YOLO

Introduction

This tutorial provides a comprehensive guide on how to use the YOLO (You Only Look Once) object detection framework, as demonstrated in Fandy Kurniawan's video. YOLO is a powerful tool for real-time object detection, widely used in computer vision applications. By following this guide, you will learn how to set up YOLO, prepare your data, and run detections effectively.

Step 1: Setting Up Your Environment

Before you can start using YOLO, you need to prepare your development environment.

1. Install Required Libraries

   • Ensure you have Python installed on your system.
   • Install the following libraries:
     • OpenCV
     • NumPy
     • TensorFlow or PyTorch (depending on the YOLO version you are using)
   • You can install them using pip:

     pip install opencv-python numpy tensorflow
     

2. Clone the YOLO Repository

   • Clone the YOLO repository from GitHub. You can use the following command:

     git clone https://github.com/AlexeyAB/darknet.git
     

   • Navigate into the cloned directory:

     cd darknet
     

3. Compile the Darknet Framework

   • Open the Makefile in a text editor and set GPU=1 and CUDNN=1 if you have a compatible GPU.
   • Compile the framework by running:

     make
     

Step 2: Downloading Pre-trained Weights

To get started with YOLO without training your model, download the pre-trained weights.

1. Get the Weights File

   • Download the YOLOv4 weights from the official YOLO website or use the following command:

     wget https://pjreddie.com/media/files/yolov4.weights
     

2. Place the Weights in the Correct Directory

   • Move the downloaded weights file into the darknet folder for easy access.

Step 3: Preparing Your Data

To perform object detection, you need to prepare your data.

1. Create a Configuration File

   • Create a .cfg file for your custom model or use the existing YOLOv4 configuration.

2. Prepare Your Dataset

   • Collect images for testing. Ensure that images are in a supported format (e.g., JPEG, PNG).
   • Create a text file listing the paths of your images.

3. Label Your Data

   • Use a labeling tool (like LabelImg) to annotate your images.
   • Save the labels in YOLO format, which consists of class index and bounding box coordinates.

Step 4: Running Inference

Now you can run YOLO to detect objects in your images.

1. Run the Detection Command

   • Use the following command to run YOLO on your images:

     ./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights data/test.jpg
     

   • Replace data/test.jpg with the path to your image.

2. View Results

   • Check the output image saved in the predictions folder to see the detected objects.

Conclusion

In this tutorial, you learned how to set up the YOLO framework, download necessary weights, prepare your data, and run object detection. YOLO is a powerful tool for real-time applications, and with this guide, you're equipped to start implementing it in your projects. For next steps, consider exploring how to train YOLO on your own dataset or integrate it into an application for practical use cases.