_k24_bc_cifar10-0531-66c9d384.params.log)) |
| X-DenseNet-BC-40-2 (k=36) | 4.37 | 396 | 1,542,682 | 654.60M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.233/xdensenet40_2_k36_bc_cifar10-0437-e9bf4192.params.log)) |
| WRN-16-10 | 2.93 | 640 | 17,116,634 | 2,414.04M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.166/wrn16_10_cifar10-0293-ecf1c17c.params.log)) |
| WRN-28-10 | 2.39 | 640 | 36,479,194 | 5,246.98M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.166/wrn28_10_cifar10-0239-16f3c8a2.params.log)) |
| WRN-40-8 | 2.37 | 512 | 35,748,314 | 5,176.90M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.166/wrn40_8_cifar10-0237-3b81d261.params.log)) |
| RoR-3-56 | 5.43 | 64 | 762,746 | 113.43M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.228/ror3_56_cifar10-0543-ee31a69a.params.log)) |
| RoR-3-110 | 4.35 | 64 | 1,637,690 | 242.07M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.235/ror3_110_cifar10-0435-03599165.params.log)) |
| Shake-Shake-ResNet-20-2x16d | 5.15 | 64 | 541,082 | 81.78M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.215/shakeshakeresnet20_2x16d_cifar10-0515-a7b8a2f7.params.log)) |
| Shake-Shake-ResNet-26-2x32d | 3.17 | 64 | 2,923,162 | 428.89M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.217/shakeshakeresnet26_2x32d_cifar10-0317-21e60e62.params.log)) |

### CIFAR-100

Some remarks:
- Testing subset is used for validation purpose.

| Model | Error, % | Params | FLOPs/2 | Remarks |
| --- | ---: | ---: | ---: | --- |
| NIN | 28.39 | 984,356 | 224.08M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.183/nin_cifar100-2839-eed0e9af.params.log)) |
| ResNet-20 | 29.64 | 278,324 | 41.30M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.180/resnet20_cifar100-2964-4e144352.params.log)) |
| ResNet-56 | 24.88 | 861,620 | 127.06M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.181/resnet56_cifar100-2488-59097710.params.log)) |
| ResNet-110 | 22.80 | 1,736,564 | 255.71M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.190/resnet110_cifar100-2280-6c5fa14b.params.log)) |
| ResNet-164(BN) | 20.44 | 1,727,284 | 255.33M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.182/resnet164bn_cifar100-2044-c7db7b5e.params.log)) |
| PreResNet-20 | 30.22 | 278,132 | 41.28M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.187/preresnet20_cifar100-3022-37f15365.params.log)) |
| PreResNet-56 | 25.05 | 861,428 | 127.04M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.188/preresnet56_cifar100-2505-4c39e83f.params.log)) |
| PreResNet-110 | 22.67 | 1,736,372 | 255.68M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.191/preresnet110_cifar100-2267-18cf4161.params.log)) |
| PreResNet-164(BN) | 20.18 | 1,726,388 | 255.10M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.192/preresnet164bn_cifar100-2018-a20557c8.params.log)) |
| ResNeXt-29 (32x4d) | 19.50 | 4,868,004 | 780.64M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.200/resnext29_32x4d_cifar100-1950-5f2eedcd.params.log)) |
| PyramidNet-110 (a=48) | 20.95 | 1,778,556 | 408.38M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.186/pyramidnet110_a48_cifar100-2095-00fd42a0.params.log)) |
| PyramidNet-110 (a=84) | 18.87 | 3,913,536 | 778.16M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.199/pyramidnet110_a84_cifar100-1887-6712d5dc.params.log)) |
| DenseNet-40 (k=12) | 24.90 | 622,360 | 210.82M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.195/densenet40_k12_cifar100-2490-908f02ba.params.log)) |
| DenseNet-BC-40 (k=12) | 28.41 | 188,092 | 74.90M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.232/densenet40_k12_bc_cifar100-2841-35cd8e6a.params.log)) |
| DenseNet-BC-40 (k=24) | 22.67 | 714,196 | 293.11M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.221/densenet40_k24_bc_cifar100-2267-2c4ef7c4.params.log)) |
| DenseNet-BC-40 (k=36) | 20.50 | 1,578,412 | 654.64M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.225/densenet40_k36_bc_cifar100-2050-d7275d39.params.log)) |
| DenseNet-100 (k=12) | 19.64 | 4,129,600 | 1,353.62M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.206/densenet100_k12_cifar100-1964-2ed5ec27.params.log)) |
| DenseNet-BC-100 (k=12) | 21.19 | 800,032 | 298.48M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.208/densenet100_k12_bc_cifar100-2119-fbd8a54c.params.log)) |
| X-DenseNet-BC-40-2 (k=24) | 23.96 | 714,196 | 293.11M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.227/xdensenet40_2_k24_bc_cifar100-2396-73d5ba88.params.log)) |
| X-DenseNet-BC-40-2 (k=36) | 21.65 | 1,578,412 | 654.64M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.234/xdensenet40_2_k36_bc_cifar100-2165-78b6e754.params.log)) |
| WRN-16-10 | 18.95 | 17,174,324 | 2,414.09M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.204/wrn16_10_cifar100-1895-bcb5c89c.params.log)) |
| RoR-3-56 | 25.49 | 768,596 | 113.43M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.229/ror3_56_cifar100-2549-43345593.params.log)) |
| RoR-3-110 | 23.64 | 1,643,540 | 242.08M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.236/ror3_110_cifar100-2364-b8c4d317.params.log)) |
| Shake-Shake-ResNet-26-2x32d | 18.80 | 2,934,772 | 428.90M | Training ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.222/shakeshakeresnet26_2x32d_cifar100-1880-bd46a741.params.log)) |

[dmlc/gluon-cv]: https://github.com/dmlc/gluon-cv
[tornadomeet/ResNet]: https://github.com/tornadomeet/ResNet
[Cadene/pretrained...pytorch]: https://github.com/Cadene/pretrained-models.pytorch
[ShichenLiu/CondenseNet]: https://github.com/ShichenLiu/CondenseNet
[clavichord93/MENet]: https://github.com/clavichord93/MENet
[clavichord93/FD-MobileNet]: https://github.com/clavichord93/FD-MobileNet
[tensorpack/tensorpack]: https://github.com/tensorpack/tensorpack
[dyhan0920/Pyramid...PyTorch]: https://github.com/dyhan0920/PyramidNet-PyTorch
[zeusees/Mnasnet...Model]: https://github.com/zeusees/Mnasnet-Pretrained-Model
[szagoruyko/diracnets]: https://github.com/szagoruyko/diracnets
[szagoruyko/functional-zoo]: https://github.com/szagoruyko/functional-zoo
[fyu/drn]: https://github.com/fyu/drn
[quark0/darts]: https://github.com/quark0/darts
[soeaver/AirNet-PyTorch]: https://github.com/soeaver/AirNet-PyTorch
[soeaver/mxnet-model]: https://github.com/soeaver/mxnet-model
[Jongchan/attention-module]: https://github.com/Jongchan/attention-module
[XingangPan/IBN-Net]: https://github.com/XingangPan/IBN-Net
[cypw/CRU-Net]: https://github.com/cypw/CRU-Net
[kevin-ssy/FishNet]: https://github.com/kevin-ssy/FishNet
[ucbdrive/dla]: https://github.com/ucbdrive/dla
[sacmehta/ESPNetv2]: https://github.com/sacmehta/ESPNetv2

