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https://github.com/bearpaw/pytorch-classification
Classification with PyTorch.
https://github.com/bearpaw/pytorch-classification
cifar10 cifar100 classification densenet imagenet preresnet pytorch resnet resnext wide-residual-networks wrn
Last synced: 3 days ago
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Classification with PyTorch.
- Host: GitHub
- URL: https://github.com/bearpaw/pytorch-classification
- Owner: bearpaw
- License: mit
- Created: 2017-05-10T05:33:36.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2024-06-18T02:36:02.000Z (8 months ago)
- Last Synced: 2025-01-23T13:04:32.834Z (10 days ago)
- Topics: cifar10, cifar100, classification, densenet, imagenet, preresnet, pytorch, resnet, resnext, wide-residual-networks, wrn
- Language: Python
- Homepage:
- Size: 428 KB
- Stars: 1,706
- Watchers: 30
- Forks: 560
- Open Issues: 34
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-pytorch-list-CNVersion - pytorch-classification - 10/100和ImageNet数据集上的分类框架。 (Tutorials & books & examples｜教程 & 书籍 & 示例 / Other libraries｜其他库:)
- Awesome-pytorch-list - pytorch-classification - 10/100 and ImageNet. (Tutorials, books, & examples / Other libraries:)
README
# pytorch-classification
Classification on CIFAR-10/100 and ImageNet with PyTorch.
## Features
* Unified interface for different network architectures
* Multi-GPU support
* Training progress bar with rich info
* Training log and training curve visualization code (see `./utils/logger.py`)
## Install
* Install [PyTorch](http://pytorch.org/)
* Clone recursively
```
git clone --recursive https://github.com/bearpaw/pytorch-classification.git
```
## Training
Please see the [Training recipes](TRAINING.md) for how to train the models.
## Results
### CIFAR
Top1 error rate on the CIFAR-10/100 benchmarks are reported. You may get different results when training your models with different random seed.
Note that the number of parameters are computed on the CIFAR-10 dataset.
| Model | Params (M) | CIFAR-10 (%) | CIFAR-100 (%) |
| ------------------- | ------------------ | ------------------ | ------------------ |
| alexnet | 2.47 | 22.78 | 56.13 |
| vgg19_bn | 20.04 | 6.66 | 28.05 |
| ResNet-110 | 1.70 | 6.11 | 28.86 |
| PreResNet-110 | 1.70 | 4.94 | 23.65 |
| WRN-28-10 (drop 0.3) | 36.48 | 3.79 | 18.14 |
| ResNeXt-29, 8x64 | 34.43 | 3.69 | 17.38 |
| ResNeXt-29, 16x64 | 68.16 | 3.53 | 17.30 |
| DenseNet-BC (L=100, k=12) | 0.77 | 4.54 | 22.88 |
| DenseNet-BC (L=190, k=40) | 25.62 | 3.32 | 17.17 |
### ImageNet
Single-crop (224x224) validation error rate is reported.
| Model | Params (M) | Top-1 Error (%) | Top-5 Error (%) |
| ------------------- | ------------------ | ------------------ | ------------------ |
| ResNet-18 | 11.69 | 30.09 | 10.78 |
| ResNeXt-50 (32x4d) | 25.03 | 22.6 | 6.29 |
## Pretrained models
Our trained models and training logs are downloadable at [OneDrive](https://mycuhk-my.sharepoint.com/personal/1155056070_link_cuhk_edu_hk/_layouts/15/guestaccess.aspx?folderid=0a380d1fece1443f0a2831b761df31905&authkey=Ac5yBC-FSE4oUJZ2Lsx7I5c).
## Supported Architectures
### CIFAR-10 / CIFAR-100
Since the size of images in CIFAR dataset is `32x32`, popular network structures for ImageNet need some modifications to adapt this input size. The modified models is in the package `models.cifar`:
- [x] [AlexNet](https://arxiv.org/abs/1404.5997)
- [x] [VGG](https://arxiv.org/abs/1409.1556) (Imported from [pytorch-cifar](https://github.com/kuangliu/pytorch-cifar))
- [x] [ResNet](https://arxiv.org/abs/1512.03385)
- [x] [Pre-act-ResNet](https://arxiv.org/abs/1603.05027)
- [x] [ResNeXt](https://arxiv.org/abs/1611.05431) (Imported from [ResNeXt.pytorch](https://github.com/prlz77/ResNeXt.pytorch))
- [x] [Wide Residual Networks](http://arxiv.org/abs/1605.07146) (Imported from [WideResNet-pytorch](https://github.com/xternalz/WideResNet-pytorch))
- [x] [DenseNet](https://arxiv.org/abs/1608.06993)
### ImageNet
- [x] All models in `torchvision.models` (alexnet, vgg, resnet, densenet, inception_v3, squeezenet)
- [x] [ResNeXt](https://arxiv.org/abs/1611.05431)
- [ ] [Wide Residual Networks](http://arxiv.org/abs/1605.07146)
## Contribute
Feel free to create a pull request if you find any bugs or you want to contribute (e.g., more datasets and more network structures).