# LibtorchDetection

**Repository Path**: XuanMo1234/LibtorchDetection

## Basic Information

- **Project Name**: LibtorchDetection
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-12-15
- **Last Updated**: 2021-12-15

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

<div align="center">

![logo](https://raw.githubusercontent.com/AllentDan/ImageBase/main/detection/libtorch_detection.png)
**C++ Library with Neural Networks for Object  
Detection Based on [LibTorch](https://pytorch.org/).**  
</div>

### [📚 Libtorch Tutorials 📚](https://github.com/AllentDan/LibtorchTutorials)

Visit [Libtorch Tutorials Project](https://github.com/AllentDan/LibtorchTutorials) if you want to know more about Libtorch Detection library.

### 📋 Table of content
 1. [Examples](#examples)
 2. [Train your own data](#trainingOwn)
 3. [Installation](#installation)
 4. [To do list](#todo)
 5. [Thanks](#thanks)
 6. [Citing](#citing)
 7. [License](#license)
 8. [Related repository](#related_repos)

### 💡 Examples <a name="examples"></a>
 1. Download the [VOC](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar) image dataset, and split the dataset into train and val parts as follows:
```
dataset
├── train
│   ├── images
|   |     ├──xxx.jpg
|   |     └......
│   ├── labels
|   |     ├──xxx.xml
|   |     └......
├── val
│   ├── images
|   |     ├──zzz.jpg
|   |     └......
│   ├── labels
|   |     ├──zzz.xml
|   |     └......
```

 2. Download the yolo4_tiny pretrained weight [here](https://github.com/AllentDan/LibtorchDetection/releases/download/0.1/yolo4_tiny.pt). And load it into your cpp project as follows:
```cpp
Detector detector;
detector.Initialize(-1, /*gpu id, -1 for cpu*/
                    416, /*resize width*/
                    416, /*resize height*/
                    "your path to class name.txt");
detector.Train("your path to dataset dir", 
                    ".jpg", /*image type*/
                    30,/*training epochs*/
                    4, /*batch size*/
                    0.001, /*learning rate*/
                    "path to save detector.pt",
                    "path to load pretrained yolo4_tiny.pt");
```

 3. Predicting test. A detector.pt file is provided in the project [here](https://github.com/AllentDan/LibtorchDetection/releases/download/0.1/detector.pt) (trained on VOC for one epoch, just for testing...). Click and download, then you can directly test the detection result through:
```cpp
cv::Mat image = cv::imread("your path to 2007_005331.jpg");
Detector detector;
detector.Initialize(0, 416, 416, "your path to voc_classes.txt");
detector.LoadWeight("detector.pt"/*the saved .pt path*/);
detector.Predict(image, true,/*show result or not*/, 0.1, /*confidence thresh*/, 0.3/*nms thresh*/);
```
the predicted result shows as follow:

![](https://raw.githubusercontent.com/AllentDan/ImageBase/main/detection/2007_005331_pred.jpg)

### 🧑‍🚀 Train your own data <a name="trainingOwn"></a>
- Create your own dataset. Using [labelImg](https://github.com/tzutalin/labelImg) through "pip install" and label your images. Split the output xml files and images into folders just like the example above.

- Training or testing. Just like the example of VOC detection, replace with your own dataset path.


### 🛠 Installation <a name="installation"></a>
**Dependency:**

- [Opencv 3+](https://opencv.org/releases/)
- [Libtorch 1.7+](https://pytorch.org/)

**Windows:**

Configure the environment for libtorch development. [Visual studio](https://allentdan.github.io/2020/03/05/windows-libtorch-configuration/) and [Qt Creator](https://allentdan.github.io/2020/03/05/QT-Creator-Opencv-Libtorch-CUDA-English/) are verified for libtorch1.7+.

**Linux && MacOS:**

Install libtorch and opencv. 

For libtorch, follow the official pytorch c++ tutorials [here](https://pytorch.org/tutorials/advanced/cpp_export.html).

For opencv, follow the official opencv install steps [here](https://github.com/opencv/opencv).

If you have already configured them both, congratulations!!! Download the pretrained weight [here](https://github.com/AllentDan/LibtorchDetection/releases/download/0.1/yolo4_tiny.pt) and a demo .pt file [here](https://github.com/AllentDan/LibtorchDetection/releases/download/0.1/detector.pt) into weights. Change the CMAKE_PREFIX_PATH to your own in CMakeLists.txt. Then just do the following:
```
cd build
cmake ..
make
./LibtorchDetection
```

### ⏳ ToDo <a name="todo"></a>
- [ ] More detection architectures, mainly one-stage algorithms.
- [ ] Data augmentations.
- [ ] Training tricks.

### 🤝 Thanks <a name="thanks"></a>
This project is under developing. By now, these projects helps a lot.
- [official pytorch](https://github.com/pytorch/pytorch)
- [yolo4 tiny](https://github.com/bubbliiiing/yolov4-tiny-pytorch)
- [labelImg](https://github.com/tzutalin/labelImg)
- [tiny xml](https://github.com/leethomason/tinyxml2)

### 📝 Citing
```
@misc{Chunyu:2021,
  Author = {Chunyu Dong},
  Title = {Libtorch Detection},
  Year = {2021},
  Publisher = {GitHub},
  Journal = {GitHub repository},
  Howpublished = {\url{https://github.com/AllentDan/LibtorchDetection}}
}
```

### 🛡️ License <a name="license"></a>
Project is distributed under [MIT License](https://github.com/qubvel/segmentation_models.pytorch/blob/master/LICENSE). 

## Related repository <a name="related_repos"></a>
Based on libtorch, I released following repositories:
- [LibtorchTutorials](https://github.com/AllentDan/LibtorchTutorials)
- [LibtorchSegmentation](https://github.com/AllentDan/LibtorchSegmentation)
- [LibtorchDetection](https://github.com/AllentDan/LibtorchDetection)

Last but not least, **don't forget** your star...

Feel free to commit issues or pull requests, contributors wanted.