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# YOLOv5 Quickstart 🚀

Embark on your journey into the dynamic realm of real-time object detection with Ultralytics 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 using state-of-the-art deep learning methods. Let's ignite the engines and soar into YOLOv5!

## Install

Prepare for launch by cloning the YOLOv5 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. These foundational tools are crucial for running YOLOv5 effectively.

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

## Inference with PyTorch Hub

Experience the simplicity of YOLOv5 PyTorch Hub inference, where models are seamlessly downloaded from the latest YOLOv5 release. This method leverages the power of PyTorch for easy model loading and execution, making it straightforward to get predictions.

    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. Explore these in the Predict mode documentation.

## Inference with detect.py

Harness `detect.py` for versatile inference on various sources. It automatically fetches models from the latest YOLOv5 release and saves results with ease. This script is ideal for command-line usage and integrating YOLOv5 into larger systems, supporting inputs like images, videos, directories, webcams, and even live streams.

    python detect.py --weights yolov5s.pt --source 0                              # webcam
    python detect.py --weights yolov5s.pt --source image.jpg                      # image
    python detect.py --weights yolov5s.pt --source video.mp4                      # video
    python detect.py --weights yolov5s.pt --source screen                         # screenshot
    python detect.py --weights yolov5s.pt --source path/                          # directory
    python detect.py --weights yolov5s.pt --source list.txt                       # list of images
    python detect.py --weights yolov5s.pt --source list.streams                   # list of streams
    python detect.py --weights yolov5s.pt --source 'path/*.jpg'                   # glob pattern
    python detect.py --weights yolov5s.pt --source 'https://youtu.be/LNwODJXcvt4' # YouTube video
    python detect.py --weights yolov5s.pt --source 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream

## Training

Replicate the YOLOv5 COCO dataset benchmarks by following the training instructions below. The necessary models and datasets (like `coco128.yaml` or the full `coco.yaml`) 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 training setups work faster). Maximize performance by using the highest possible `--batch-size` or use `--batch-size -1` for the YOLOv5 AutoBatch feature, which automatically finds the optimal batch size. The following batch sizes are ideal for V100-16GB GPUs. Refer to our configuration guide for details on model configuration files (`*.yaml`).

    # Train YOLOv5n on COCO128 for 3 epochs
    python train.py --data coco128.yaml --epochs 3 --weights yolov5n.pt --batch-size 128

    # Train YOLOv5s on COCO for 300 epochs
    python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5s.yaml --batch-size 64

    # Train YOLOv5m on COCO for 300 epochs
    python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5m.yaml --batch-size 40

    # Train YOLOv5l on COCO for 300 epochs
    python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5l.yaml --batch-size 24

    # Train YOLOv5x on COCO for 300 epochs
    python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5x.yaml --batch-size 16

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 in computer vision. 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. Explore further resources like Ultralytics HUB for dataset management and model training without code, or check out our Solutions page for real-world applications and inspiration. Keep exploring, keep innovating, and enjoy the marvels of YOLOv5. Happy detecting! 🌠🔍

📅 Created 1 year ago ✏️ Updated 5 months ago

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