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https://github.com/cvhub520/yolov5_obb
https://github.com/cvhub520/yolov5_obb
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- Host: GitHub
- URL: https://github.com/cvhub520/yolov5_obb
- Owner: CVHub520
- License: gpl-3.0
- Created: 2023-10-31T06:06:41.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2023-10-31T06:10:04.000Z (about 1 year ago)
- Last Synced: 2024-04-21T11:18:51.994Z (7 months ago)
- Language: Python
- Size: 3.39 MB
- Stars: 33
- Watchers: 3
- Forks: 7
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
## Introduction
YOLOv5-OBB is a variant of YOLOv5 that supports oriented bounding boxes. This model is designed to yield predictions that better fit objects that are positioned at an angle.
[X-AnyLabeling](https://github.com/CVHub520/X-AnyLabeling) is not only an annotation tool, it’s a leap forward into the future of automated data annotation. It’s designed to not only simplify the process of annotation but also to integrate cutting-edge AI models for superior results. With a focus on practical applications, X-AnyLabeling strives to provide an industrial-grade, feature-rich tool that will assist developers in automating annotation and data processing for a wide range of complex tasks.
## Annotation
### Installation
```bash
cd yolov5_obb
git submodule update --init --recursive
cd X-AnyLabeling
pip install -r requirements.txt
# pip install -r requirements-gpu.txt
python anylabeling/app.py
```
### Toturial
- Prepare a predefined category label file (refer to [this](./X-AnyLabeling/assets/classes.txt)).
- Click on the 'Format' option in the top menu bar, select 'DOTA' and import the file prepared in the previous step.
[Option-1]
Basic usage
- Press the shortcut key "O" to create a rotation shape.
- Open edit mode (shortcut: "Ctrl+J") and click to select the rotation box.
- rotate the selected box via shortcut "zxcv", where:
- z: Large counterclockwise rotation
- x: Small counterclockwise rotation
- c: Small clockwise rotation
- v: Large clockwise rotation
[Option-2]
Additionally, you can use the model to batch pre-label the current dataset.
- Press the shorcut key "Ctrl+A" to open the Auto-Labeling mode;
- Choose an appropriate default model or load a custom model.
- Press the shorcut key "Ctrl+M" to run all images once.
For more detail, you can refer to this [document](https://medium.com/@CVHub520/x-anylabeling-pioneering-the-annotation-revolution-eed0ae788f7d).
## Getting start
### Installation
- Requirements
- Python 3.7+
- PyTorch ≥ 1.7
- CUDA 9.0 or higher
- Ubuntu 16.04/18.04
Note:
1. please be aware that if you downloaded the source code from the origin [repo](https://github.com/hukaixuan19970627/yolov5_obb.git), it is advisable to make necessary modifications to the poly_nms_cuda.cu file. Failing to do so will likely result in compilation issues.
2. For Windows user, please refer to this [issue](https://github.com/hukaixuan19970627/yolov5_obb/issues/224) if you have difficulty in generating utils/nms_rotated_ext.cpython-XX-XX-XX-XX.so)**
- Install
a. Create a conda virtual environment and activate it:
```bash
conda create -n yolov5_obb python=3.8 -y
source activate yolov5_obb
```
b. Make sure your CUDA runtime api version ≤ CUDA driver version. (for example 11.3 ≤ 11.4)
```bash
nvcc -V
nvidia-smi
```
c. Install PyTorch and torchvision following the [official instructions](https://pytorch.org/) based on your machine env, and make sure cudatoolkit version same as CUDA runtime api version, e.g.,
```bash
pip install torch==1.12.0+cu116 torchvision==0.13.0+cu116 torchaudio==0.12.0 --extra-index-url https://download.pytorch.org/whl/cu116
nvcc -V
python
>>> import torch
>>> torch.version.cuda
>>> exit()
```
d. Clone the modified version of the follow YOLOv5_OBB repository.
```
git clone https://github.com/CVHub520/yolov5_obb.git
cd yolov5_obb
```
e. Install yolov5-obb.
```python
pip install -r requirements.txt
cd utils/nms_rotated
python setup.py develop # or "pip install -v -e ."
```
- DOTA_devkit [Optional]
If you need to split the high-resolution image and evaluate the oriented bounding boxes (OBB), it is recommended to use the following tool:
```
cd yolov5_obb/DOTA_devkit
sudo apt-get install swig
swig -c++ -python polyiou.i
python setup.py build_ext --inplace
```
### Datasets
Prepare custom dataset files
Note: Ensure that the label format is [polygon classname difficulty], for example, you can set **difficulty=0** unless otherwise specified.
```
x1 y1 x2 y2 x3 y3 x4 y4 classname diffcult
1686.0 1517.0 1695.0 1511.0 1711.0 1535.0 1700.0 1541.0 large-vehicle 1
```
Then, modify the path parameters and run this [script](./divide.py) if there is no need to split the high-resolution images. Otherwise, you can follow the steps below.
```shell
cd yolov5_obb
python DOTA_devkit/ImgSplit_multi_process.py
```
Ensure that your dataset is organized in the directory structure as shown below:
```
.
└── dataset_demo
├── images
│ └── P0032.png
└── labelTxt
└── P0032.txt
```
Finally, you can create a custom data yaml file, e.g., [yolov5obb_demo.yaml](./data/yolov5obb_demo.yaml).
**Note:**
* DOTA is a high resolution image dataset, so it needs to be splited before training/testing to get better performance.
* For single-class problems, it is recommended to add a "None" class, effectively making it a 2-class task, e.g., [DroneVehicle_poly.yaml](./data/DroneVehicle_poly.yaml)
### Train/Val/Detect
> Before formally starting the training task, please follow the following recommendations:
> 1. Ensure that the input resolution is set to a multiple of 32.
> 2. By default, set the batch size to 8. If you increase it to 16 or larger, adjust the scaling factor for the box loss to help the convergence of the `theta`.
- To train on multiple GPUs with Distributed Data Parallel (DDP) mode, please refer to this shell [script](./sh/ddp_train.sh).
- To train the orignal dataset demo without split dataset, please refer to the following command:
```bash
python train.py \
--weights weights/yolov5n.pt \
--data data/task.yaml \
--hyp data/hyps/obb/hyp.finetune_dota.yaml \
--epochs 300 \
--batch-size 1 \
--img 1024 \
--device 0 \
--name /path/to/save_dir
```
- To detect a custom image file/folder/video, please refer to the following command:
```bash
python detect.py \
--weights /path/to/*.pt \
--source /path/to/image \
--img 1024 \
--device 0 \
--conf-thres 0.25 \
--iou-thres 0.2 \
--name /path/to/save_dir
```
> Note:
For more details, please refer to this [document](./docs/GetStart.md).
## Deploy
- Export *.onnx file:
```bash
python export.py \
--weights runs/train/task/weights/best.pt \
--data data/task.yaml \
--imgsz 1024 \
--simplify \
--opset 12 \
--include onnx
```
### Python
- Detect with the exported onnx file using onnxruntime:
```bash
python deploy/onnxruntime/python/main.py \
--model /path/to/*.onnx \
--image /path/to/image
```
### C++
- Enter the directory:
```bash
cd opencv/cpp
.
└── cpp
├── CMakeLists.txt
├── build
├── image
│ ├── demo.jpg
├── main.cpp
├── model
│ └── yolov5m_obb_csl_dotav15.onnx
└── obb
├── include
└── src
```
- Install the [OpenCV](https://github.com/opencv/opencv) and [Eigen3](https://eigen.tuxfamily.org/index.php?title=Main_Page) libraries.
> Note, it is recommended to use OpenCV version 4.6.0 or newer, where v4.7.0 has been successfully tested.
- Place the images and model files in the specified directory.
- Modify the contents of the [CMakeLists.txt](./deploy/opencv/cpp/CMakeLists.txt), [main.cpp](./deploy/opencv/cpp/main.cpp), and [yolo_obb.h](./deploy/opencv/cpp/obb/include/yolo_obb.h) files according to your specific requirements and use case.
- Run the demo:
```bash
mkdir build && cd build
cmake ..
make
```
# Model Zoo
The results on **DOTA_subsize1024_gap200_rate1.0** test-dev set are shown in the table below. (**password: yolo**)
|Model
(download link) |Size
(pixels) | TTA
(multi-scale/
rotate testing) | OBB mAPtest
0.5
DOTAv1.0 | OBB mAPtest
0.5
DOTAv1.5 | OBB mAPtest
0.5
DOTAv2.0 | Speed
CPU b1
(ms)|Speed
2080Ti b1
(ms) |Speed
2080Ti b16
(ms) |params
(M) |FLOPs
@640 (B)
| ---- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
|yolov5m [[baidu](https://pan.baidu.com/s/1UPNaMuQ_gNce9167FZx8-w)/[google](https://drive.google.com/file/d/1NMgxcN98cmBg9_nVK4axxqfiq4pYh-as/view?usp=sharing)] |1024 | × |**77.3** |**73.2** |**58.0** |**328.2** |**16.9** |**11.3** |**21.6** |**50.5**
|yolov5s [[baidu](https://pan.baidu.com/s/1Lqw42xlSZxZn-2gNniBpmw?pwd=yolo)] |1024 | × |**76.8** |- |- |- |**15.6** | - |**7.5** |**17.5**
|yolov5n [[baidu](https://pan.baidu.com/s/1Lqw42xlSZxZn-2gNniBpmw?pwd=yolo)] |1024 | × |**73.3** |- |- |- |**15.2** | - |**2.0** |**5.0**
Table Notes (click to expand)
* All checkpoints are trained to 300 epochs with [COCO pre-trained checkpoints](https://github.com/ultralytics/yolov5/releases/tag/v6.0), default settings and hyperparameters.
* **mAPtest dota** values are for single-model single-scale on [DOTA](https://captain-whu.github.io/DOTA/index.html)(1024,1024,200,1.0) dataset.
Reproduce Example:
```shell
python val.py --data 'data/dotav15_poly.yaml' --img 1024 --conf 0.01 --iou 0.4 --task 'test' --batch 16 --save-json --name 'dotav15_test_split'
python tools/TestJson2VocClassTxt.py --json_path 'runs/val/dotav15_test_split/best_obb_predictions.json' --save_path 'runs/val/dotav15_test_split/obb_predictions_Txt'
python DOTA_devkit/ResultMerge_multi_process.py --scrpath 'runs/val/dotav15_test_split/obb_predictions_Txt' --dstpath 'runs/val/dotav15_test_split/obb_predictions_Txt_Merged'
zip the poly format results files and submit it to https://captain-whu.github.io/DOTA/evaluation.html
```
* **Speed** averaged over DOTAv1.5 val_split_subsize1024_gap200 images using a 2080Ti gpu. NMS + pre-process times is included.
Reproduce by `python val.py --data 'data/dotav15_poly.yaml' --img 1024 --task speed --batch 1`
| Model Name | File Size | Input Size | Configuration File |
| ---------------------------------------------- | --------- | ---------- | -------------------------------------------------------- |
| [yolov5n_obb_drone_vehicle.onnx](https://github.com/CVHub520/X-AnyLabeling/releases/download/v1.0.0/yolov5n_obb_drone_vehicle.onnx) | 8.39MB | 864 | [yolov5n_obb_drone_vehicle.yaml](./data/DroneVehicle_poly.yaml) |
| [yolov5s_obb_csl_dotav10.onnx](https://github.com/CVHub520/X-AnyLabeling/releases/download/v1.0.0/yolov5s_obb_csl_dotav10.onnx) | 29.8MB | 1024 | [dotav1_poly.yaml](./data/dotav1_poly.yaml) |
| [yolov5m_obb_csl_dotav15.onnx](https://github.com/CVHub520/X-AnyLabeling/releases/download/v1.0.0/yolov5m_obb_csl_dotav15.onnx) | 83.6MB | 1024 | [dotav15_poly.yaml](./data/dotav15_poly.yaml) |
| [yolov5m_obb_csl_dotav20.onnx](https://github.com/CVHub520/X-AnyLabeling/releases/download/v1.0.0/yolov5m_obb_csl_dotav20.onnx) | 83.6MB | 1024 | [dotav2_poly.yaml](./data/dotav2_poly.yaml) |
## Acknowledgements
This project relies on the following open-source projects and resources:
* [hukaixuan19970627/yolov5_obb](https://github.com/hukaixuan19970627/yolov5_obb.git)