https://github.com/zlmzju/fusenet
Deep fusion project of deeply-fused nets, and the study on the connection to ensembling
https://github.com/zlmzju/fusenet
cifar dfn ensemble fusion imagenet mxnet resnet
Last synced: 1 day ago
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Deep fusion project of deeply-fused nets, and the study on the connection to ensembling
- Host: GitHub
- URL: https://github.com/zlmzju/fusenet
- Owner: zlmzju
- Created: 2016-11-23T02:58:46.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2018-07-12T01:59:46.000Z (almost 7 years ago)
- Last Synced: 2024-11-08T17:48:25.581Z (5 months ago)
- Topics: cifar, dfn, ensemble, fusion, imagenet, mxnet, resnet
- Language: Jupyter Notebook
- Size: 1.39 MB
- Stars: 228
- Watchers: 22
- Forks: 81
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- Awesome-MXNet - fusenet
README
# Deep Fusion Project
This project contains the models used for the experiments in the paper:
>[Liming Zhao](http://www.zhaoliming.net/),
[Jingdong Wang](http://research.microsoft.com/en-us/um/people/jingdw/),
[Xi Li](http://mypage.zju.edu.cn/xilics),
[Zhuowen Tu](http://pages.ucsd.edu/~ztu/),
and [Wenjun Zeng](https://www.microsoft.com/en-us/research/people/wezeng/).
"On the Connection of Deep Fusion to Ensembling."
arXiv preprint [arXiv:1611.07718](http://arxiv.org/abs/1611.07718) (2016).
Contact: Liming Zhao ([email protected])
## Introduction
In this work, we provide a systematic study to the prevailing ResNet architecture by showing a connection from a general deeply-fused net view to ensembling.
>Our empirical results uncover that the deepest network among the ensemble components does not contribute the most significantly to the overall performance and instead it provides a manner to introduce many layers and thus guarantee the ensemble size.
Guided by the above study and observation, we develop a new deeply-fused network that combines two networks in a merge-and-run fusion manner.
Our approach demonstrates consistent improvements over the ResNet with the comparable setup
on CIFAR-10, CIFAR-100, SVHN, and ImageNet.
## Results
- Test error (%) on CIFAR (flip/translation augmentation) and SVHN (no augmentation):
Method | Depth | #Params | CIFAR-10 | CIFAR-100| SVHN
--------|:-----:|:-------:|:--------:|:--------:|:--------:
DFN-MR1 | 56 | 1.7M | 4.94 | 24.46 | 1.66
DFN-MR2 | 32 | 14.9M | 3.94 | 19.25 | **1.51**
DFN-MR3 | 50 | 24.8M | **3.57** | **19.00**| 1.55
- Training and validation error (%) on ImageNet:
Method | Depth | #Params | Top-1 train | Top-5 train | Top-1 val | Top-5 val |
--------|:-----:|:-------:|:-----------:|:-----------:|:---------:|:---------:|
ResNet | 98 | 45.0M | 15.09 | 3.25 | 23.38 | 6.79 |
DFN-MR | 50 | 46.4M | **14.46** | **3.16** | **23.16** | **6.61** |
## Requirements
- Install [MXNet](http://mxnet.readthedocs.io/en/latest/how_to/build.html) on a machine (Windows, Linux, and Mac OS) with CUDA GPU and optional [cuDNN](https://developer.nvidia.com/cudnn).
- Apply my modified data processing patch on the latest MXNet by merging the pull request:
```shell
git pull origin pull/3936/head master
```
- (Recommended) If you fail to apply the above patch, you can simply use [my MXNet repository](https://github.com/zlmzju/mxnet/tree/fusenet):
```shell
git clone --recursive -b fusenet https://github.com/zlmzju/mxnet.git
```
## How to Train
Step by step tutorial with jupyter notebook is now available, please check the file [tutorial.ipynb](tutorial.ipynb).
### dataset
You can prepare the `*.rec` file by yourself, or simply download the `Cifar` dataset from [data.dmlc.ml](http://data.dmlc.ml/mxnet/data/) or my [google drive](https://drive.google.com/open?id=0By55MQnF3PHCQmRhRTBuWk5DRkk) (recommended), which includes both `Cifar` and `SVHN` datasets.
For ImageNet dataset, follow the [mxnet official document](http://mxnet.incubator.apache.org/tutorials/vision/large_scale_classification.html?highlight=imagenet)
to prepare.
### training
Current code supports training different deeply-fused nets on Cifar-10, Cifar-100, SVHN and ImageNet, such as `plain` network, `resnet`, `cross` (dfn-mr),`half` (dfn-il), `side` (dfn-il without identities), `fuse3` (three fusions), `fuse6` (six fusions), and `ensemble` (with sharing weights, training code will come later). All the networks are contained in the `network` folder.
For example, running the following command can train the `DFN-MR` network (we call it `cross` in the coding stage) on Cifar-10.
```shell
python train_model.py --dataset=cifar10 --network=cross --depth=56 --gpus=0,1 --dataset=
```
To train DFN-MR network on ImageNet, run
```shell
python train_imagenet.py --network=symbol_cross --gpus=0,1,2,3 --data-dir=
```
## Other usages
### visualization
If you want to show the network architecture, run the following command to visualize the network.
```shell
python show_model.py --network=half --depth=26 --widen-factor=1
```
You will obtain a picture [half_d26.png](./visualize/half_d26.png) in the [visualize](./visualize/) folder, and more examples can be found there.
Note that you may need to install [graphviz](http://graphviz.readthedocs.io/en/latest/manual.html#installation) for visualization.
### show results
The training log are saved to `snapshot` folder, and you can use `get_results.py` to obtain the final result of multiple runs in the format of `median (mean +/- std, best)`.
## Citation
Please cite our papers on deep fusion in your publications if it helps your research:
```
@article{WangWZZ16,
author = {Jingdong Wang and
Zhen Wei and
Ting Zhang and
Wenjun Zeng},
title = {Deeply-Fused Nets},
journal = {CoRR},
volume = {abs/1605.07716},
year = {2016},
url = {http://arxiv.org/abs/1605.07716},
}
```
```
@article{ZhaoWLTZ16,
author = {Liming Zhao and
Jingdong Wang and
Xi Li and
Zhuowen Tu and
Wenjun Zeng},
title = {On the Connection of Deep Fusion to Ensembling},
journal = {CoRR},
volume = {abs/1611.07718},
year = {2016},
url = {http://arxiv.org/abs/1611.07718},
}
```