YOLOv5 Quickstart 🚀
Embark on your journey into the dynamic realm of real-time object detection with YOLOv5! This guide is crafted to serve as a comprehensive starting point for AI enthusiasts and professionals aiming to master YOLOv5. From initial setup to advanced training techniques, we've got you covered. By the end of this guide, you'll have the knowledge to implement YOLOv5 into your projects confidently. Let's ignite the engines and soar into YOLOv5!
Prepare for launch by cloning the repository and establishing the environment. This ensures that all the necessary requirements are installed. Check that you have Python>=3.8.0 and PyTorch>=1.8 ready for takeoff.
Inference with PyTorch Hub
import torch # Model loading model = torch.hub.load("ultralytics/yolov5", "yolov5s") # Can be 'yolov5n' - 'yolov5x6', or 'custom' # Inference on images img = "https://ultralytics.com/images/zidane.jpg" # Can be a file, Path, PIL, OpenCV, numpy, or list of images # Run inference results = model(img) # Display results results.print() # Other options: .show(), .save(), .crop(), .pandas(), etc.
Inference with detect.py
python detect.py --weights yolov5s.pt --source 0 # webcam img.jpg # image vid.mp4 # video screen # screenshot path/ # directory list.txt # list of images list.streams # list of streams 'path/*.jpg' # glob 'https://youtu.be/LNwODJXcvt4' # YouTube 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
Replicate the YOLOv5 COCO benchmarks with the instructions below. The necessary models and datasets are pulled directly from the latest YOLOv5 release. Training YOLOv5n/s/m/l/x on a V100 GPU should typically take 1/2/4/6/8 days respectively (note that Multi-GPU setups work faster). Maximize performance by using the highest possible
--batch-size or use
--batch-size -1 for the YOLOv5 AutoBatch feature. The following batch sizes are ideal for V100-16GB GPUs.
To conclude, YOLOv5 is not only a state-of-the-art tool for object detection but also a testament to the power of machine learning in transforming the way we interact with the world through visual understanding. As you progress through this guide and begin applying YOLOv5 to your projects, remember that you are at the forefront of a technological revolution, capable of achieving remarkable feats. Should you need further insights or support from fellow visionaries, you're invited to our GitHub repository home to a thriving community of developers and researchers. Keep exploring, keep innovating, and enjoy the marvels of YOLOv5. Happy detecting! 🌠🔍
Created 2023-11-12, Updated 2023-12-03
Authors: glenn-jocher (2)