Detect objects in images and videos using YOLOv5
Identify objects in images
Detect objects in images using YOLOv5
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YOLOv5 is a state-of-the-art real-time object detection system that detects objects in images and videos. It is the fifth iteration of the popular YOLO (You Only Look Once) series, known for its speed, accuracy, and simplicity. Built on PyTorch, YOLOv5 is widely used for tasks like face detection, pedestrian detection, and surveillance. Its open-source nature makes it highly customizable for specific use cases.
• Multiple Model Sizes: YOLOv5 offers models of different sizes (s, m, l, x) to balance between speed and accuracy.
• Real-Time Detection: Designed for fast inference, enabling real-time object detection in videos and webcam feeds.
• Multi-Platform Support: Runs on CPUs, GPUs, and mobile devices.
• Customizable: Users can train YOLOv5 on their own datasets for tailored object detection.
• Support for Various Formats: Works with images, videos, and streaming data.
git clone https://github.com/ultralytics/yolov5.git
pip install -r yolov5/requirements.txt
python detect.py --source input.jpg
What makes YOLOv5 faster than other object detection models?
YOLOv5 uses a simplified architecture and efficient backbone networks to achieve fast inference speeds.
How do I train YOLOv5 on my own dataset?
data.yaml file with your dataset path.python train.py.Can YOLOv5 run on mobile devices?
Yes, YOLOv5 supports mobile deployment through frameworks like Core ML and TensorFlow Lite.