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https://github.com/AntonMu/TrainYourOwnYOLO

Train a state-of-the-art yolov3 object detector from scratch!
https://github.com/AntonMu/TrainYourOwnYOLO

annotating-images custom-yolo deep-learning deep-learning-tutorial detector google-colab gpu inference keras keras-models object-detection python tensorflow2 tf2 trainyourownyolo transfer-learning wandb weights-and-biases yolo yolov3

Last synced: 17 days ago
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Train a state-of-the-art yolov3 object detector from scratch!

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README 

        
# TrainYourOwnYOLO: Building a Custom Object Detector from Scratch [![License: CC BY 4.0](https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/) [![DOI](https://zenodo.org/badge/197467673.svg)](https://zenodo.org/badge/latestdoi/197467673)



This repo let's you train a custom image detector using the state-of-the-art [YOLOv3](https://pjreddie.com/darknet/yolo/) computer vision algorithm. For a short write up check out this [medium post](https://medium.com/@muehle/how-to-train-your-own-yolov3-detector-from-scratch-224d10e55de2). This repo works with TensorFlow 2.3 and Keras 2.4.



### Before getting started:



- 🍴 **fork** this repo so that you can use it as part of your own project.

- ⭐ **star** this repo to get notifications on future improvements.



### Pipeline Overview



To build and test your YOLO object detection algorithm follow the below steps:



 1. [Image Annotation](/1_Image_Annotation/)

	 - Install Microsoft's Visual Object Tagging Tool (VoTT)

	 - Annotate images

 2. [Training](/2_Training/)

 	- Download pre-trained weights

 	- Train your custom YOLO model on annotated images 

 3. [Inference](/3_Inference/)

 	- Detect objects in new images and videos



## Repo structure

+ [`1_Image_Annotation`](/1_Image_Annotation/): Scripts and instructions on annotating images

+ [`2_Training`](/2_Training/): Scripts and instructions on training your YOLOv3 model

+ [`3_Inference`](/3_Inference/): Scripts and instructions on testing your trained YOLO model on new images and videos

+ [`Data`](/Data/): Input Data, Output Data, Model Weights and Results

+ [`Utils`](/Utils/): Utility scripts used by main scripts



## Getting Started



### Google Colab Tutorial Open In Colab

With Google Colab you can skip most of the set up steps and start training your own model right away. 



### Requisites

The only hard requirement is a running version of python 3.6 or 3.7. To install python 3.7 go to 

- [python.org/downloads](https://www.python.org/downloads/release/python-376/) 



and follow the installation instructions. Note that this repo has only been tested with python 3.6 and python 3.7 thus it is recommened to use either `python3.6` or `python3.7`.



To speed up training, it is recommended to use a **GPU with CUDA** support. For example on [AWS](/2_Training/AWS/) you can use a `p2.xlarge` instance (Tesla K80 GPU with 12GB memory). Inference speed on a typical CPU is approximately ~2 images per second. If you want to use your own machine, follow the instructions at [tensorflow.org/install/gpu](https://www.tensorflow.org/install/gpu) to install CUDA drivers. Make sure to install the [correct version of CUDA and cuDNN](https://www.tensorflow.org/install/source#linux). 



### Installation



#### Setting up Virtual Environment [Linux or Mac]



Clone this repo with:

```bash

git clone https://github.com/AntonMu/TrainYourOwnYOLO

cd TrainYourOwnYOLO/

```

Create Virtual **(Linux/Mac)** Environment:

```bash

python3 -m venv env

source env/bin/activate

```

Make sure that, from now on, you **run all commands from within your virtual environment**.



#### Setting up Virtual Environment [Windows]

Use the [Github Desktop GUI](https://desktop.github.com/) to clone this repo to your local machine. Navigate to the `TrainYourOwnYOLO` project folder and open a power shell window by pressing **Shift + Right Click** and selecting `Open PowerShell window here` in the drop-down menu.



Create Virtual **(Windows)** Environment:



```powershell

py -m venv env

.\env\Scripts\activate

```

![PowerShell](/Utils/Screenshots/PowerShell.png)

Make sure that, from now on, you **run all commands from within your virtual environment**.



#### Install Required Packages [Windows, Mac or Linux]

Install required packages (from within your virtual environment) via:



```bash

pip install -r requirements.txt

```

If this fails, you may have to upgrade your pip version first with `pip install pip --upgrade`.



## Quick Start (Inference only)

To test the cat face detector on test images located in [`TrainYourOwnYOLO/Data/Source_Images/Test_Images`](/Data/Source_Images/Test_Images) run the `Minimal_Example.py` script in the root folder with:



```bash

python Minimal_Example.py

```



The outputs are saved in [`TrainYourOwnYOLO/Data/Source_Images/Test_Image_Detection_Results`](/Data/Source_Images/Test_Image_Detection_Results). This includes:

 - Cat pictures with bounding boxes around faces with confidence scores and

 - [`Detection_Results.csv`](/Data/Source_Images/Test_Image_Detection_Results/Detection_Results.csv) file with file names and locations of bounding boxes.



 If you want to detect cat faces in your own pictures, replace the cat images in [`Data/Source_Images/Test_Images`](/Data/Source_Images/Test_Images) with your own images.



## Full Start (Training and Inference)



To train your own custom YOLO object detector please follow the instructions detailed in the three numbered subfolders of this repo:

- [`1_Image_Annotation`](/1_Image_Annotation/),

- [`2_Training`](/2_Training/) and

- [`3_Inference`](/3_Inference/).

 

**To make everything run smoothly it is highly recommended to keep the original folder structure of this repo!**



Each `*.py` script has various command line options that help tweak performance and change things such as input and output directories. All scripts are initialized with good default values that help accomplish all tasks as long as the original folder structure is preserved. To learn more about available command line options of a python script `` run:



```bash

python  -h

```

### **NEW:** Weights and Biases

TrainYourOwnYOLO supports [Weights & Biases](https://wandb.ai/home/) to track your experiments online. Sign up at [wandb.ai](https://wandb.ai/home) to get an API key and run:

```bash

wandb -login 

```

where `` is your Weights & Biases API key. 



### Multi-Stream-Multi-Model-Multi-GPU

If you want to run multiple streams in parallel, head over to [github.com/bertelschmitt/multistreamYOLO](https://github.com/bertelschmitt/multistreamYOLO). Thanks to @bertelschmitt for putting the work into this.



## License



Unless explicitly stated otherwise at the top of a file, all code is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). This repo makes use of [**ilmonteux/logohunter**](https://github.com/ilmonteux/logohunter) which itself is inspired by [**qqwweee/keras-yolo3**](https://github.com/qqwweee/keras-yolo3).



## Troubleshooting



0. If you encounter any error, please make sure you follow the instructions **exactly** (word by word). Once you are familiar with the code, you're welcome to modify it as needed but in order to minimize error, I encourage you to not deviate from the instructions above. If you would like to file an issue, please use the provided template and make sure to fill out all fields. 



1. If you encounter a `FileNotFoundError`, `Module not found` or similar error, make sure that you did not change the folder structure. Your directory structure **must** look exactly like this: 

    ```text

    TrainYourOwnYOLO

    └─── 1_Image_Annotation

    └─── 2_Training

    └─── 3_Inference

    └─── Data

    └─── Utils

    ```

    If you use a different name such as e.g. `TrainYourOwnYOLO-master` you will have to specify the correct paths as command line arguments in every function call.



    Don't use spaces in file or folder names, i.e. instead of `my folder` use `my_folder`.



2. If you are a Linux user and having trouble installing `*.snap` package files try:

    ```bash

    snap install --dangerous vott-2.1.0-linux.snap

    ```

    See [Snap Tutorial](https://tutorials.ubuntu.com/tutorial/advanced-snap-usage#2) for more information.

3. If you have a newer version of python on your system, make sure that you create your virtual environment with version 3.7. You can use virtualenv for this: 

    ```

    pip install virtualenv

    virtualenv env --python=python3.7

    ```

    Then follow the same steps as above. 

## Need more help? File an Issue!

If you would like to file an issue, please use the provided issue template and make sure to complete all fields. This makes it easier to reproduce the issue for someone trying to help you. 



![Issue](/Utils/Screenshots/Issue.gif)



Issues without a completed issue template will be closed and marked with the label "issue template not completed". 



## Stay Up-to-Date



- ⭐ **star** this repo to get notifications on future improvements and

- 🍴 **fork** this repo if you like to use it as part of your own project.



![CatVideo](/Utils/Screenshots/CatVideo.gif)



## Licensing 

This work is licensed under a [Creative Commons Attribution 4.0 International

License][cc-by]. This means that you are free to:



 * **Share** — copy and redistribute the material in any medium or format

 * **Adapt** — remix, transform, and build upon the material for any purpose, even commercially.



Under the following terms:



 * **Attribution** 

 

 Cite as:

 

  ```text

  @misc{TrainYourOwnYOLO,

    title = {TrainYourOwnYOLO: Building a Custom Object Detector from Scratch},

    author = {Anton Muehlemann},

    year = {2019},

    url = {https://github.com/AntonMu/TrainYourOwnYOLO},

    doi = {10.5281/zenodo.5112375}

  }

  ```

If your work doesn't include a citation list, simply link this [github repo](https://github.com/AntonMu/TrainYourOwnYOLO)!

 

[![CC BY 4.0][cc-by-image]][cc-by]



[cc-by]: http://creativecommons.org/licenses/by/4.0/

[cc-by-image]: https://i.creativecommons.org/l/by/4.0/88x31.png

[cc-by-shield]: https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg