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An improved version of the AlexNet model, adding parameter initialization from ResNet.

Project description


Update (Feb 16, 2020)

Now you can install this library directly using pip!

pip3 install --upgrade alexnet_pytorch

Update (Feb 13, 2020)

The update is for ease of use and deployment.

It is also now incredibly simple to load a pretrained model with a new number of classes for transfer learning:

from alexnet_pytorch import AlexNet
model = 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 choice hybrid training precision + dynamic loss amplified version, if you need to learn more and details about apex tools, please visit


This repository contains an op-for-op PyTorch reimplementation of AlexNet.

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 models
  • Use AlexNet models for classification or feature extraction

Upcoming features: In the next few days, you will be able to:

  • Quickly finetune an AlexNet on your own dataset
  • Export AlexNet models for production

Table of contents

  1. About AlexNet
  2. Model Description
  3. Installation
  4. Usage
  5. Contributing

About AlexNet

If 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 Description

AlexNet 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 structure Top-1 error Top-5 error
alexnet 43.48 20.93


Install from pypi:

pip install alexnet_pytorch

Install from source:

git clone
cd AlexNet-PyTorch
pip install -e .


Loading pretrained models

Load an AlexNet:

from alexnet_pytorch import AlexNet
model = AlexNet.from_name('alexnet')

Load a pretrained AlexNet:

from alexnet_pytorch import AlexNet
model = AlexNet.from_pretrained('alexnet')

Example: Classification

We assume that in your current directory, there is a img.jpg file and a labels_map.txt file (ImageNet class names). These are both included in examples/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), where H and W are expected to be at least 224. The images have to be loaded in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].

Here's a sample execution.

import json

import torch
import torchvision.transforms as transforms
from PIL import Image

from alexnet_pytorch import AlexNet

# Open image
input_image ="img.jpg")

# Preprocess image
preprocess = transforms.Compose([
  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 names
labels_map = json.load(open("labels_map.txt"))
labels_map = [labels_map[str(i)] for i in range(1000)]

# Classify with AlexNet
model = AlexNet.from_pretrained("alexnet")

# move the input and model to GPU for speed if available
if torch.cuda.is_available():
  input_batch ="cuda")"cuda")

with torch.no_grad():
  logits = model(input_batch)
preds = torch.topk(logits, k=5).indices.squeeze(0).tolist()

for idx in preds:
  label = labels_map[idx]
  prob = torch.softmax(logits, dim=1)[0, idx].item()
  print(f"{label:<75} ({prob * 100:.2f}%)")

Example: Feature Extraction

You can easily extract features with model.extract_features:

import torch
from alexnet_pytorch import AlexNet
model = 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 ONNX

Exporting to ONNX for deploying to production is now simple:

import torch 
from alexnet_pytorch import AlexNet

model = AlexNet.from_pretrained('alexnet')
dummy_input = torch.randn(16, 3, 224, 224)

torch.onnx.export(model, dummy_input, "demo.onnx", verbose=True)

Example: Visual

cd $REPO$/framework

Then open the browser and type in the browser address

Enjoy it.


See examples/imagenet for details about evaluating on ImageNet.

For more datasets result. Please see research/


If 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!


ImageNet Classification with Deep Convolutional Neural Networks

Alex Krizhevsky,Ilya Sutskever,Geoffrey E. Hinton


We 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.


title:{ImageNet Classification with Deep Convolutional Neural Networks},
author:{Alex Krizhevsky,Ilya Sutskever,Geoffrey E. Hinton},

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