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@@ -0,0 +1 @@ +/pretrainedmodels-0.7.4.tar.gz diff --git a/python-pretrainedmodels.spec b/python-pretrainedmodels.spec new file mode 100644 index 0000000..b06073e --- /dev/null +++ b/python-pretrainedmodels.spec @@ -0,0 +1,807 @@ +%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 @@ -0,0 +1 @@ +f0f88cdb721a8587af7cb87fdac4756e pretrainedmodels-0.7.4.tar.gz |