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:

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_MODEL_ZOO variable as follow: export TORCH_MODEL_ZOO="/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.png

> 'nasnetalarge': data/cat.png' 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 92.693, 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	73.522	91.560
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!