%global _empty_manifest_terminate_build 0
Name:		python-pretrainedmodels
Version:	0.7.4
Release:	1
Summary:	Pretrained models for Pytorch
License:	MIT License
URL:		https://github.com/cadene/pretrained-models.pytorch
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/84/0e/be6a0e58447ac16c938799d49bfb5fb7a80ac35e137547fc6cee2c08c4cf/pretrainedmodels-0.7.4.tar.gz
BuildArch:	noarch


%description
PNASNet-5-Large | [Tensorflow](https://github.com/tensorflow/models/tree/master/research/slim) | 82.858 | 96.182
[PNASNet-5-Large](https://github.com/Cadene/pretrained-models.pytorch#pnasnet) | Our porting | 82.736 | 95.992
NASNet-A-Large | [Tensorflow](https://github.com/tensorflow/models/tree/master/research/slim) | 82.693 | 96.163
[NASNet-A-Large](https://github.com/Cadene/pretrained-models.pytorch#nasnet) | Our porting | 82.566 | 96.086
SENet154 | [Caffe](https://github.com/hujie-frank/SENet) | 81.32 | 95.53
[SENet154](https://github.com/Cadene/pretrained-models.pytorch#senet) | Our porting | 81.304 | 95.498
PolyNet | [Caffe](https://github.com/CUHK-MMLAB/polynet) | 81.29 | 95.75
[PolyNet](https://github.com/Cadene/pretrained-models.pytorch#polynet) | Our porting | 81.002 | 95.624
InceptionResNetV2 | [Tensorflow](https://github.com/tensorflow/models/tree/master/slim) | 80.4 | 95.3
InceptionV4 | [Tensorflow](https://github.com/tensorflow/models/tree/master/slim) | 80.2 | 95.3
[SE-ResNeXt101_32x4d](https://github.com/Cadene/pretrained-models.pytorch#senet) | Our porting | 80.236 | 95.028
SE-ResNeXt101_32x4d | [Caffe](https://github.com/hujie-frank/SENet) | 80.19 | 95.04
[InceptionResNetV2](https://github.com/Cadene/pretrained-models.pytorch#inception) | Our porting | 80.170 | 95.234
[InceptionV4](https://github.com/Cadene/pretrained-models.pytorch#inception) | Our porting | 80.062 | 94.926
[DualPathNet107_5k](https://github.com/Cadene/pretrained-models.pytorch#dualpathnetworks) | Our porting | 79.746 | 94.684
ResNeXt101_64x4d | [Torch7](https://github.com/facebookresearch/ResNeXt) | 79.6 | 94.7
[DualPathNet131](https://github.com/Cadene/pretrained-models.pytorch#dualpathnetworks) | Our porting | 79.432 | 94.574
[DualPathNet92_5k](https://github.com/Cadene/pretrained-models.pytorch#dualpathnetworks) | Our porting | 79.400 | 94.620
[DualPathNet98](https://github.com/Cadene/pretrained-models.pytorch#dualpathnetworks) | Our porting | 79.224 | 94.488
[SE-ResNeXt50_32x4d](https://github.com/Cadene/pretrained-models.pytorch#senet) | Our porting | 79.076 | 94.434
SE-ResNeXt50_32x4d | [Caffe](https://github.com/hujie-frank/SENet) | 79.03 | 94.46
[Xception](https://github.com/Cadene/pretrained-models.pytorch#xception) | [Keras](https://github.com/keras-team/keras/blob/master/keras/applications/xception.py) | 79.000 | 94.500
[ResNeXt101_64x4d](https://github.com/Cadene/pretrained-models.pytorch#resnext) | Our porting | 78.956 | 94.252
[Xception](https://github.com/Cadene/pretrained-models.pytorch#xception) | Our porting | 78.888 | 94.292
ResNeXt101_32x4d | [Torch7](https://github.com/facebookresearch/ResNeXt) | 78.8 | 94.4
SE-ResNet152 | [Caffe](https://github.com/hujie-frank/SENet) | 78.66 | 94.46
[SE-ResNet152](https://github.com/Cadene/pretrained-models.pytorch#senet) | Our porting | 78.658 | 94.374
ResNet152 | [Pytorch](https://github.com/pytorch/vision#models) | 78.428 | 94.110
[SE-ResNet101](https://github.com/Cadene/pretrained-models.pytorch#senet) | Our porting | 78.396 | 94.258
SE-ResNet101 | [Caffe](https://github.com/hujie-frank/SENet) | 78.25 | 94.28
[ResNeXt101_32x4d](https://github.com/Cadene/pretrained-models.pytorch#resnext) | Our porting | 78.188 | 93.886
FBResNet152 | [Torch7](https://github.com/facebook/fb.resnet.torch) | 77.84 | 93.84
SE-ResNet50 | [Caffe](https://github.com/hujie-frank/SENet) | 77.63 | 93.64
[SE-ResNet50](https://github.com/Cadene/pretrained-models.pytorch#senet) | Our porting | 77.636 | 93.752
[DenseNet161](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 77.560 | 93.798
[ResNet101](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 77.438 | 93.672
[FBResNet152](https://github.com/Cadene/pretrained-models.pytorch#facebook-resnet) | Our porting | 77.386 | 93.594
[InceptionV3](https://github.com/Cadene/pretrained-models.pytorch#inception) | [Pytorch](https://github.com/pytorch/vision#models) | 77.294 | 93.454
[DenseNet201](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 77.152 | 93.548
[DualPathNet68b_5k](https://github.com/Cadene/pretrained-models.pytorch#dualpathnetworks) | Our porting | 77.034 | 93.590
[CaffeResnet101](https://github.com/Cadene/pretrained-models.pytorch#caffe-resnet) | [Caffe](https://github.com/KaimingHe/deep-residual-networks) | 76.400 | 92.900
[CaffeResnet101](https://github.com/Cadene/pretrained-models.pytorch#caffe-resnet) | Our porting | 76.200 | 92.766
[DenseNet169](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 76.026 | 92.992
[ResNet50](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 76.002 | 92.980
[DualPathNet68](https://github.com/Cadene/pretrained-models.pytorch#dualpathnetworks) | Our porting | 75.868 | 92.774
[DenseNet121](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 74.646 | 92.136
[VGG19_BN](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 74.266 | 92.066
NASNet-A-Mobile | [Tensorflow](https://github.com/tensorflow/models/tree/master/research/slim) | 74.0 | 91.6
[NASNet-A-Mobile](https://github.com/veronikayurchuk/pretrained-models.pytorch/blob/master/pretrainedmodels/models/nasnet_mobile.py) | Our porting | 74.080 | 91.740
[ResNet34](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 73.554 | 91.456
[BNInception](https://github.com/Cadene/pretrained-models.pytorch#bninception) | Our porting | 73.524 | 91.562
[VGG16_BN](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 73.518 | 91.608
[VGG19](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 72.080 | 90.822
[VGG16](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 71.636 | 90.354
[VGG13_BN](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 71.508 | 90.494
[VGG11_BN](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 70.452 | 89.818
[ResNet18](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 70.142 | 89.274
[VGG13](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 69.662 | 89.264
[VGG11](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 68.970 | 88.746
[SqueezeNet1_1](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 58.250 | 80.800
[SqueezeNet1_0](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 58.108 | 80.428
[Alexnet](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 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](https://github.com/Cadene/pretrained-models.pytorch#compute-imagenet-validation-metrics)
## Documentation
### Available models
#### NASNet*
Source: [TensorFlow Slim repo](https://github.com/tensorflow/models/tree/master/research/slim)
- `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](https://github.com/facebook/fb.resnet.torch)
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](https://github.com/KaimingHe/deep-residual-networks)
- `cafferesnet101(num_classes=1000, pretrained='imagenet')`
#### Inception*
Source: [TensorFlow Slim repo](https://github.com/tensorflow/models/tree/master/slim) and [Pytorch/Vision repo](https://github.com/pytorch/vision/tree/master/torchvision) 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](https://github.com/Cadene/tensorflow-model-zoo.torch/pull/2) by [Xiong Yuanjun](http://yjxiong.me)
- `bninception(num_classes=1000, pretrained='imagenet')`
#### ResNeXt*
Source: [ResNeXt repo of FaceBook](https://github.com/facebookresearch/ResNeXt)
- `resnext101_32x4d(num_classes=1000, pretrained='imagenet')`
- `resnext101_62x4d(num_classes=1000, pretrained='imagenet')`
#### DualPathNetworks
Source: [MXNET repo of Chen Yunpeng](https://github.com/cypw/DPNs)
The porting has been made possible by [Ross Wightman](http://rwightman.com) in his [PyTorch repo](https://github.com/rwightman/pytorch-dpn-pretrained).
As you can see [here](https://github.com/rwightman/pytorch-dpn-pretrained) 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](https://github.com/keras-team/keras/blob/master/keras/applications/xception.py)
The porting has been made possible by [T Standley](https://github.com/tstandley/Xception-PyTorch).
- `xception(num_classes=1000, pretrained='imagenet')`
#### SENet*
Source: [Caffe repo of Jie Hu](https://github.com/hujie-frank/SENet)
- `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](https://github.com/tensorflow/models/tree/master/research/slim)
- `pnasnet5large(num_classes=1000, pretrained='imagenet')`
- `pnasnet5large(num_classes=1001, pretrained='imagenet+background')`
#### PolyNet
Source: [Caffe repo of the CUHK Multimedia Lab](https://github.com/CUHK-MMLAB/polynet)
- `polynet(num_classes=1000, pretrained='imagenet')`
#### TorchVision
Source: [Pytorch/Vision repo](https://github.com/pytorch/vision/tree/master/torchvision)
(`inceptionv3` included in [Inception*](https://github.com/Cadene/pretrained-models.pytorch#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`:
```python
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`:
```python
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.
```python
# 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`:
```python
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.

%package -n python3-pretrainedmodels
Summary:	Pretrained models for Pytorch
Provides:	python-pretrainedmodels
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-pretrainedmodels
PNASNet-5-Large | [Tensorflow](https://github.com/tensorflow/models/tree/master/research/slim) | 82.858 | 96.182
[PNASNet-5-Large](https://github.com/Cadene/pretrained-models.pytorch#pnasnet) | Our porting | 82.736 | 95.992
NASNet-A-Large | [Tensorflow](https://github.com/tensorflow/models/tree/master/research/slim) | 82.693 | 96.163
[NASNet-A-Large](https://github.com/Cadene/pretrained-models.pytorch#nasnet) | Our porting | 82.566 | 96.086
SENet154 | [Caffe](https://github.com/hujie-frank/SENet) | 81.32 | 95.53
[SENet154](https://github.com/Cadene/pretrained-models.pytorch#senet) | Our porting | 81.304 | 95.498
PolyNet | [Caffe](https://github.com/CUHK-MMLAB/polynet) | 81.29 | 95.75
[PolyNet](https://github.com/Cadene/pretrained-models.pytorch#polynet) | Our porting | 81.002 | 95.624
InceptionResNetV2 | [Tensorflow](https://github.com/tensorflow/models/tree/master/slim) | 80.4 | 95.3
InceptionV4 | [Tensorflow](https://github.com/tensorflow/models/tree/master/slim) | 80.2 | 95.3
[SE-ResNeXt101_32x4d](https://github.com/Cadene/pretrained-models.pytorch#senet) | Our porting | 80.236 | 95.028
SE-ResNeXt101_32x4d | [Caffe](https://github.com/hujie-frank/SENet) | 80.19 | 95.04
[InceptionResNetV2](https://github.com/Cadene/pretrained-models.pytorch#inception) | Our porting | 80.170 | 95.234
[InceptionV4](https://github.com/Cadene/pretrained-models.pytorch#inception) | Our porting | 80.062 | 94.926
[DualPathNet107_5k](https://github.com/Cadene/pretrained-models.pytorch#dualpathnetworks) | Our porting | 79.746 | 94.684
ResNeXt101_64x4d | [Torch7](https://github.com/facebookresearch/ResNeXt) | 79.6 | 94.7
[DualPathNet131](https://github.com/Cadene/pretrained-models.pytorch#dualpathnetworks) | Our porting | 79.432 | 94.574
[DualPathNet92_5k](https://github.com/Cadene/pretrained-models.pytorch#dualpathnetworks) | Our porting | 79.400 | 94.620
[DualPathNet98](https://github.com/Cadene/pretrained-models.pytorch#dualpathnetworks) | Our porting | 79.224 | 94.488
[SE-ResNeXt50_32x4d](https://github.com/Cadene/pretrained-models.pytorch#senet) | Our porting | 79.076 | 94.434
SE-ResNeXt50_32x4d | [Caffe](https://github.com/hujie-frank/SENet) | 79.03 | 94.46
[Xception](https://github.com/Cadene/pretrained-models.pytorch#xception) | [Keras](https://github.com/keras-team/keras/blob/master/keras/applications/xception.py) | 79.000 | 94.500
[ResNeXt101_64x4d](https://github.com/Cadene/pretrained-models.pytorch#resnext) | Our porting | 78.956 | 94.252
[Xception](https://github.com/Cadene/pretrained-models.pytorch#xception) | Our porting | 78.888 | 94.292
ResNeXt101_32x4d | [Torch7](https://github.com/facebookresearch/ResNeXt) | 78.8 | 94.4
SE-ResNet152 | [Caffe](https://github.com/hujie-frank/SENet) | 78.66 | 94.46
[SE-ResNet152](https://github.com/Cadene/pretrained-models.pytorch#senet) | Our porting | 78.658 | 94.374
ResNet152 | [Pytorch](https://github.com/pytorch/vision#models) | 78.428 | 94.110
[SE-ResNet101](https://github.com/Cadene/pretrained-models.pytorch#senet) | Our porting | 78.396 | 94.258
SE-ResNet101 | [Caffe](https://github.com/hujie-frank/SENet) | 78.25 | 94.28
[ResNeXt101_32x4d](https://github.com/Cadene/pretrained-models.pytorch#resnext) | Our porting | 78.188 | 93.886
FBResNet152 | [Torch7](https://github.com/facebook/fb.resnet.torch) | 77.84 | 93.84
SE-ResNet50 | [Caffe](https://github.com/hujie-frank/SENet) | 77.63 | 93.64
[SE-ResNet50](https://github.com/Cadene/pretrained-models.pytorch#senet) | Our porting | 77.636 | 93.752
[DenseNet161](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 77.560 | 93.798
[ResNet101](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 77.438 | 93.672
[FBResNet152](https://github.com/Cadene/pretrained-models.pytorch#facebook-resnet) | Our porting | 77.386 | 93.594
[InceptionV3](https://github.com/Cadene/pretrained-models.pytorch#inception) | [Pytorch](https://github.com/pytorch/vision#models) | 77.294 | 93.454
[DenseNet201](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 77.152 | 93.548
[DualPathNet68b_5k](https://github.com/Cadene/pretrained-models.pytorch#dualpathnetworks) | Our porting | 77.034 | 93.590
[CaffeResnet101](https://github.com/Cadene/pretrained-models.pytorch#caffe-resnet) | [Caffe](https://github.com/KaimingHe/deep-residual-networks) | 76.400 | 92.900
[CaffeResnet101](https://github.com/Cadene/pretrained-models.pytorch#caffe-resnet) | Our porting | 76.200 | 92.766
[DenseNet169](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 76.026 | 92.992
[ResNet50](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 76.002 | 92.980
[DualPathNet68](https://github.com/Cadene/pretrained-models.pytorch#dualpathnetworks) | Our porting | 75.868 | 92.774
[DenseNet121](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 74.646 | 92.136
[VGG19_BN](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 74.266 | 92.066
NASNet-A-Mobile | [Tensorflow](https://github.com/tensorflow/models/tree/master/research/slim) | 74.0 | 91.6
[NASNet-A-Mobile](https://github.com/veronikayurchuk/pretrained-models.pytorch/blob/master/pretrainedmodels/models/nasnet_mobile.py) | Our porting | 74.080 | 91.740
[ResNet34](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 73.554 | 91.456
[BNInception](https://github.com/Cadene/pretrained-models.pytorch#bninception) | Our porting | 73.524 | 91.562
[VGG16_BN](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 73.518 | 91.608
[VGG19](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 72.080 | 90.822
[VGG16](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 71.636 | 90.354
[VGG13_BN](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 71.508 | 90.494
[VGG11_BN](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 70.452 | 89.818
[ResNet18](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 70.142 | 89.274
[VGG13](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 69.662 | 89.264
[VGG11](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 68.970 | 88.746
[SqueezeNet1_1](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 58.250 | 80.800
[SqueezeNet1_0](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 58.108 | 80.428
[Alexnet](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 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](https://github.com/Cadene/pretrained-models.pytorch#compute-imagenet-validation-metrics)
## Documentation
### Available models
#### NASNet*
Source: [TensorFlow Slim repo](https://github.com/tensorflow/models/tree/master/research/slim)
- `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](https://github.com/facebook/fb.resnet.torch)
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](https://github.com/KaimingHe/deep-residual-networks)
- `cafferesnet101(num_classes=1000, pretrained='imagenet')`
#### Inception*
Source: [TensorFlow Slim repo](https://github.com/tensorflow/models/tree/master/slim) and [Pytorch/Vision repo](https://github.com/pytorch/vision/tree/master/torchvision) 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](https://github.com/Cadene/tensorflow-model-zoo.torch/pull/2) by [Xiong Yuanjun](http://yjxiong.me)
- `bninception(num_classes=1000, pretrained='imagenet')`
#### ResNeXt*
Source: [ResNeXt repo of FaceBook](https://github.com/facebookresearch/ResNeXt)
- `resnext101_32x4d(num_classes=1000, pretrained='imagenet')`
- `resnext101_62x4d(num_classes=1000, pretrained='imagenet')`
#### DualPathNetworks
Source: [MXNET repo of Chen Yunpeng](https://github.com/cypw/DPNs)
The porting has been made possible by [Ross Wightman](http://rwightman.com) in his [PyTorch repo](https://github.com/rwightman/pytorch-dpn-pretrained).
As you can see [here](https://github.com/rwightman/pytorch-dpn-pretrained) 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](https://github.com/keras-team/keras/blob/master/keras/applications/xception.py)
The porting has been made possible by [T Standley](https://github.com/tstandley/Xception-PyTorch).
- `xception(num_classes=1000, pretrained='imagenet')`
#### SENet*
Source: [Caffe repo of Jie Hu](https://github.com/hujie-frank/SENet)
- `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](https://github.com/tensorflow/models/tree/master/research/slim)
- `pnasnet5large(num_classes=1000, pretrained='imagenet')`
- `pnasnet5large(num_classes=1001, pretrained='imagenet+background')`
#### PolyNet
Source: [Caffe repo of the CUHK Multimedia Lab](https://github.com/CUHK-MMLAB/polynet)
- `polynet(num_classes=1000, pretrained='imagenet')`
#### TorchVision
Source: [Pytorch/Vision repo](https://github.com/pytorch/vision/tree/master/torchvision)
(`inceptionv3` included in [Inception*](https://github.com/Cadene/pretrained-models.pytorch#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`:
```python
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`:
```python
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.
```python
# 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`:
```python
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.

%package help
Summary:	Development documents and examples for pretrainedmodels
Provides:	python3-pretrainedmodels-doc
%description help
PNASNet-5-Large | [Tensorflow](https://github.com/tensorflow/models/tree/master/research/slim) | 82.858 | 96.182
[PNASNet-5-Large](https://github.com/Cadene/pretrained-models.pytorch#pnasnet) | Our porting | 82.736 | 95.992
NASNet-A-Large | [Tensorflow](https://github.com/tensorflow/models/tree/master/research/slim) | 82.693 | 96.163
[NASNet-A-Large](https://github.com/Cadene/pretrained-models.pytorch#nasnet) | Our porting | 82.566 | 96.086
SENet154 | [Caffe](https://github.com/hujie-frank/SENet) | 81.32 | 95.53
[SENet154](https://github.com/Cadene/pretrained-models.pytorch#senet) | Our porting | 81.304 | 95.498
PolyNet | [Caffe](https://github.com/CUHK-MMLAB/polynet) | 81.29 | 95.75
[PolyNet](https://github.com/Cadene/pretrained-models.pytorch#polynet) | Our porting | 81.002 | 95.624
InceptionResNetV2 | [Tensorflow](https://github.com/tensorflow/models/tree/master/slim) | 80.4 | 95.3
InceptionV4 | [Tensorflow](https://github.com/tensorflow/models/tree/master/slim) | 80.2 | 95.3
[SE-ResNeXt101_32x4d](https://github.com/Cadene/pretrained-models.pytorch#senet) | Our porting | 80.236 | 95.028
SE-ResNeXt101_32x4d | [Caffe](https://github.com/hujie-frank/SENet) | 80.19 | 95.04
[InceptionResNetV2](https://github.com/Cadene/pretrained-models.pytorch#inception) | Our porting | 80.170 | 95.234
[InceptionV4](https://github.com/Cadene/pretrained-models.pytorch#inception) | Our porting | 80.062 | 94.926
[DualPathNet107_5k](https://github.com/Cadene/pretrained-models.pytorch#dualpathnetworks) | Our porting | 79.746 | 94.684
ResNeXt101_64x4d | [Torch7](https://github.com/facebookresearch/ResNeXt) | 79.6 | 94.7
[DualPathNet131](https://github.com/Cadene/pretrained-models.pytorch#dualpathnetworks) | Our porting | 79.432 | 94.574
[DualPathNet92_5k](https://github.com/Cadene/pretrained-models.pytorch#dualpathnetworks) | Our porting | 79.400 | 94.620
[DualPathNet98](https://github.com/Cadene/pretrained-models.pytorch#dualpathnetworks) | Our porting | 79.224 | 94.488
[SE-ResNeXt50_32x4d](https://github.com/Cadene/pretrained-models.pytorch#senet) | Our porting | 79.076 | 94.434
SE-ResNeXt50_32x4d | [Caffe](https://github.com/hujie-frank/SENet) | 79.03 | 94.46
[Xception](https://github.com/Cadene/pretrained-models.pytorch#xception) | [Keras](https://github.com/keras-team/keras/blob/master/keras/applications/xception.py) | 79.000 | 94.500
[ResNeXt101_64x4d](https://github.com/Cadene/pretrained-models.pytorch#resnext) | Our porting | 78.956 | 94.252
[Xception](https://github.com/Cadene/pretrained-models.pytorch#xception) | Our porting | 78.888 | 94.292
ResNeXt101_32x4d | [Torch7](https://github.com/facebookresearch/ResNeXt) | 78.8 | 94.4
SE-ResNet152 | [Caffe](https://github.com/hujie-frank/SENet) | 78.66 | 94.46
[SE-ResNet152](https://github.com/Cadene/pretrained-models.pytorch#senet) | Our porting | 78.658 | 94.374
ResNet152 | [Pytorch](https://github.com/pytorch/vision#models) | 78.428 | 94.110
[SE-ResNet101](https://github.com/Cadene/pretrained-models.pytorch#senet) | Our porting | 78.396 | 94.258
SE-ResNet101 | [Caffe](https://github.com/hujie-frank/SENet) | 78.25 | 94.28
[ResNeXt101_32x4d](https://github.com/Cadene/pretrained-models.pytorch#resnext) | Our porting | 78.188 | 93.886
FBResNet152 | [Torch7](https://github.com/facebook/fb.resnet.torch) | 77.84 | 93.84
SE-ResNet50 | [Caffe](https://github.com/hujie-frank/SENet) | 77.63 | 93.64
[SE-ResNet50](https://github.com/Cadene/pretrained-models.pytorch#senet) | Our porting | 77.636 | 93.752
[DenseNet161](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 77.560 | 93.798
[ResNet101](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 77.438 | 93.672
[FBResNet152](https://github.com/Cadene/pretrained-models.pytorch#facebook-resnet) | Our porting | 77.386 | 93.594
[InceptionV3](https://github.com/Cadene/pretrained-models.pytorch#inception) | [Pytorch](https://github.com/pytorch/vision#models) | 77.294 | 93.454
[DenseNet201](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 77.152 | 93.548
[DualPathNet68b_5k](https://github.com/Cadene/pretrained-models.pytorch#dualpathnetworks) | Our porting | 77.034 | 93.590
[CaffeResnet101](https://github.com/Cadene/pretrained-models.pytorch#caffe-resnet) | [Caffe](https://github.com/KaimingHe/deep-residual-networks) | 76.400 | 92.900
[CaffeResnet101](https://github.com/Cadene/pretrained-models.pytorch#caffe-resnet) | Our porting | 76.200 | 92.766
[DenseNet169](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 76.026 | 92.992
[ResNet50](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 76.002 | 92.980
[DualPathNet68](https://github.com/Cadene/pretrained-models.pytorch#dualpathnetworks) | Our porting | 75.868 | 92.774
[DenseNet121](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 74.646 | 92.136
[VGG19_BN](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 74.266 | 92.066
NASNet-A-Mobile | [Tensorflow](https://github.com/tensorflow/models/tree/master/research/slim) | 74.0 | 91.6
[NASNet-A-Mobile](https://github.com/veronikayurchuk/pretrained-models.pytorch/blob/master/pretrainedmodels/models/nasnet_mobile.py) | Our porting | 74.080 | 91.740
[ResNet34](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 73.554 | 91.456
[BNInception](https://github.com/Cadene/pretrained-models.pytorch#bninception) | Our porting | 73.524 | 91.562
[VGG16_BN](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 73.518 | 91.608
[VGG19](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 72.080 | 90.822
[VGG16](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 71.636 | 90.354
[VGG13_BN](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 71.508 | 90.494
[VGG11_BN](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 70.452 | 89.818
[ResNet18](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 70.142 | 89.274
[VGG13](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 69.662 | 89.264
[VGG11](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 68.970 | 88.746
[SqueezeNet1_1](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 58.250 | 80.800
[SqueezeNet1_0](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 58.108 | 80.428
[Alexnet](https://github.com/Cadene/pretrained-models.pytorch#torchvision) | [Pytorch](https://github.com/pytorch/vision#models) | 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](https://github.com/Cadene/pretrained-models.pytorch#compute-imagenet-validation-metrics)
## Documentation
### Available models
#### NASNet*
Source: [TensorFlow Slim repo](https://github.com/tensorflow/models/tree/master/research/slim)
- `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](https://github.com/facebook/fb.resnet.torch)
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](https://github.com/KaimingHe/deep-residual-networks)
- `cafferesnet101(num_classes=1000, pretrained='imagenet')`
#### Inception*
Source: [TensorFlow Slim repo](https://github.com/tensorflow/models/tree/master/slim) and [Pytorch/Vision repo](https://github.com/pytorch/vision/tree/master/torchvision) 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](https://github.com/Cadene/tensorflow-model-zoo.torch/pull/2) by [Xiong Yuanjun](http://yjxiong.me)
- `bninception(num_classes=1000, pretrained='imagenet')`
#### ResNeXt*
Source: [ResNeXt repo of FaceBook](https://github.com/facebookresearch/ResNeXt)
- `resnext101_32x4d(num_classes=1000, pretrained='imagenet')`
- `resnext101_62x4d(num_classes=1000, pretrained='imagenet')`
#### DualPathNetworks
Source: [MXNET repo of Chen Yunpeng](https://github.com/cypw/DPNs)
The porting has been made possible by [Ross Wightman](http://rwightman.com) in his [PyTorch repo](https://github.com/rwightman/pytorch-dpn-pretrained).
As you can see [here](https://github.com/rwightman/pytorch-dpn-pretrained) 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](https://github.com/keras-team/keras/blob/master/keras/applications/xception.py)
The porting has been made possible by [T Standley](https://github.com/tstandley/Xception-PyTorch).
- `xception(num_classes=1000, pretrained='imagenet')`
#### SENet*
Source: [Caffe repo of Jie Hu](https://github.com/hujie-frank/SENet)
- `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](https://github.com/tensorflow/models/tree/master/research/slim)
- `pnasnet5large(num_classes=1000, pretrained='imagenet')`
- `pnasnet5large(num_classes=1001, pretrained='imagenet+background')`
#### PolyNet
Source: [Caffe repo of the CUHK Multimedia Lab](https://github.com/CUHK-MMLAB/polynet)
- `polynet(num_classes=1000, pretrained='imagenet')`
#### TorchVision
Source: [Pytorch/Vision repo](https://github.com/pytorch/vision/tree/master/torchvision)
(`inceptionv3` included in [Inception*](https://github.com/Cadene/pretrained-models.pytorch#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`:
```python
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`:
```python
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.
```python
# 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`:
```python
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.

%prep
%autosetup -n pretrainedmodels-0.7.4

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-pretrainedmodels -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.7.4-1
- Package Spec generated