automatic import of python-pretrainedmodels

author: CoprDistGit <infra@openeuler.org> 2023-04-10 21:44:18 +0000
committer: CoprDistGit <infra@openeuler.org> 2023-04-10 21:44:18 +0000
commit: 4f4a8e22dca7aa5c74de08cc24fb9c5d45efe194 (patch)
tree: fc5f4d2cf88f4a5809e17654b7848f7553cbd802
parent: b57438ce1ede23cb1fb6f5b2d97451bd0e4ce5c4 (diff)
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+/pretrainedmodels-0.7.4.tar.gz
diff --git a/python-pretrainedmodels.spec b/python-pretrainedmodels.spec
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+%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
diff --git a/sources b/sources
new file mode 100644
index 0000000..97b3132
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+f0f88cdb721a8587af7cb87fdac4756e  pretrainedmodels-0.7.4.tar.gz
author	CoprDistGit <infra@openeuler.org>	2023-04-10 21:44:18 +0000
committer	CoprDistGit <infra@openeuler.org>	2023-04-10 21:44:18 +0000
commit	4f4a8e22dca7aa5c74de08cc24fb9c5d45efe194 (patch)
tree	fc5f4d2cf88f4a5809e17654b7848f7553cbd802
parent	b57438ce1ede23cb1fb6f5b2d97451bd0e4ce5c4 (diff)