open sourcevision

YOLOv5

Real-time, high-accuracy object detection with YOLOv5.

Developed by Ultralytics

Official Site
1.9M - 86.7MParams
YesAPI Available
stableStability
1.0Version
GPL-3.0 LicenseLicense
PyTorchFramework
NoRuns Locally
Real-World Applications
  • Surveillance systemsOptimized Capability
  • Autonomous vehiclesOptimized Capability
  • RoboticsOptimized Capability
  • Industrial automationOptimized Capability
Implementation Example
Example Prompt
Use YOLOv5 to detect objects in an image.
Model Output
"Detected objects: ['person', 'bicycle', 'car'] with confidence scores."
Advantages
  • ✓ Exceptional speed, achieving real-time detection without compromising accuracy.
  • ✓ Easy integration with PyTorch, allowing flexibility for customization and fine-tuning.
  • ✓ Comprehensive documentation and active community support ensure ease of use.
Limitations
  • ✗ Performance can vary based on hardware specifications and model configuration.
  • ✗ Requires significant computational resources for optimal performance.
  • ✗ Limited out-of-the-box performance on highly complex datasets without fine-tuning.
Model Intelligence & Architecture

Technical Documentation

YOLOv5 is a high-performance, open-source object detection model developed by Ultralytics. Built on the PyTorch framework, it provides real-time image detection with exceptional accuracy and speed, making it suitable for various vision-based applications.

Technical Overview

YOLOv5 is an evolution in the YOLO (You Only Look Once) family of object detectors that prioritize fast processing and robust detection. Its architecture balances detection precision and inference speed, enabling real-time detection even on edge devices. YOLOv5 supports multi-scale predictions and anchor-based bounding box regression, optimizing for diverse object sizes and shapes.

Framework & Architecture

  • Framework: PyTorch
  • Architecture: Custom CNN backbone with CSPNet and PANet for effective feature extraction and aggregation
  • Parameters: Highly optimized convolutional layers and modules tailored for object detection tasks
  • Latest Version: 1.0

The model leverages the capabilities of PyTorch for seamless model training, inference, and deployment. Its modular architecture allows developers to customize layers or tune parameters for their specific use cases.

Key Features / Capabilities

  • Real-time object detection with high accuracy and speed
  • Open-source and actively maintained by Ultralytics
  • Supports detection of multiple object classes simultaneously
  • Lightweight and efficient architecture suitable for deployment on edge devices
  • Extensive documentation and community support
  • Pretrained weights available for quick starts

Use Cases

  • Surveillance Systems: Automated detection for security monitoring
  • Autonomous Vehicles: Real-time detection of pedestrians and obstacles
  • Robotics: Enabling robots to perceive and interact with their environment
  • Industrial Automation: Quality control and object sorting via vision systems

Access & Licensing

YOLOv5 is released under the GPL-3.0 License and is fully open-source, providing free access for commercial and non-commercial use. Developers can access the source code and pretrained models on GitHub. Detailed official documentation is available at Ultralytics YOLOv5 Docs for easy integration and deployment.

Technical Specification Sheet

FAQs

Technical Details
Architecture
CSPNet with Focus, CSPDarknet, Leaky ReLU activation
Stability
stable
Framework
PyTorch
Signup Required
No
API Available
Yes
Runs Locally
No
Release Date
2020-06-09

Best For

Real-time applications requiring high-speed object detection.

Alternatives

Faster R-CNN, SSD, EfficientDet

Pricing Summary

Free to use under open-source license.

Compare With

YOLOv5 vs Faster R-CNNYOLOv5 vs SSDYOLOv5 vs EfficientDetYOLOv5 vs TensorFlow Object Detection API

Explore Tags

#object detection AI#real-time detection

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