https://github.com/prlz77/resnext.pytorch

Reproduces ResNet-V3 with pytorch
https://github.com/prlz77/resnext.pytorch

cifar pytorch resnet resnext

Last synced: 14 days ago
JSON representation

Reproduces ResNet-V3 with pytorch

• Host: GitHub
• URL: https://github.com/prlz77/resnext.pytorch
• Owner: prlz77
• License: mit
• Created: 2017-03-30T07:35:24.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2020-08-01T23:01:12.000Z (over 4 years ago)
• Last Synced: 2024-10-23T04:59:30.793Z (21 days ago)
• Topics: cifar, pytorch, resnet, resnext
• Language: Python
• Homepage:
• Size: 154 KB
• Stars: 505
• Watchers: 15
• Forks: 119
• Open Issues: 7
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# ResNeXt.pytorch

Reproduces ResNet-V3 (Aggregated Residual Transformations for Deep Neural Networks) with pytorch.

- [x] Tried on pytorch 1.6

- [x] Trains on Cifar10 and Cifar100

- [x] Upload Cifar Training Curves

- [x] Upload Cifar Trained Models

- [x] Pytorch 0.4.0

- [ ] Train Imagenet

## Download

```bash

git clone https://github.com/prlz77/resnext.pytorch

cd resnext.pytorch

# git checkout R4.0 or R3.0 for backwards compatibility (not recommended).

```

## Usage

To train on Cifar-10 using 2 gpu:

```bash

python train.py ~/DATASETS/cifar.python cifar10 -s ./snapshots --log ./logs --ngpu 2 --learning_rate 0.05 -b 128

```

It should reach *~3.65%* on Cifar-10, and *~17.77%* on Cifar-100.

After train phase, you can check saved model.

**Thanks to [@AppleHolic](https://github.com/AppleHolic) we have now a test script:**

To test on Cifar-10 using 2 gpu:

```bash

python test.py ~/DATASETS/cifar.python cifar10 --ngpu 2 --load ./snapshots/model.pytorch --test_bs 128 

```

## Configurations

From [the original paper](https://arxiv.org/pdf/1611.05431.pdf):

| cardinality |  base_width  | parameters |  Error cifar10   |   error cifar100  | default |

|:-----------:|:------------:|:----------:|:----------------:|:-----------------:|:-------:|

|      8      |      64      |    34.4M   |       3.65       |       17.77       |    x    |

|      16     |      64      |    68.1M   |       3.58       |       17.31       |         |

**Update:** ``widen_factor`` has been disentangled from ``base_width`` because it was confusing. Now widen factor is set to consant 4, and ``base_width`` is the same as in the original paper.

## Trained models and curves

[Link](https://mega.nz/#F!wbJXDS6b!YN3hCDi1tT3SdNFrLPm7mA) to trained models corresponding to the following curves:

**Update:** several commits have been pushed after training the models in Mega, so it is recommended to revert to ``e10c37d8cf7a958048bc0f58cd86c3e8ac4e707d``

![CIFAR-10](https://github.com/prlz77/ResNeXt.pytorch/blob/master/cifar10/cifar-10.jpg)

![CIFAR-100](https://github.com/prlz77/ResNeXt.pytorch/blob/master/cifar100/cifar-100.jpg)

## Other frameworks

* [torch (@facebookresearch)](https://github.com/facebookresearch/ResNeXt). (Original) Cifar and Imagenet

* [caffe (@terrychenism)](https://github.com/terrychenism/ResNeXt). Imagenet

* [MXNet (@dmlc)](https://github.com/dmlc/mxnet/tree/master/example/image-classification#imagenet-1k). Imagenet

## Cite

```

@article{xie2016aggregated,

  title={Aggregated residual transformations for deep neural networks},

  author={Xie, Saining and Girshick, Ross and Doll{\'a}r, Piotr and Tu, Zhuowen and He, Kaiming},

  journal={arXiv preprint arXiv:1611.05431},

  year={2016}

}

```