Pretrained models for Pytorch (Work in progress)

The goal of this repo is:

to help to reproduce research papers results (transfer learning setups for instance),
to access pretrained ConvNets with a unique interface/API inspired by torchvision.

News:

27/10/2018: Fix compatibility issues, Add tests, Add travis
04/06/2018: PolyNet and PNASNet-5-Large thanks to Alex Parinov
16/04/2018: SE-ResNet* and SE-ResNeXt* thanks to Alex Parinov
09/04/2018: SENet154 thanks to Alex Parinov
22/03/2018: CaffeResNet101 (good for localization with FasterRCNN)
21/03/2018: NASNet Mobile thanks to Veronika Yurchuk and Anastasiia
25/01/2018: DualPathNetworks thanks to Ross Wightman, Xception thanks to T Standley, improved TransformImage API
13/01/2018: pip install pretrainedmodels, pretrainedmodels.model_names, pretrainedmodels.pretrained_settings
12/01/2018: python setup.py install
08/12/2017: update data url (/!\ git pull is needed)
30/11/2017: improve API (model.features(input), model.logits(features), model.forward(input), model.last_linear)
16/11/2017: nasnet-a-large pretrained model ported by T. Durand and R. Cadene
22/07/2017: torchvision pretrained models
22/07/2017: momentum in inceptionv4 and inceptionresnetv2 to 0.1
17/07/2017: model.input_range attribut
17/07/2017: BNInception pretrained on Imagenet

Summary

Installation

Install from pip

pip install pretrainedmodels

Install from repo

git clone https://github.com/Cadene/pretrained-models.pytorch.git
cd pretrained-models.pytorch
python setup.py install

Quick examples

To import pretrainedmodels:

import pretrainedmodels

To print the available pretrained models:

print(pretrainedmodels.model_names)
> ['fbresnet152', 'bninception', 'resnext101_32x4d', 'resnext101_64x4d', 'inceptionv4', 'inceptionresnetv2', 'alexnet', 'densenet121', 'densenet169', 'densenet201', 'densenet161', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'inceptionv3', 'squeezenet1_0', 'squeezenet1_1', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn', 'vgg19_bn', 'vgg19', 'nasnetalarge', 'nasnetamobile', 'cafferesnet101', 'senet154',  'se_resnet50', 'se_resnet101', 'se_resnet152', 'se_resnext50_32x4d', 'se_resnext101_32x4d', 'cafferesnet101', 'polynet', 'pnasnet5large']

To print the available pretrained settings for a chosen model:

print(pretrainedmodels.pretrained_settings['nasnetalarge'])
> {'imagenet': {'url': 'http://data.lip6.fr/cadene/pretrainedmodels/nasnetalarge-a1897284.pth', 'input_space': 'RGB', 'input_size': [3, 331, 331], 'input_range': [0, 1], 'mean': [0.5, 0.5, 0.5], 'std': [0.5, 0.5, 0.5], 'num_classes': 1000}, 'imagenet+background': {'url': 'http://data.lip6.fr/cadene/pretrainedmodels/nasnetalarge-a1897284.pth', 'input_space': 'RGB', 'input_size': [3, 331, 331], 'input_range': [0, 1], 'mean': [0.5, 0.5, 0.5], 'std': [0.5, 0.5, 0.5], 'num_classes': 1001}}

To load a pretrained models from imagenet:

model_name = 'nasnetalarge' # could be fbresnet152 or inceptionresnetv2
model = pretrainedmodels.__dict__[model_name](num_classes=1000, pretrained='imagenet')
model.eval()

Note: By default, models will be downloaded to your $HOME/.torch folder. You can modify this behavior using the $TORCH_HOME variable as follow: export TORCH_HOME="/local/pretrainedmodels"

To load an image and do a complete forward pass:

import torch
import pretrainedmodels.utils as utils

load_img = utils.LoadImage()

# transformations depending on the model
# rescale, center crop, normalize, and others (ex: ToBGR, ToRange255)
tf_img = utils.TransformImage(model) 

path_img = 'data/cat.jpg'

input_img = load_img(path_img)
input_tensor = tf_img(input_img)         # 3x400x225 -> 3x299x299 size may differ
input_tensor = input_tensor.unsqueeze(0) # 3x299x299 -> 1x3x299x299
input = torch.autograd.Variable(input_tensor,
    requires_grad=False)

output_logits = model(input) # 1x1000

To extract features (beware this API is not available for all networks):

output_features = model.features(input) # 1x14x14x2048 size may differ
output_logits = model.logits(output_features) # 1x1000

Few use cases

Compute imagenet logits

See examples/imagenet_logits.py to compute logits of classes appearance over a single image with a pretrained model on imagenet.

$ python examples/imagenet_logits.py -h
> nasnetalarge, resnet152, inceptionresnetv2, inceptionv4, ...

$ python examples/imagenet_logits.py -a nasnetalarge --path_img data/cat.jpg
> 'nasnetalarge': data/cat.jpg' is a 'tiger cat'

Compute imagenet evaluation metrics

See examples/imagenet_eval.py to evaluate pretrained models on imagenet valset.

$ python examples/imagenet_eval.py /local/common-data/imagenet_2012/images -a nasnetalarge -b 20 -e
> * Acc@1 82.693, Acc@5 96.13

Evaluation on imagenet

Accuracy on validation set (single model)

Results were obtained using (center cropped) images of the same size than during the training process.

Model	Version	Acc@1	Acc@5
PNASNet-5-Large	Tensorflow	82.858	96.182
PNASNet-5-Large	Our porting	82.736	95.992
NASNet-A-Large	Tensorflow	82.693	96.163
NASNet-A-Large	Our porting	82.566	96.086
SENet154	Caffe	81.32	95.53
SENet154	Our porting	81.304	95.498
PolyNet	Caffe	81.29	95.75
PolyNet	Our porting	81.002	95.624
InceptionResNetV2	Tensorflow	80.4	95.3
InceptionV4	Tensorflow	80.2	95.3
SE-ResNeXt101_32x4d	Our porting	80.236	95.028
SE-ResNeXt101_32x4d	Caffe	80.19	95.04
InceptionResNetV2	Our porting	80.170	95.234
InceptionV4	Our porting	80.062	94.926
DualPathNet107_5k	Our porting	79.746	94.684
ResNeXt101_64x4d	Torch7	79.6	94.7
DualPathNet131	Our porting	79.432	94.574
DualPathNet92_5k	Our porting	79.400	94.620
DualPathNet98	Our porting	79.224	94.488
SE-ResNeXt50_32x4d	Our porting	79.076	94.434
SE-ResNeXt50_32x4d	Caffe	79.03	94.46
Xception	Keras	79.000	94.500
ResNeXt101_64x4d	Our porting	78.956	94.252
Xception	Our porting	78.888	94.292
ResNeXt101_32x4d	Torch7	78.8	94.4
SE-ResNet152	Caffe	78.66	94.46
SE-ResNet152	Our porting	78.658	94.374
ResNet152	Pytorch	78.428	94.110
SE-ResNet101	Our porting	78.396	94.258
SE-ResNet101	Caffe	78.25	94.28
ResNeXt101_32x4d	Our porting	78.188	93.886
FBResNet152	Torch7	77.84	93.84
SE-ResNet50	Caffe	77.63	93.64
SE-ResNet50	Our porting	77.636	93.752
DenseNet161	Pytorch	77.560	93.798
ResNet101	Pytorch	77.438	93.672
FBResNet152	Our porting	77.386	93.594
InceptionV3	Pytorch	77.294	93.454
DenseNet201	Pytorch	77.152	93.548
DualPathNet68b_5k	Our porting	77.034	93.590
CaffeResnet101	Caffe	76.400	92.900
CaffeResnet101	Our porting	76.200	92.766
DenseNet169	Pytorch	76.026	92.992
ResNet50	Pytorch	76.002	92.980
DualPathNet68	Our porting	75.868	92.774
DenseNet121	Pytorch	74.646	92.136
VGG19_BN	Pytorch	74.266	92.066
NASNet-A-Mobile	Tensorflow	74.0	91.6
NASNet-A-Mobile	Our porting	74.080	91.740
ResNet34	Pytorch	73.554	91.456
BNInception	Our porting	73.524	91.562
VGG16_BN	Pytorch	73.518	91.608
VGG19	Pytorch	72.080	90.822
VGG16	Pytorch	71.636	90.354
VGG13_BN	Pytorch	71.508	90.494
VGG11_BN	Pytorch	70.452	89.818
ResNet18	Pytorch	70.142	89.274
VGG13	Pytorch	69.662	89.264
VGG11	Pytorch	68.970	88.746
SqueezeNet1_1	Pytorch	58.250	80.800
SqueezeNet1_0	Pytorch	58.108	80.428
Alexnet	Pytorch	56.432	79.194

Notes:

the Pytorch version of ResNet152 is not a porting of the Torch7 but has been retrained by facebook.
For the PolyNet evaluation each image was resized to 378x378 without preserving the aspect ratio and then the central 331×331 patch from the resulting image was used.

Beware, the accuracy reported here is not always representative of the transferable capacity of the network on other tasks and datasets. You must try them all! :P

Reproducing results

Please see Compute imagenet validation metrics

Documentation

Available models

NASNet*

Source: TensorFlow Slim repo

nasnetalarge(num_classes=1000, pretrained='imagenet')
nasnetalarge(num_classes=1001, pretrained='imagenet+background')
nasnetamobile(num_classes=1000, pretrained='imagenet')

FaceBook ResNet*

Source: Torch7 repo of FaceBook

There are a bit different from the ResNet* of torchvision. ResNet152 is currently the only one available.

fbresnet152(num_classes=1000, pretrained='imagenet')

Caffe ResNet*

Source: Caffe repo of KaimingHe

cafferesnet101(num_classes=1000, pretrained='imagenet')

Inception*

Source: TensorFlow Slim repo and Pytorch/Vision repo for inceptionv3

inceptionresnetv2(num_classes=1000, pretrained='imagenet')
inceptionresnetv2(num_classes=1001, pretrained='imagenet+background')
inceptionv4(num_classes=1000, pretrained='imagenet')
inceptionv4(num_classes=1001, pretrained='imagenet+background')
inceptionv3(num_classes=1000, pretrained='imagenet')

BNInception

Source: Trained with Caffe by Xiong Yuanjun

bninception(num_classes=1000, pretrained='imagenet')

ResNeXt*

Source: ResNeXt repo of FaceBook

resnext101_32x4d(num_classes=1000, pretrained='imagenet')
resnext101_62x4d(num_classes=1000, pretrained='imagenet')

DualPathNetworks

Source: MXNET repo of Chen Yunpeng

The porting has been made possible by Ross Wightman in his PyTorch repo.

As you can see here DualPathNetworks allows you to try different scales. The default one in this repo is 0.875 meaning that the original input size is 256 before croping to 224.

dpn68(num_classes=1000, pretrained='imagenet')
dpn98(num_classes=1000, pretrained='imagenet')
dpn131(num_classes=1000, pretrained='imagenet')
dpn68b(num_classes=1000, pretrained='imagenet+5k')
dpn92(num_classes=1000, pretrained='imagenet+5k')
dpn107(num_classes=1000, pretrained='imagenet+5k')

'imagenet+5k' means that the network has been pretrained on imagenet5k before being finetuned on imagenet1k.

Xception

Source: Keras repo

The porting has been made possible by T Standley.

xception(num_classes=1000, pretrained='imagenet')

SENet*

Source: Caffe repo of Jie Hu

senet154(num_classes=1000, pretrained='imagenet')
se_resnet50(num_classes=1000, pretrained='imagenet')
se_resnet101(num_classes=1000, pretrained='imagenet')
se_resnet152(num_classes=1000, pretrained='imagenet')
se_resnext50_32x4d(num_classes=1000, pretrained='imagenet')
se_resnext101_32x4d(num_classes=1000, pretrained='imagenet')

PNASNet*

Source: TensorFlow Slim repo

pnasnet5large(num_classes=1000, pretrained='imagenet')
pnasnet5large(num_classes=1001, pretrained='imagenet+background')

PolyNet

Source: Caffe repo of the CUHK Multimedia Lab

polynet(num_classes=1000, pretrained='imagenet')

TorchVision

Source: Pytorch/Vision repo

(inceptionv3 included in Inception*)

resnet18(num_classes=1000, pretrained='imagenet')
resnet34(num_classes=1000, pretrained='imagenet')
resnet50(num_classes=1000, pretrained='imagenet')
resnet101(num_classes=1000, pretrained='imagenet')
resnet152(num_classes=1000, pretrained='imagenet')
densenet121(num_classes=1000, pretrained='imagenet')
densenet161(num_classes=1000, pretrained='imagenet')
densenet169(num_classes=1000, pretrained='imagenet')
densenet201(num_classes=1000, pretrained='imagenet')
squeezenet1_0(num_classes=1000, pretrained='imagenet')
squeezenet1_1(num_classes=1000, pretrained='imagenet')
alexnet(num_classes=1000, pretrained='imagenet')
vgg11(num_classes=1000, pretrained='imagenet')
vgg13(num_classes=1000, pretrained='imagenet')
vgg16(num_classes=1000, pretrained='imagenet')
vgg19(num_classes=1000, pretrained='imagenet')
vgg11_bn(num_classes=1000, pretrained='imagenet')
vgg13_bn(num_classes=1000, pretrained='imagenet')
vgg16_bn(num_classes=1000, pretrained='imagenet')
vgg19_bn(num_classes=1000, pretrained='imagenet')

Model API

Once a pretrained model has been loaded, you can use it that way.

Important note: All image must be loaded using PIL which scales the pixel values between 0 and 1.

`model.input_size`

Attribut of type list composed of 3 numbers:

number of color channels,
height of the input image,
width of the input image.

Example:

[3, 299, 299] for inception* networks,
[3, 224, 224] for resnet* networks.

`model.input_space`

Attribut of type str representating the color space of the image. Can be RGB or BGR.

`model.input_range`

Attribut of type list composed of 2 numbers:

min pixel value,
max pixel value.

Example:

[0, 1] for resnet* and inception* networks,
[0, 255] for bninception network.

`model.mean`

Attribut of type list composed of 3 numbers which are used to normalize the input image (substract "color-channel-wise").

Example:

[0.5, 0.5, 0.5] for inception* networks,
[0.485, 0.456, 0.406] for resnet* networks.

`model.std`

Attribut of type list composed of 3 numbers which are used to normalize the input image (divide "color-channel-wise").

Example:

[0.5, 0.5, 0.5] for inception* networks,
[0.229, 0.224, 0.225] for resnet* networks.

`model.features`

/!\ work in progress (may not be available)

Method which is used to extract the features from the image.

Example when the model is loaded using fbresnet152:

print(input_224.size())            # (1,3,224,224)
output = model.features(input_224) 
print(output.size())               # (1,2048,1,1)

# print(input_448.size())          # (1,3,448,448)
output = model.features(input_448)
# print(output.size())             # (1,2048,7,7)

`model.logits`

/!\ work in progress (may not be available)

Method which is used to classify the features from the image.

Example when the model is loaded using fbresnet152:

output = model.features(input_224) 
print(output.size())               # (1,2048, 1, 1)
output = model.logits(output)
print(output.size())               # (1,1000)

`model.forward`

Method used to call model.features and model.logits. It can be overwritten as desired.

Note: A good practice is to use model.__call__ as your function of choice to forward an input to your model. See the example bellow.

# Without model.__call__
output = model.forward(input_224)
print(output.size())      # (1,1000)

# With model.__call__
output = model(input_224)
print(output.size())      # (1,1000)

`model.last_linear`

Attribut of type nn.Linear. This module is the last one to be called during the forward pass.

Can be replaced by an adapted nn.Linear for fine tuning.
Can be replaced by pretrained.utils.Identity for features extraction.

Example when the model is loaded using fbresnet152:

print(input_224.size())            # (1,3,224,224)
output = model.features(input_224) 
print(output.size())               # (1,2048,1,1)
output = model.logits(output)
print(output.size())               # (1,1000)

# fine tuning
dim_feats = model.last_linear.in_features # =2048
nb_classes = 4
model.last_linear = nn.Linear(dim_feats, nb_classes)
output = model(input_224)
print(output.size())               # (1,4)

# features extraction
model.last_linear = pretrained.utils.Identity()
output = model(input_224)
print(output.size())               # (1,2048)

Reproducing

Hand porting of ResNet152

th pretrainedmodels/fbresnet/resnet152_dump.lua
python pretrainedmodels/fbresnet/resnet152_load.py

Automatic porting of ResNeXt

https://github.com/clcarwin/convert_torch_to_pytorch

Hand porting of NASNet, InceptionV4 and InceptionResNetV2

https://github.com/Cadene/tensorflow-model-zoo.torch

Acknowledgement

Thanks to the deep learning community and especially to the contributers of the pytorch ecosystem.

DrShutkot/pretrained-models.pytorch

Pretrained models for Pytorch (Work in progress)

Summary

Installation

Install from pip

Install from repo

Quick examples

Few use cases

Compute imagenet logits

Compute imagenet evaluation metrics

Evaluation on imagenet

Accuracy on validation set (single model)

Reproducing results

Documentation

Available models

NASNet*

FaceBook ResNet*

Caffe ResNet*

Inception*

BNInception

ResNeXt*

DualPathNetworks

Xception

SENet*

PNASNet*

PolyNet

TorchVision

Model API

model.input_size

model.input_space

model.input_range

model.mean

model.std

model.features

model.logits

model.forward

model.last_linear

Reproducing

Hand porting of ResNet152

Automatic porting of ResNeXt

Hand porting of NASNet, InceptionV4 and InceptionResNetV2

Acknowledgement

`model.input_size`

`model.input_space`

`model.input_range`

`model.mean`

`model.std`

`model.features`

`model.logits`

`model.forward`

`model.last_linear`