Package: alexnet-pytorch
AlexNet-PyTorchUpdate (Feb 16, 2020)Now you can install this library directly using pip!pip3 install --upgrade alexnet_pytorchUpdate (Feb 13, 2020)The update is for ease of use and deployment.Example: Export to ONNXExample: Extract featuresExample: VisualIt is also now incredibly simple to load a pretrained model with a new number of classes for transfer learning:fromalexnet_pytorchimportAlexNetmodel=AlexNet.from_pretrained('alexnet',num_classes=10)Update (January 15, 2020)This update allows you to use NVIDIA's Apex tool for accelerated training. By default choicehybrid training precision+dynamic loss amplifiedversion, if you need to learn more and details aboutapextools, please visithttps://github.com/NVIDIA/apex.OverviewThis repository contains an op-for-op PyTorch reimplementation ofAlexNet.The goal of this implementation is to be simple, highly extensible, and easy to integrate into your own projects. This implementation is a work in progress -- new features are currently being implemented.At the moment, you can easily:Load pretrained AlexNet modelsUse AlexNet models for classification or feature extractionUpcoming features: In the next few days, you will be able to:Quickly finetune an AlexNet on your own datasetExport AlexNet models for productionTable of contentsAbout AlexNetModel DescriptionInstallationUsageLoad pretrained modelsExample: ClassifyExample: Extract featuresExample: Export to ONNXExample: VisualContributingAbout AlexNetIf you're new to AlexNets, here is an explanation straight from the official PyTorch implementation:Current approaches to object recognition make essential use of machine learning methods. To improve their performance, we can collect larger datasets, learn more powerful models, and use better techniques for preventing overfitting. Until recently, datasets of labeled images were relatively
small — on the order of tens of thousands of images (e.g., NORB [16], Caltech-101/256 [8, 9], and
CIFAR-10/100 [12]). Simple recognition tasks can be solved quite well with datasets of this size,
especially if they are augmented with label-preserving transformations. For example, the currentbest error rate on the MNIST digit-recognition task (<0.3%) approaches human performance [4].
But objects in realistic settings exhibit considerable variability, so to learn to recognize them it is
necessary to use much larger training sets. And indeed, the shortcomings of small image datasets
have been widely recognized (e.g., Pinto et al. [21]), but it has only recently become possible to collect labeled datasets with millions of images. The new larger datasets include LabelMe [23], which
consists of hundreds of thousands of fully-segmented images, and ImageNet [6], which consists of
over 15 million labeled high-resolution images in over 22,000 categories.Model DescriptionAlexNet competed in the ImageNet Large Scale Visual Recognition Challenge on September 30, 2012. The network achieved a top-5 error of 15.3%, more than 10.8 percentage points lower than that of the runner up. The original paper's primary result was that the depth of the model was essential for its high performance, which was computationally expensive, but made feasible due to the utilization of graphics processing units (GPUs) during training.The 1-crop error rates on the imagenet dataset with the pretrained model are listed below.Model structureTop-1 errorTop-5 erroralexnet43.4820.93InstallationInstall from pypi:pipinstallalexnet_pytorchInstall from source:gitclonehttps://github.com/lornatang/AlexNet-PyTorch.gitcdAlexNet-PyTorch
pipinstall-e.UsageLoading pretrained modelsLoad an AlexNet:fromalexnet_pytorchimportAlexNetmodel=AlexNet.from_name('alexnet')Load a pretrained AlexNet:fromalexnet_pytorchimportAlexNetmodel=AlexNet.from_pretrained('alexnet')Example: ClassificationWe assume that in your current directory, there is aimg.jpgfile and alabels_map.txtfile (ImageNet class names). These are both included inexamples/simple.All pre-trained models expect input images normalized in the same way,
i.e. mini-batches of 3-channel RGB images of shape(3 x H x W), whereHandWare expected to be at least224.
The images have to be loaded in to a range of[0, 1]and then normalized usingmean = [0.485, 0.456, 0.406]andstd = [0.229, 0.224, 0.225].Here's a sample execution.importjsonimporttorchimporttorchvision.transformsastransformsfromPILimportImagefromalexnet_pytorchimportAlexNet# Open imageinput_image=Image.open("img.jpg")# Preprocess imagepreprocess=transforms.Compose([transforms.Resize(256),transforms.CenterCrop(224),transforms.ToTensor(),transforms.Normalize(mean=[0.485,0.456,0.406],std=[0.229,0.224,0.225]),])input_tensor=preprocess(input_image)input_batch=input_tensor.unsqueeze(0)# create a mini-batch as expected by the model# Load class nameslabels_map=json.load(open("labels_map.txt"))labels_map=[labels_map[str(i)]foriinrange(1000)]# Classify with AlexNetmodel=AlexNet.from_pretrained("alexnet")model.eval()# move the input and model to GPU for speed if availableiftorch.cuda.is_available():input_batch=input_batch.to("cuda")model.to("cuda")withtorch.no_grad():logits=model(input_batch)preds=torch.topk(logits,k=5).indices.squeeze(0).tolist()print("-----")foridxinpreds:label=labels_map[idx]prob=torch.softmax(logits,dim=1)[0,idx].item()print(f"{label:<75}({prob*100:.2f}%)")Example: Feature ExtractionYou can easily extract features withmodel.extract_features:importtorchfromalexnet_pytorchimportAlexNetmodel=AlexNet.from_pretrained('alexnet')# ... image preprocessing as in the classification example ...inputs=torch.randn(1,3,224,224)print(inputs.shape)# torch.Size([1, 3, 224, 224])features=model.extract_features(inputs)print(features.shape)# torch.Size([1, 256, 6, 6])Example: Export to ONNXExporting to ONNX for deploying to production is now simple:importtorchfromalexnet_pytorchimportAlexNetmodel=AlexNet.from_pretrained('alexnet')dummy_input=torch.randn(16,3,224,224)torch.onnx.export(model,dummy_input,"demo.onnx",verbose=True)Example: Visualcd $REPO$/framework
sh start.shThen open the browser and type in the browser addresshttp://127.0.0.1:20000/.Enjoy it.ImageNetSeeexamples/imagenetfor details about evaluating on ImageNet.For more datasets result. Please seeresearch/README.md.ContributingIf you find a bug, create a GitHub issue, or even better, submit a pull request. Similarly, if you have questions, simply post them as GitHub issues.I look forward to seeing what the community does with these models!CreditImageNet Classification with Deep Convolutional Neural NetworksAlex Krizhevsky,Ilya Sutskever,Geoffrey E. HintonAbstractWe trained a large, deep convolutional neural network to classify the 1.2 million
high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5%
and 17.0% which is considerably better than the previous state-of-the-art. The
neural network, which has 60 million parameters and 650,000 neurons, consists
of five convolutional layers, some of which are followed by max-pooling layers,
and three fully-connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully-connected
layers we employed a recently-developed regularization method called “dropout”
that proved to be very effective. We also entered a variant of this model in the
ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%,
compared to 26.2% achieved by the second-best entry.paper@article{AlexNet,
title:{ImageNet Classification with Deep Convolutional Neural Networks},
author:{Alex Krizhevsky,Ilya Sutskever,Geoffrey E. Hinton},
journal={nips},
year={2012}
}