https://github.com/lornatang/alexnet-pytorch
An PyTorch implementation AlexNet.Simple, easy to use and efficient
https://github.com/lornatang/alexnet-pytorch
alexnet alexnet-models alexnet-pytorch classifier deep-learning imagenet-classification-challenge pytorch
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An PyTorch implementation AlexNet.Simple, easy to use and efficient
- Host: GitHub
- URL: https://github.com/lornatang/alexnet-pytorch
- Owner: Lornatang
- License: apache-2.0
- Created: 2019-12-16T12:58:47.000Z (over 6 years ago)
- Default Branch: main
- Last Pushed: 2023-06-06T03:22:40.000Z (almost 3 years ago)
- Last Synced: 2025-12-23T11:58:58.224Z (5 months ago)
- Topics: alexnet, alexnet-models, alexnet-pytorch, classifier, deep-learning, imagenet-classification-challenge, pytorch
- Language: Python
- Homepage:
- Size: 1.09 MB
- Stars: 97
- Watchers: 1
- Forks: 41
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# AlexNet-PyTorch
## Overview
This repository contains an op-for-op PyTorch reimplementation of [ImageNet Classification with Deep Convolutional Neural Networks](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf).
## Table of contents
- [AlexNet-PyTorch](#alexnet-pytorch)
- [Overview](#overview)
- [Table of contents](#table-of-contents)
- [Download weights](#download-weights)
- [Download datasets](#download-datasets)
- [How Test and Train](#how-test-and-train)
- [Test](#test)
- [Train model](#train-model)
- [Resume train model](#resume-train-model)
- [Result](#result)
- [Contributing](#contributing)
- [Credit](#credit)
- [ImageNet Classification with Deep Convolutional Neural Networks](#imagenet-classification-with-deep-convolutional-neural-networks)
## Download weights
- [Google Driver](https://drive.google.com/drive/folders/17ju2HN7Y6pyPK2CC_AqnAfTOe9_3hCQ8?usp=sharing)
- [Baidu Driver](https://pan.baidu.com/s/1yNs4rqIb004-NKEdKBJtYg?pwd=llot)
## Download datasets
Contains MNIST, CIFAR10&CIFAR100, TinyImageNet_200, MiniImageNet_1K, ImageNet_1K, Caltech101&Caltech256 and more etc.
- [Google Driver](https://drive.google.com/drive/folders/1f-NSpZc07Qlzhgi6EbBEI1wTkN1MxPbQ?usp=sharing)
- [Baidu Driver](https://pan.baidu.com/s/1arNM38vhDT7p4jKeD4sqwA?pwd=llot)
Please refer to `README.md` in the `data` directory for the method of making a dataset.
## How Test and Train
Both training and testing only need to modify the `config.py` file.
### Test
- line 29: `model_num_classes` change to `1000`.
- line 31: `mode` change to `test`.
- line 79: `model_path` change to `./results/pretrained_models/AlexNet-ImageNet_1K-9df8cd0f.pth.tar`.
```bash
python3 test.py
```
### Train model
- line 29: `model_num_classes` change to `1000`.
- line 31: `mode` change to `train`.
- line 33: `exp_name` change to `AlexNet-ImageNet_1K`.
- line 45: `pretrained_model_path` change to `./results/pretrained_models/AlexNet-ImageNet_1K-9df8cd0f.pth.tar`.
```bash
python3 train.py
```
### Resume train model
- line 29: `model_num_classes` change to `1000`.
- line 31: `mode` change to `train`.
- line 33: `exp_name` change to `AlexNet-ImageNet_1K`.
- line 48: `resume` change to `./samples/AlexNet-ImageNet_1K/epoch_xxx.pth.tar`.
```bash
python3 train.py
```
## Result
Source of original paper results: [https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf](https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf)
In the following table, the top-x error value in `()` indicates the result of the project, and `-` indicates no test.
| Model | Dataset | Top-1 error (val) | Top-5 error (val) |
|:-------:|:-----------:|:-----------------:|:-----------------:|
| AlexNet | ImageNet_1K | 36.7%(**43.8%**) | 15.4%(**21.3%**) |
```bash
# Download `AlexNet-ImageNet_1K-9df8cd0f.pth.tar` weights to `./results/pretrained_models`
# More detail see `README.md`
python3 ./inference.py
```
Input:
Output:
```text
Build AlexNet model successfully.
Load AlexNet model weights `/AlexNet-PyTorch/results/pretrained_models/AlexNet-ImageNet_1K-9df8cd0f.pth.tar` successfully.
tench, Tinca tinca (95.73%)
bolete (1.20%)
triceratops (0.43%)
platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus (0.36%)
croquet ball (0.28%)
```
## Contributing
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!
### Credit
#### ImageNet Classification with Deep Convolutional Neural Networks
*Alex Krizhevsky,Ilya Sutskever,Geoffrey E. Hinton*
##### Abstract
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.
[[Paper]](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)
```bibtex
@article{AlexNet,
title = {ImageNet Classification with Deep Convolutional Neural Networks},
author = {Alex Krizhevsky,Ilya Sutskever,Geoffrey E. Hinton},
journal = {nips},
year = {2012}
}
```