https://github.com/dansuh17/alexnet-pytorch

Pytorch Implementation of AlexNet
https://github.com/dansuh17/alexnet-pytorch

alexnet dataset paper pytorch

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Pytorch Implementation of AlexNet

• Host: GitHub
• URL: https://github.com/dansuh17/alexnet-pytorch
• Owner: dansuh17
• Created: 2018-07-18T03:43:22.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2023-10-03T23:52:17.000Z (about 1 year ago)
• Last Synced: 2024-10-23T10:06:48.798Z (22 days ago)
• Topics: alexnet, dataset, paper, pytorch
• Language: Python
• Size: 2.01 MB
• Stars: 175
• Watchers: 2
• Forks: 57
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

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README

        
# Pytorch implementation of AlexNet

- Now compatible with `pytorch==0.4.0`

This is an implementaiton of AlexNet, as introduced in the paper "ImageNet Classification with Deep Convolutional Neural Networks" by Alex Krizhevsky et al. ([original paper](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf))

This was the first very successful CNN for image classification that led to breakout of deep learning 'hype', as well as the first successful example of utilizing dropout layers.

## Prerequisites

- python >= 3.5

- pytorch==0.4.0

You can install required packages by:

```bash

pip3 install -r requirements.txt

```

## DataSet

This implemenation uses the [ILSVRC 2012 dataset](http://www.image-net.org/challenges/LSVRC/2012/), also known as the 'ImageNet 2012 dataset'.

The data size is dreadfully large (138G!), but this amount of large-sized dataset is required for successful training of AlexNet.

Testing with [Tiny ImageNet](https://tiny-imagenet.herokuapp.com/) or [MNIST](http://yann.lecun.com/exdb/mnist/) could not be done due to their smaller feature sizes (images do not fit the input size 227 x 227).

After downloading the dataset file (i.e., `ILSVRC2012_img_train.tar`), use `extract_imagenet.sh` to extract the entire dataset. 

```bash

extract_imagenet.sh

```

ImageNet 2012's dataset structure is already arranged as `/root/[class]/[img_id].jpeg`, so using `torchvision.datasets.ImageFolder` is convenient.

## Training

```bash

python3 model.py

```

Specify the data path by modifying the constant `TRAIN_IMG_DIR` at the beginning of the script.

Also tune model parameters by modifying constants at the beginning of the script.