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https://github.com/e96031413/AA-YOLO

The proposed Attention ALL-CNN Twin Head YOLO (AA -YOLO) outperforms the original YOLOv4-tiny on the COCO dataset by 3.3% and reduces the model parameters by 7.26%. Source code is at https://github.com/e96031413/AA-YOLO
https://github.com/e96031413/AA-YOLO

aa-yolo darknet pytorch yolo yolov3 yolov4 yolov4-tiny

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The proposed Attention ALL-CNN Twin Head YOLO (AA -YOLO) outperforms the original YOLOv4-tiny on the COCO dataset by 3.3% and reduces the model parameters by 7.26%. Source code is at https://github.com/e96031413/AA-YOLO

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README 

        
# Attention ALL-CNN Twin Head YOLO (AA -YOLO)



Official implementation of **Improving Tiny YOLO with Fewer Model Parameters**, IEEE BigMM 2021



### Abstract

With the rapid development of convolutional neural networks (CNNs), there are a variety of techniques that can improve existing CNN models, including attention mechanisms, activation functions, and data augmentation. However, integrating these techniques can lead to a significant increase in the number of parameters and FLOPs. Here, we integrated Efficient Channel Attention Net(ECA-Net), Mish activation function, All Convolutional Net (ALL-CNN), and a twin detection head architecture into YOLOv4-tiny, yielding an AP 50 of 44.2% on the MS COCO 2017 dataset. The proposed Attention ALL-CNN Twin Head YOLO (A 2 -YOLO) outperforms the original YOLOv4-tiny on the same dataset by 3.3% and reduces the model parameters by 7.26%. Source code is at https://github.com/e96031413/AA-YOLO



### Note

This project is based on [WongKinYiu/PyTorch_YOLOv4 u3_preview branch](https://github.com/WongKinYiu/PyTorch_YOLOv4/tree/u3_preview) with some modification



The AA-YOLO architecture file is at [AA-YOLO-twin-head.cfg](https://github.com/e96031413/PyTorch_YOLOv4-tiny/blob/main/cfg/AA-YOLO-twin-head.cfg)



- You can use this project to train 416x416 YOLOv4-tiny.

[GitHub Issues](https://github.com/WongKinYiu/ScaledYOLOv4/issues/41)



- View our experiment environment infos [here](https://github.com/e96031413/PyTorch_YOLOv4-tiny/blob/main/experiment-info.md)



### Development Log

 Expand Development Log 



### 2021/03/04更新：

使用test.py針對Cross-Stitch架構進行AP測試時，必須到test.py的第43行將model.fuse()功能關閉

```

#model.fuse()

```



### 2021/02/08更新：

新增detect.py計算FPS功能 (detect.py的第8行、138~140行、171行)

```

# line 8

FPS_avg = []



# line 138~140

from statistics import mean 

print('%sDone. (FPS:%.1f)' % (s, 1/(t2 - t1)))

FPS_avg.append(1/(t2-t1))



# line 171

print('Avg FPS: (%.1f)' % (mean(FPS_avg)))

```



解決test.py執行時遇到numpy必須要1.17版本及No module named 'pycocotools'的方法:

```bash

#移除所有numpy

pip uninstall numpy

#安裝1.17版本numpy

pip install numpy==1.17

#安裝pycocotools

pip install pycocotools

#執行test.py

python test.py --data coco2017.data --cfg yolov4-tiny.cfg --weights yolov4-tiny.pt --img 416 --augment

```



### 2021/01/10更新：

新增支援yolov4.conv.137的Pre-trained weight功能(在model.py第456~456行)

```

    elif file == 'yolov4.conv.137':

        cutoff = 137

```



### 2020/12/29更新：

新增YOLOv4-tiny的RouteGroup功能



[Feature-request: YOLOv4-tiny #1350](https://github.com/ultralytics/yolov3/issues/1350#issuecomment-651602149)



新增ReLU6(在utils/layers.py)、ReLU(在utils/layers.py)、DepthWise Convolution(在models.py)、ShuffleNetUnit(在models.py)



**如何透過[u5版本](https://github.com/WongKinYiu/PyTorch_YOLOv4/tree/u5)的yaml檔案進行backbone修改？**



[Yolov4 with Efficientnet b0-b7 Backbone](https://shihyung1221.medium.com/yolov4-with-efficientnet-b0-b7-backbone-529d0ce67cf0)



[Yolov4 with MobileNet V2/V3 Backbone](https://shihyung1221.medium.com/yolov4-with-mobilenet-v2-v3-backbone-c18c0f10bc29)



[Yolov4 with Resnext50/ SE-Resnet50 Backbones](https://shihyung1221.medium.com/yolov4-with-resnext50-se-resnet50-backbones-c324242c48f4)



[Yolov4 with Resnet Backbone](https://shihyung1221.medium.com/yolov4-with-resnet-backbone-eb141b6e79ca)



[Yolov4 with VGG Backbone](https://shihyung1221.medium.com/yolov4-with-vgg-backbone-ae0cedab4f0f)



### 2020/12/28更新：

新增以下四篇Paper的程式碼(在models.py、utils/layers.py)



SE Block paper : [arxiv.org/abs/1709.01507](arxiv.org/abs/1709.01507)



CBAM Block paper : [arxiv.org/abs/1807.06521](arxiv.org/abs/1807.06521)



ECA Block paper : [arxiv.org/abs/1910.03151](arxiv.org/abs/1910.03151)



Funnel Activation for Visual Recognition : [arxiv.org/abs/2007.11824](arxiv.org/abs/2007.11824)



### 2020/12/1更新：

修改了PyTorch_YOLOv4的u3_preview當中，models.py第355行(支援pre-trained weight)、train.py第67行(支援32倍數的解析度)、dataset.py第262及267行(處理dataset相對路徑的問題)

models.py line 355

```python

def load_darknet_weights(self, weights, cutoff=-1):

    # Parses and loads the weights stored in 'weights'

    # Establish cutoffs (load layers between 0 and cutoff. if cutoff = -1 all are loaded)

    file = Path(weights).name

    if file == 'darknet53.conv.74':

        cutoff = 75

    elif file == 'yolov3-tiny.conv.15':

        cutoff = 15

    elif file == 'yolov4-tiny.conv.29':

        cutoff = 29

	.

	.

	.

	.

```

train.py line 67

```python

    gs = 32  # (pixels) grid size # 原本gs = 64改成32

    assert math.fmod(imgsz_min, gs) == 0, '--img-size %g must be a %g-multiple' % (imgsz_min, gs)

```

utils/dataset.py  line 262 and 267

```python

class LoadImagesAndLabels(Dataset):  # for training/testing

    def __init__(self, path, img_size=416, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False,

                 cache_images=False, single_cls=False):

        path = str(Path(path))  # os-agnostic

        parent = str(Path(path).parent) + os.sep                              # add this

        assert os.path.isfile(path), 'File not found %s. See %s' % (path, help_url)

        with open(path, 'r') as f:

            self.img_files = [x.replace('/', os.sep) for x in f.read().splitlines()  # os-agnostic

                              if os.path.splitext(x)[-1].lower() in img_formats]

            self.img_files = [x.replace('./', parent) if x.startswith('./') else x for x in self.img_files]    # add this

```



## Experiment Environment



Our environment setting on [https://www.twcc.ai/](TWCC)

```

NVIDIA-SMI 450.80.02    Driver Version: 450.80.02    CUDA Version: 11.0

python 3.6.9 

PyTorch 1.6.0

Torchvision 0.7.0

tensorflow-20.06-tf2-py3:tag1614508696283

numpy 1.17.0

```



## Important note!!!

2021/03/31 update:



**Be careful to use NAS (Network Attached Storage) to store the coco dataset, it might slow down the tranining speed. Store your dataset locally.**



## Requirements



```

# Install mish-cuda if you want to use fewer GPU memory during training (from 12G -> 8G) 

pip install git+https://github.com/thomasbrandon/mish-cuda/

pip install -r requirements.txt

```



## Download COCO 2017 dataset

```

sh data/get_coco.sh

```



## Training



```

CUDA_VISIBLE_DEVICES=0 python train.py --data coco2017.data --cfg AA-YOLO-twin-head.cfg --weights 'yolov4-tiny.conv.29' --name yolov4-tiny --img 416

```



## Testing



```

CUDA_VISIBLE_DEVICES=0 python test.py --data coco2017.data --cfg AA-YOLO-twin-head.cfg --weights yolov4-tiny.pt --img 416 --augment

```



## Inference

```

CUDA_VISIBLE_DEVICES=0 python detect.py --weights AA-YOLO-twin-head.pt --img 416 --source file.mp4  # video

                                                                                    file.jpg  # image 

```



## Training Result Visualization

```

python -c from utils import utils;utils.plot_results().

```



## Citation

```

@inproceedings{9643269,  

    title={Improving Tiny YOLO with Fewer Model Parameters},

    author={Liu, Yanwei and Ma, Ching-Wen},  

    booktitle={2021 IEEE Seventh International Conference on Multimedia Big Data (BigMM)},   

    pages={61-64},

    year={2021},

    doi={10.1109/BigMM52142.2021.00017}}

```



## Acknowledgements



* [https://github.com/AlexeyAB/darknet](https://github.com/AlexeyAB/darknet)

* [https://github.com/ultralytics/yolov3](https://github.com/ultralytics/yolov3)

* [https://github.com/WongKinYiu/PyTorch_YOLOv4](https://github.com/WongKinYiu/PyTorch_YOLOv4)

* [https://github.com/ultralytics/yolov5](https://github.com/ultralytics/yolov5)

* [HaloTrouvaille/YOLO-Multi-Backbones-Attention](https://github.com/HaloTrouvaille/YOLO-Multi-Backbones-Attention/tree/1f425d379783b6d132b44f14ecfd251d8e2448fa)

* [SpursLipu/YOLOv3v4-ModelCompression-MultidatasetTraining-Multibackbone](https://github.com/SpursLipu/YOLOv3v4-ModelCompression-MultidatasetTraining-Multibackbone)