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https://github.com/argusswift/YOLOv4-pytorch

This is a pytorch repository of YOLOv4, attentive YOLOv4 and mobilenet YOLOv4 with PASCAL VOC and COCO
https://github.com/argusswift/YOLOv4-pytorch

attention cbam mobilenetv2 mobilenetv3 object-detection pytorch senet yolov4

Last synced: 12 days ago
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This is a pytorch repository of YOLOv4, attentive YOLOv4 and mobilenet YOLOv4 with PASCAL VOC and COCO

Host: GitHub
URL: https://github.com/argusswift/YOLOv4-pytorch
Owner: argusswift
Created: 2020-07-19T05:36:46.000Z (almost 4 years ago)
Default Branch: master
Last Pushed: 2024-05-03T19:45:19.000Z (about 2 months ago)
Last Synced: 2024-05-21T08:11:45.954Z (about 1 month ago)
Topics: attention, cbam, mobilenetv2, mobilenetv3, object-detection, pytorch, senet, yolov4
Language: Python
Homepage:
Size: 22.1 MB
Stars: 1,683
Watchers: 49
Forks: 333
Open Issues: 129
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml

Lists

awesome-yolo-object-detection - argusswift/YOLOv4-pytorch - pytorch?style=social"/> : This is a pytorch repository of YOLOv4, attentive YOLOv4 and mobilenet YOLOv4 with PASCAL VOC and COCO. (Other Versions of YOLO)

README

        [![996.icu](https://img.shields.io/badge/link-996.icu-red.svg)](https://996.icu)

[![LICENSE](https://img.shields.io/badge/license-Anti%20996-blue.svg)](https://github.com/996icu/996.ICU/blob/master/LICENSE)

# YOLOv4-pytorch (attentive YOLOv4 and Mobilenetv3 YOLOv4)

tips：深度学习指导，目标检测、目标跟踪、语义分割等，小型数据集详询QQ3419923783

* This is a PyTorch re-implementation of YOLOv4 architecture based on the official darknet implementation [AlexeyAB/darknet](https://github.com/AlexeyAB/darknet) with PASCAL VOC, COCO and Customer dataset

## Results(updating)

| name | train Dataset | test Dataset | test size | mAP | inference time(ms) | params(M) |model link |

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

| mobilenetv2-YOLOV4 | VOC trainval(07+12) | VOC test(07) | 416 | 0.851| 11.29 | 46.34 | [args](https://pan.baidu.com/s/10cAzQLHZmPxpHyCsNncV_w) |

## Update!!!

Mobilenetv3-YOLOv4 is arriving!（You only need to change the MODEL_TYPE in config/yolov4_config.py）

## News!!!

This repo add some useful attention methods in backbone.The following pictures illustrate such thing:

* SEnet(CVPR 2017)

![SEnet](https://github.com/argusswift/YOLOv4-pytorch/blob/master/data/SEnet.jpg)

* CBAM(CVPR 2018)

![CBAM](https://github.com/argusswift/YOLOv4-pytorch/blob/master/data/CBAM.png)

## Highlights

### YOLOv4 (attentive YOLOv4 and Mobilenet-YOLOv4) with some useful module

This repo is simple to use,easy to read and uncomplicated to improve compared with others!!!

## Environment

* Nvida GeForce RTX 2080TI

* CUDA10.0

* CUDNN7.0

* windows or linux

* python 3.6

---

## Brief

* [x] CoordAttention([CVPR2021](https://arxiv.org/abs/2103.02907))(torch>=1.4)

* [x] DO-Conv([arxiv2020](https://arxiv.org/abs/2006.12030))(torch>=1.2)

* [x] Attention

* [x] fp_16 training

* [x] Mish

* [x] Custom data

* [x] Data Augment (RandomHorizontalFlip, RandomCrop, RandomAffine, Resize)

* [x] Multi-scale Training (320 to 640)

* [x] focal loss

* [x] CIOU

* [x] Label smooth

* [x] Mixup

* [x] cosine lr

---

## Install dependencies

Run the installation script to install all the dependencies. You need to provide the conda install path (e.g. ~/anaconda3) and the name for the created conda environment (here ```YOLOv4-pytorch```).  

```bash

pip3 install -r requirements.txt --user

```

**Note:** The install script has been tested on an Ubuntu 18.04 and Window 10 system. In case of issues, check the [detailed installation instructions](INSTALL.md). 

## Prepared work

### 1、Git clone YOLOv4 repository

```Bash

git clone github.com/argusswift/YOLOv4-pytorch.git

```

Update the `"PROJECT_PATH"` in the config/yolov4_config.py.

---

### 2、Prepared dataset

### PascalVOC

  ```Shell

  # Download the data.

  cd $HOME/data

  wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar

  wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar

  wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar

  # Extract the data.

  tar -xvf VOCtrainval_11-May-2012.tar

  tar -xvf VOCtrainval_06-Nov-2007.tar

  tar -xvf VOCtest_06-Nov-2007.tar

  ```

* Download links:{[VOC 2012_trainval](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar) 、[VOC 2007_trainval](http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar)、[VOC2007_test](http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar)}、

### MSCOCO 2017

  ```Shell

	#step1: download the following data and annotation

	2017 Train images [118K/18GB]

	2017 Val images [5K/1GB]

	2017 Test images [41K/6GB]

	2017 Train/Val annotations [241MB]

	#step2: arrange the data to the following structure

	COCO

 	---train

	---test

	---val

	---annotations

  ```

* Download links:{[train2017_img](http://images.cocodataset.org/zips/train2017.zip)

、[train2017_ann](http://images.cocodataset.org/annotations/annotations_trainval2017.zip)

、[val2017_img](http://images.cocodataset.org/zips/val2017.zip)

 、[val2017_ann](http://images.cocodataset.org/annotations/stuff_annotations_trainval2017.zip)

、[test2017_img](http://images.cocodataset.org/zips/test2017.zip)

 、[test2017_list](http://images.cocodataset.org/annotations/image_info_test2017.zip)

}

### And then

* Put them in the dir, and update the `"DATA_PATH"` in the config/yolov4_config.py.

* (for COCO) Use coco_to_voc.py to transfer COCO datatype to VOC datatype.

* Convert data format :use utils/voc.py or utils/coco.py convert the pascal voc *.xml format (COCO *.json format)to *.txt format (Image_path   xmin0,ymin0,xmax0,ymax0,class0   xmin1,ymin1,xmax1,ymax1,class1  ...).

### 3、Download weight file

* Darknet pre-trained weight :  [yolov4](https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT)

* Mobilenet pre-trained weight :   [mobilenetv2](https://pan.baidu.com/s/1sjixK2L9L0YgQnvfDuVTJQ)(code:args),[mobilenetv3](https://pan.baidu.com/s/175wKejULuM0ZD05b9iSftg)(code:args)

* Make dir `weight/` in the YOLOv4 and put the weight file in.

* set MODEL_TYPE in config/yolov4_config.py when you run training program.

### 4、Transfer to your own dataset（train your own dataset）

* Put pictures of your dataset into the JPEGImages folder, and Annotations files into the Annotations folder.

* Use the xml_to_txt.py file to write the list of training and test files to ImageSets/Main/*.txt.

* Convert data format :use utils/voc.py or utils/coco.py convert the pascal voc *.xml format (COCO *.json format)to *.txt format (Image_path   xmin0,ymin0,xmax0,ymax0,class0   xmin1,ymin1,xmax1,ymax1,class1  ...).

---

## To train

Run the following command to start training and see the details in the `config/yolov4_config.py` and you should set DATA_TYPE is VOC or COCO when you run training program.

```Bash

CUDA_VISIBLE_DEVICES=0 nohup python -u train.py  --weight_path weight/yolov4.weights --gpu_id 0 > nohup.log 2>&1 &

```

Also * It supports to resume training adding `--resume`, it will load `last.pt` automaticly by using commad

```Bash

CUDA_VISIBLE_DEVICES=0 nohup python -u train.py  --weight_path weight/last.pt --gpu_id 0 > nohup.log 2>&1 &

```

---

## To detect

Modify your detecte img path:DATA_TEST=/path/to/your/test_data # your own images

```Bash

for VOC dataset:

CUDA_VISIBLE_DEVICES=0 python3 eval_voc.py --weight_path weight/best.pt --gpu_id 0 --visiual $DATA_TEST --eval --mode det

for COCO dataset:

CUDA_VISIBLE_DEVICES=0 python3 eval_coco.py --weight_path weight/best.pt --gpu_id 0 --visiual $DATA_TEST --eval --mode det

```

The images can be seen in the `output/`. you could see pictures like follows:

![det-result](https://github.com/argusswift/YOLOv4-pytorch/blob/master/data/det-result.jpg)

---

## To test video

Modify:

* video_path:/path/to/your/video

* weight_path:/path/to/your/weight

* output_dir:/path/to/save/dir

```Bash

CUDA_VISIBLE_DEVICES=0 python3 video_test.py --weight_path best.pt --gpu_id 0 --video_path video.mp4 --output_dir --output_dir

```

---

## To evaluate （PASCAL VOC）

Modify your evaluate dataset path:DATA_PATH=/path/to/your/test_data # your own images

```Bash

for VOC dataset:

CUDA_VISIBLE_DEVICES=0 python3 eval_voc.py --weight_path weight/best.pt --gpu_id 0 --visiual $DATA_TEST --eval --mode val

```

![results](https://github.com/argusswift/YOLOv4-pytorch/blob/master/data/results.jpg)

If you want to see the picture above, you should use follow commands:

```Bash

# To get ground truths of your dataset

python3 utils/get_gt_txt.py

# To plot P-R curve and calculate mean average precision

python3 utils/get_map.py 

```

## To evaluate （COCO）

Modify your evaluate dataset path:DATA_PATH=/path/to/your/test_data # your own images

```bash

CUDA_VISIBLE_DEVICES=0 python3 eval_coco.py --weight_path weight/best.pt --gpu_id 0 --visiual $DATA_TEST --eval --mode val

type=bbox

Running per image evaluation...      DONE (t=0.34s).

Accumulating evaluation results...   DONE (t=0.08s).

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.438

 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.607

 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.469

 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.253

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.486

 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.567

 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.342

 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.571

 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.632

 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.458

 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.691

 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.790

```

---

## To evaluate your model parameters

```Bash

python3 utils/modelsize.py

```

---

## To visualize heatmaps

Set showatt=Ture in val_voc.py and you will see the heatmaps emerged from network' output

```Bash

for VOC dataset:

CUDA_VISIBLE_DEVICES=0 python3 eval_voc.py --weight_path weight/best.pt --gpu_id 0 --visiual $DATA_TEST --eval

for COCO dataset:

CUDA_VISIBLE_DEVICES=0 python3 eval_coco.py --weight_path weight/best.pt --gpu_id 0 --visiual $DATA_TEST --eval

```

The heatmaps can be seen in the `output/` like this:

![heatmaps](https://github.com/argusswift/YOLOv4-pytorch/blob/master/data/heatmap.jpg)

---

 

## Reference

* tensorflow : https://github.com/Stinky-Tofu/Stronger-yolo

* pytorch : https://github.com/ultralytics/yolov3

https://github.com/Peterisfar/YOLOV3

* keras : https://github.com/qqwweee/keras-yolo3