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https://github.com/ethanhe42/resnet-cifar10-caffe
ResNet-20/32/44/56/110 on CIFAR-10 with Caffe
https://github.com/ethanhe42/resnet-cifar10-caffe
caffe cifar cifar10 resnet vgg16
Last synced: 17 days ago
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ResNet-20/32/44/56/110 on CIFAR-10 with Caffe
- Host: GitHub
- URL: https://github.com/ethanhe42/resnet-cifar10-caffe
- Owner: ethanhe42
- License: mit
- Created: 2016-08-13T16:39:08.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2022-02-28T02:34:52.000Z (over 2 years ago)
- Last Synced: 2024-04-13T06:57:38.982Z (7 months ago)
- Topics: caffe, cifar, cifar10, resnet, vgg16
- Language: Python
- Homepage: https://yihui-he.github.io/blog/resnet-on-cifar-10-with-caffe
- Size: 453 KB
- Stars: 160
- Watchers: 3
- Forks: 125
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
README
# ResNet on CIFAR-10 with Caffe
[GitHub - yihui-he/resnet-cifar10-caffe: ResNet-20/32/44/56/110 on CIFAR-10 with Caffe](https://github.com/yihui-he/resnet-cifar10-caffe)
### Testing
```bash
~/caffe/build/tools/caffe test -gpu 0 -iterations 100 -model resnet-20/trainval.prototxt -weights resnet-20/snapshot/solver_iter_64000.caffemodel
```
| Model | Acc | Claimed Acc |
| --- | --- | --- |
| https://github.com/yihui-he/resnet-cifar10-caffe/releases/download/1.0/resnet20_iter_64000.caffemodel | 91.4% | 0.9125 |
| https://github.com/yihui-he/resnet-cifar10-caffe/releases/download/1.0/resnet32_iter_64000.caffemodel | 92.48% | 0.9248999999999999 |
| ResNet-44 | % | 0.9283 |
| https://github.com/yihui-he/resnet-cifar10-caffe/releases/download/1.0/resnet56_iter_64000.caffemodel | 92.9% | 0.9303 |
| ResNet-110 | % | 0.9339 |
### Citation
If you find the code useful in your research, please consider citing:
```
@InProceedings{He_2017_ICCV,
author = {He, Yihui and Zhang, Xiangyu and Sun, Jian},
title = {Channel Pruning for Accelerating Very Deep Neural Networks},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}
}
```
### Training
```bash
#build caffe
git clone https://github.com/yihui-he/resnet-cifar10-caffe
./download_cifar.sh
./train.sh [GPUs] [NET]
#eg., ./train.sh 0 resnet-20
#find logs at resnet-20/logs
```
### Visualization
specify caffe path in [cfgs.py](cfgs.py) and use plot.py to generate beautful loss plots.
```bash
python plot.py PATH/TO/LOGS
```
Results are consistent with original paper. seems there’s no much difference between resnet-20 and plain-20. However, from the second plot, you can see that plain-110 have difficulty to converge.
### How I generate prototxts:
use [net_generator.py](net_generator.py) to generate `solver.prototxt` and `trainval.prototxt`, you can generate resnet or plain net of depth 20/32/44/56/110, or even deeper if you want. you just need to change `n` according to `depth=6n+2`
### How I generate lmdb data:
```bash
./create_cifar.sh
```
create 4 pixel padded training LMDB and testing LMDB, then create a soft link `ln -s cifar-10-batches-py` in this folder. - get [cifar10 python version](https://www.cs.toronto.edu/~kriz/cifar.html) - use [data_utils.py](data_utils.py) to generate 4 pixel padded training data and testing data. Horizontal flip and random crop are performed on the fly while training.
### Other models in Caffe
[](https://yihui-he.github.io/blog/resnet-on-imagenet-with-caffe)
[](https://yihui-he.github.io/blog/xception-with-caffe)