YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset, and represents Ultralytics open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.

Documentation

Medium Article1
Medium Article2
Medium Article3

Quick Start Examples

Install

Clone repo and install requirements.txt in a Python>=3.7.0 environment, including PyTorch>=1.7.

git clone https://github.com/sinankocatrk/Yolov5_RedCircleDetection  # clone
cd Yolov5_RedCircleDetection
pip install -r requirements.txt  # install

Inference

Inference with YOLOv5 and PyTorch Hub . Models download automatically from the latest YOLOv5 release.

import torch

# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # or yolov5m, yolov5l, yolov5x, custom

# Images
img = 'https://ultralytics.com/images/zidane.jpg'  # or file, Path, PIL, OpenCV, numpy, list

# Inference
results = model(img)

# Results
results.print()  # or .show(), .save(), .crop(), .pandas(), etc.

Inference with detect.py

detect.py runs inference on a variety of sources, downloading models automatically from the latest YOLOv5 release and saving results to runs/detect.

python detect.py --weight best.pt --source 0  # webcam
                                          img.jpg  # image
                                          vid.mp4  # video
                                          path/  # directory
                                          path/*.jpg  # glob
                                          'https://youtu.be/Zgi9g1ksQHc'  # YouTube
                                          'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream