The goal of this repo is:
News:
pip install pretrainedmodels, pretrainedmodels.model_names, pretrainedmodels.pretrained_settingspython setup.py installgit pull is needed)model.features(input), model.logits(features), model.forward(input), model.last_linear)pip install pretrainedmodelsgit clone https://github.com/Cadene/pretrained-models.pytorch.gitcd pretrained-models.pytorchpython setup.py installpretrainedmodels:import pretrainedmodels
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']
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}}
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
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
output_features = model.features(input) # 1x14x14x2048 size may differ
output_logits = model.logits(output_features) # 1x1000
$ 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'
$ python examples/imagenet_eval.py /local/common-data/imagenet_2012/images -a nasnetalarge -b 20 -e
> * Acc@1 92.693, Acc@5 96.13
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:
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
Please see Compute imagenet validation metrics
Source: TensorFlow Slim repo
nasnetalarge(num_classes=1000, pretrained='imagenet')nasnetalarge(num_classes=1001, pretrained='imagenet+background')nasnetamobile(num_classes=1000, pretrained='imagenet')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')Source: Caffe repo of KaimingHe
cafferesnet101(num_classes=1000, pretrained='imagenet')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')Source: Trained with Caffe by Xiong Yuanjun
bninception(num_classes=1000, pretrained='imagenet')Source: ResNeXt repo of FaceBook
resnext101_32x4d(num_classes=1000, pretrained='imagenet')resnext101_62x4d(num_classes=1000, pretrained='imagenet')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.
Source: Keras repo
The porting has been made possible by T Standley.
xception(num_classes=1000, pretrained='imagenet')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')Source: TensorFlow Slim repo
pnasnet5large(num_classes=1000, pretrained='imagenet')pnasnet5large(num_classes=1001, pretrained='imagenet+background')Source: Caffe repo of the CUHK Multimedia Lab
polynet(num_classes=1000, pretrained='imagenet')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')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_sizeAttribut of type list composed of 3 numbers:
Example:
[3, 299, 299] for inception* networks,[3, 224, 224] for resnet* networks.model.input_spaceAttribut of type str representating the color space of the image. Can be RGB or BGR.
model.input_rangeAttribut of type list composed of 2 numbers:
Example:
[0, 1] for resnet* and inception* networks,[0, 255] for bninception network.model.meanAttribut 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.stdAttribut 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.forwardMethod 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_linearAttribut of type nn.Linear. This module is the last one to be called during the forward pass.
nn.Linear for fine tuning.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)
th pretrainedmodels/fbresnet/resnet152_dump.lua
python pretrainedmodels/fbresnet/resnet152_load.py
https://github.com/clcarwin/convert_torch_to_pytorch
https://github.com/Cadene/tensorflow-model-zoo.torch
Thanks to the deep learning community and especially to the contributers of the pytorch ecosystem.
FAQs
Pretrained models for Pytorch
We found that pretrainedmodels demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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