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open-aff

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code and trained models for "Attentional Feature Fusion"

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- classification.py
- fusion.py
- __init__.py
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cmd_scripts.txt
README.md
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train_imagenet.py

# Installation Guide

1. Get the code

git clone https://github.com/YimianDai/open-aff

Downloads the entire project code from GitHub to your computer.

cd open-aff

Moves into the project folder you just downloaded.

2. Official Install Script

Easy Recommended

Prerequisites

Python 3 Python is required to use pip.

pip install --upgrade mxnet-cu101 gluoncv

Installs the package published on PyPI directly — no need to clone the source.

✅ After installing, open a new terminal and run the program's version command (e.g. --version) to confirm it worked.

Pulled directly from this repo's README.

// repository documentation

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Attentional Feature Fusion
==============

MXNet/Gluon code for "Attentional Feature Fusion" <https://arxiv.org/abs/2009.14082>

What's in this repo so far:

* Code, trained models, and training logs for ImageNet

**PS:**

* If you are the reviewers of our submitted paper, please note that the accuracy of current implementation is a bit higher than the accuracy in the paper because it is a new implementation with a bag of tricks.
* 如果您是我的学位论文评审专家，发现论文与这个 repo 的数字有些出入，那是因为在论文提交后我又将代码重新实现了一遍，添加了 AutoAugment、Label Smooting 这些技巧，所以目前这个 repo 中的分类准确率会比论文中的数字高一些，还请见谅。

**Change Logs:**

* 2020-10-08: Re-implement the image classification code with a bag of tricks
* 2020-09-29: Upload the image classification codes and trained models for the submitted paper

**To Do:**

* Running AFF-ResNeXt-50 and AFF-ResNet-50 on ImageNet
* Update Grad-CAM results on new trained models
* Re-implement the segmentation code
* Convert to PyTorch

**In Progress:**

* Running iAFF-ResNeXt-50 on ImageNet

**Done:**

* Re-implement the image classification code with a bag of tricks
 
## Requirements
 
Install [MXNet](https://mxnet.apache.org/) and [Gluon-CV](https://gluon-cv.mxnet.io/):
  
```
pip install --upgrade mxnet-cu101 gluoncv
```

If you are going to use autoaugment:

```
python3 -m pip install --upgrade "mxnet_cu101<2.0.0"
python3 -m pip install autogluon
```

## Experiments

All trained model params and training logs are in `./params`

The training commands / shell scripts are in `cmd_scripts.txt`

<!-- ### CIFAR-100

| Architecture                                    | Params   | Accuracy    |
| --------                                        | -------  | ----------- |
| Attention-Augmented-Wide-ResNet-28-10 [[3]](#3) | 36.2M    | 81.6     |
| SENet-29 [[4]](#4)                              | 35.0M    | 82.2     |
| SKNet-29  [[7]](#7)                             | 27.7M    | 82.7     |
| PyramidNet-272-alpha-200 [[8]](#8)              | 26.0M    | 83.6     |
| Neural Architecture Transfer (NAT-M4) [[9]](#9) | 9.0M     | 88.3     |
| AutoAugment+PyramidNet+ShakeDrop [[10]](#10)    | 26.0M    | 89.3     |
| **AFF-ResNet-32**                               | **7.9M** | **90.8** |
| **AFF-ResNeXt-38-32x4d (ours)**                 | **7.8M** | **91.2** |
| **AFF-ResNeXt-47-32x4d (ours)**                 | **9.7M** | **91.8** | -->

###  ImageNet

| Architecture                                    | Params    | top-1 err.  |
| --------                                        | -------   | ----------- |
| ResNet-101 [[1]](#1)                            | 42.5M     | 23.2        |
| Efficient-Channel-Attention-Net-101 [[2]](#2)   | 42.5M     | 21.4        |
| Attention-Augmented-ResNet-101 [[3]](#3)        | 45.4M     | 21.3        |
| SENet-101 [[4]](#4)                             | 49.4M     | 20.9        |
| Gather-Excite-$\theta^{+}$-ResNet-101 [[5]](#5) | 58.4M     | 20.7        |
| Local-Importance-Pooling-ResNet-101 [[6]](#6)   | 42.9M     | 20.7        |
| **AFF-ResNet-50 (ours)**                        | **30.3M** | **20.3**    |
| **iAFF-ResNet-50 (ours)**                       | **35.1M** | **20.2**    |
| **iAFF-ResNeXt-50-32x4d (ours)**                | **34.7M** | **19.78**    |

<img src=https://raw.githubusercontent.com/YimianDai/imgbed/master/github/aff/Localization_Reduced.jpg width=100%>
<img src=https://raw.githubusercontent.com/YimianDai/imgbed/master/github/aff/SmallObject_Reduced.jpg width=100%>

## PyTorch Version

@bobo0810 has contributed the PyTorch version. Please check the `aff_pytorch` directory for details.

Many thanks for @bobo0810 for his contribution.

## Citation

Please cite our paper in your publications if our work helps your research. BibTeX reference is as follows.

```
@inproceedings{dai21aff,
  title   =  {Attentional Feature Fusion},
  author  =  {Yimian Dai and Fabian Gieseke and Stefan Oehmcke and Yiquan Wu and Kobus Barnard},
  booktitle =  {{IEEE} Winter Conference on Applications of Computer Vision, {WACV} 2021}
  year    =  {2021}
}
```

## References

<a id="1">[1]</a> 
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun:
Deep Residual Learning for Image Recognition. CVPR 2016: 770-778

<a id="2">[2]</a> 
Qilong Wang, Banggu Wu, Pengfei Zhu, Peihua Li, Wangmeng Zuo, Qinghua Hu:
ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. CVPR 2020: 11531-11539

<a id="3">[3]</a> 
Irwan Bello, Barret Zoph, Quoc Le, Ashish Vaswani, Jonathon Shlens:
Attention Augmented Convolutional Networks. ICCV 2019: 3285-3294

<a id="4">[4]</a> 
Jie Hu, Li Shen, Gang Sun:
Squeeze-and-Excitation Networks. CVPR 2018: 7132-7141

<a id="5">[5]</a> 
Jie Hu, Li Shen, Samuel Albanie, Gang Sun, Andrea Vedaldi:
Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks. NeurIPS 2018: 9423-9433

<a id="6">[6]</a> 
Ziteng Gao, Limin Wang, Gangshan Wu:
LIP: Local Importance-Based Pooling. ICCV 2019: 3354-3363

<a id="7">[7]</a> 
Xiang Li, Wenhai Wang, Xiaolin Hu, Jian Yang:
Selective Kernel Networks. CVPR 2019: 510-519

<a id="8">[8]</a> 
Dongyoon Han, Jiwhan Kim, Junmo Kim:
Deep Pyramidal Residual Networks. CVPR 2017: 6307-6315

<a id="9">[9]</a> 
Zhichao Lu, Gautam Sreekumar, Erik D. Goodman, Wolfgang Banzhaf, Kalyanmoy Deb, Vishnu Naresh Boddeti:
Neural Architecture Transfer. CoRR abs/2005.05859 (2020)

<a id="10">[10]</a> 
Ekin D. Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, Quoc V. Le:
AutoAugment: Learning Augmentation Strategies From Data. CVPR 2019: 113-123