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https://github.com/yingkunwu/R-YOLOv4
This is a PyTorch-based R-YOLOv4 implementation which combines YOLOv4 model and loss function from R3Det for arbitrary oriented object detection.
https://github.com/yingkunwu/R-YOLOv4
oriented-object-detection pytorch yolov4
Last synced: 12 days ago
JSON representation
This is a PyTorch-based R-YOLOv4 implementation which combines YOLOv4 model and loss function from R3Det for arbitrary oriented object detection.
- Host: GitHub
- URL: https://github.com/yingkunwu/R-YOLOv4
- Owner: yingkunwu
- Created: 2021-03-11T12:05:25.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-01-10T22:01:16.000Z (6 months ago)
- Last Synced: 2024-05-22T07:52:18.250Z (about 1 month ago)
- Topics: oriented-object-detection, pytorch, yolov4
- Language: Python
- Homepage:
- Size: 41.7 MB
- Stars: 113
- Watchers: 3
- Forks: 19
- Open Issues: 3
-
Metadata Files:
- Readme: Readme.md
Lists
- awesome-yolo-object-detection - kunnnnethan/R-YOLOv4 - YOLOv4?style=social"/> : This is a PyTorch-based R-YOLOv4 implementation which combines YOLOv4 model and loss function from R3Det for arbitrary oriented object detection. (Applications)
README
# R-YOLOv4
### Introduction
The objective of this project is to adapt YOLOv4 model to detecting oriented objects. As a result, modifying the original loss function of the model is required. I got a successful result by increasing the number of anchor boxes with different rotating angle and combining smooth-L1-IoU loss function proposed by [R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object](https://arxiv.org/abs/1908.05612) into the original loss for bounding boxes.
### Features
---
#### Loss Function (only for x, y, w, h, theta)
---
#### **\*UPDATE***
The followings are updated to the code to improve the performance of the original setting.
* Circular Smooth Labeling
* KFIoU
* Yolov5
* Yolov7
### Setup
1. Clone repository
```
$ git clone https://github.com/kunnnnethan/R-YOLOv4.git
$ cd R-YOLOv4/
```
2. Create Environment
* Conda
1. Create virual environment
```
$ conda create -n ryolo python=3.8
$ conda activate ryolo
```
2. Install PyTorch and torchvision following the [official instructions](https://pytorch.org), e.g.,
```
If you are using CUDA 11.8 version
$ conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
```
3. Install required libraries
```
$ pip install -r requirements.txt
```
4. Install detectron2 for calculating SkewIoU on GPU following the [official instructions](https://detectron2.readthedocs.io/en/latest/tutorials/install.html), e.g.,
```
python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'
```
* Docker
```
$ docker build -t ryolo docker/
$ sudo docker run -it --rm --gpus all --ipc=host --ulimit memlock=-1 -v ${your_path}:/workspace ryolo
```
6. Download pretrained weights
[weights](https://drive.google.com/drive/folders/1PkVYTTN9YlToM3ZbNsUKbAs6RDwh94am?usp=share_link)
7. Make sure your files arrangment looks like the following
Note that each of your dataset folder in `data` should split into three files, namely `train`, `test`, and `detect`.
```
R-YOLOv4/
├── train.py
├── test.py
├── detect.py
├── xml2txt.py
├── environment.xml
├── requirements.txt
├── model/
├── datasets/
├── lib/
├── outputs/
├── weights/
├── pretrained/ (for training)
└── UCAS-AOD/ (for testing and detection)
└── data/
└── UCAS-AOD/
├── class.names
├── train/
├── ...png
└── ...txt
├── test/
├── ...png
└── ...txt
└── detect/
└── ...png
```
### Train
I have implemented methods to load and train three different datasets. They are UCAS-AOD, DOTA, and custom dataset respectively. You can check out how I loaded those dataset into the model at [/datasets](https://github.com/kunnnnethan/R-YOLOv4/tree/main/datasets). The angle of each bounding box is limited in `(- pi/2, pi/2]`, and the height of each bounding box is always longer than it's width.
Check all the setting in .yaml files that you are going to use in the [/data](https://github.com/kunnnnethan/R-YOLOv4/tree/main/data) folder.
```
$ python train.py --model_name DOTA_yolov7_csl_800 --config data/hyp.yaml --img_size 800 --data data/DOTA.yaml --epochs 100 --mode csl --ver yolov7
```
You can run [display_inputs.py](https://github.com/kunnnnethan/R-YOLOv4/blob/main/display_inputs.py) to visualize whether your data is loaded successfully.
#### UCAS-AOD dataset
Please refer to [this repository](https://github.com/kunnnnethan/UCAS-AOD-benchmark) to rearrange files so that it can be loaded and trained by this model.
#### DOTA dataset
Download the official dataset from [here](https://captain-whu.github.io/DOTA/dataset.html). The original files should be able to be loaded and trained by this model.
#### Train with custom dataset
1. Use [labelImg2](https://github.com/chinakook/labelImg2) to help label your data. labelImg2 is capable of labeling rotated objects.
2. Move your data folder into the `R-YOLOv4/data` folder.
3. Run xml2txt.py
1. generate txt files:
```python xml2txt.py --data_folder your-path --action gen_txt```
2. delete xml files:
```python xml2txt.py --data_folder your-path --action del_xml```
A [trash](https://drive.google.com/uc?export=download&id=1YBDtCoRXEVkPQUUcfoChWKq8WVzm7IF-) custom dataset that I made are provided for your convenience.
### Test
```
$ python test.py --data data/DOTA.yaml --hyp data/hyp.yaml --weight_path weights/DOTA_yolov7_csl_800/best.pth --batch_size 8 --img_size 800 --mode csl --ver yolov7
```
### detect
```
$ python detect.py --data data/UCAS_AOD.yaml --hyp data/hyp.yaml --weight_path weights/DOTA_yolov7_csl_800/best.pth --batch_size 8 --img_size 800 --conf_thres 0.3 --mode csl --ver yolov7
```
#### Tensorboard
If you would like to use tensorboard for tracking traing process.
* Open additional terminal in the same folder where you are running program.
* Run command ```$ tensorboard --logdir=weights --port=6006```
* Go to [http://localhost:6006/]( http://localhost:6006/)
### Results
#### UCAS_AOD
#### DOTA
#### trash (custom dataset)
### References
[WongKinYiu/yolov7](https://github.com/WongKinYiu/yolov7/tree/main)
[ultralytics/yolov5](https://github.com/ultralytics/yolov5/tree/master/utils)
[Tianxiaomo/pytorch-YOLOv4](https://github.com/Tianxiaomo/pytorch-YOLOv4)
[yangxue0827/RotationDetection](https://github.com/yangxue0827/RotationDetection)
[eriklindernoren/PyTorch-YOLOv3](https://github.com/eriklindernoren/PyTorch-YOLOv3)
**YOLOv4: Optimal Speed and Accuracy of Object Detection**
```
@article{yolov4,
title={YOLOv4: Optimal Speed and Accuracy of Object Detection},
author={Alexey Bochkovskiy, Chien-Yao Wang, Hong-Yuan Mark Liao},
journal = {arXiv},
year={2020}
}
```
**R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object**
```
@article{r3det,
title={R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object},
author={Xue Yang, Junchi Yan, Ziming Feng, Tao He},
journal = {arXiv},
year={2019}
}
```