Yolov5

What is Yolov5 ?

YOLOv5 is a real-time object detection system that is part of the You Only Look Once (YOLO) series. It is designed to detect objects in images and videos efficiently and accurately. YOLOv5 is known for its simplicity, flexibility, and strong performance, making it a popular choice for developers and researchers.

Features

• High-speed detection: Optimized for real-time object detection in images and video streams.
• Easy integration: Simple API and compatibility with Python and ONNX for seamless integration into applications.
• Pre-trained models: Comes with pre-trained models for common object detection tasks.
• Multi-platform support: Runs on CPUs, GPUs, and mobile devices.
• Customizable: Supports custom datasets and models for specific use cases.
• Open-source: Free and open-source under the Apache 2.0 license.

How to use Yolov5 ?

1. Install the package: Clone the repository and install the required dependencies:

   git clone https://github.com/ultralytics/yolov5.git  
   cd yolov5  
   pip install -r requirements.txt  
   

2. Download a pre-trained model: YOLOv5 provides several pre-trained models that you can download directly from the repository.
3. Run detection: Use the detect.py script to detect objects in images or videos:

   python detect.py --weights yolov5s.pt --source input.jpg  
   

4. Customize: Modify the model or configuration files to suit your specific needs, such as training on a custom dataset.

Frequently Asked Questions

What does YOLOv5 support?
YOLOv5 supports object detection in images and video streams. It works with various input formats, including webcam, video files, and image files.

How do I train YOLOv5 on my own dataset?
To train YOLOv5 on your dataset, you need to prepare your data in the YOLO format (images and labels), modify the configuration files, and run the training script. Detailed instructions are available in the official documentation.

Where are the detection results saved?
By default, detection results are saved in the runs/detect directory. You can change the output path by modifying the --output parameter in the detect script.