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When using pretrained models, PyTorch sets the model to be unfrozen (will have its weights adjusted) by default. So we'll be training the whole model: # Setting up the model # load in pretrained and reset final fully connected res_mod = models.resnet34(pretrained=True) num_ftrs = res_mod.fc.in_features res_mod.fc = nn.Linear(num_ftrs, 2)

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Load a pretrained PyTorch model¶ model_name = "resnet18" model = getattr ( torchvision . models , model_name )( pretrained = True ) model = model . eval () # We grab the TorchScripted model via tracing input_shape = [ 1 , 3 , 224 , 224 ] input_data = torch . randn ( input_shape ) scripted_model = torch . jit . trace ( model , input_data ...

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Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ return _squeezenet('1_0', pretrained, progress, **kwargs) def squeezenet1_1(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> SqueezeNet: r"""SqueezeNet 1.1 model from the ...

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Training the model¶ Now, let’s write a general function to train a model. Here, we will illustrate: Scheduling the learning rate; Saving the best model; In the following, parameter scheduler is an LR scheduler object from torch.optim.lr_scheduler. predict pretrained_model.eval() pretrained_model.freeze() y_hat = pretrained_model(x). Restoring Training State¶. If you don't just want to load weights, but instead restore the full training, do the following

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The CIFAR-10 model is a CNN that composes layers of convolution, pooling, rectified linear unit (ReLU) nonlinearities, and local contrast normalization with a linear classifier on top of it all. We have defined the model in the CAFFE_ROOT/examples/cifar10 directory's cifar10_quick_train_test.prototxt.

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The CIFAR-10 dataset consists of 60,000 32 x 32 colour images. They are divided in 10 classes Loading CIFAR-10 with RecordIO files. Being able to load data efficiently is a very important part of MXNet $ python fine-tune.py --pretrained-model resnext-101 --load-epoch 0000 --gpus 0,1,2,3...

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