YOLOv5m Smoke Detection Model
| Property | Value |
|---|---|
| Author | keremberke |
| Framework | PyTorch |
| Accuracy | 99.47% mAP@0.5 |
| Library Version | YOLOv5 7.0.6 |
What is yolov5m-smoke?
YOLOv5m-smoke is a specialized object detection model designed for accurate smoke detection in various environments. Built on the YOLOv5 architecture, it achieves an impressive 99.47% mAP@0.5 on validation datasets, making it highly reliable for smoke detection applications.
Implementation Details
The model is implemented using PyTorch and the YOLOv5 framework version 7.0.6. It features customizable inference parameters including confidence thresholds, IoU thresholds, and maximum detection limits. The model supports both standard inference and test-time augmentation for improved accuracy.
- Configurable confidence threshold (default: 0.25)
- Adjustable IoU threshold (default: 0.45)
- Support for class-agnostic NMS
- Maximum detection limit of 1000 objects per image
Core Capabilities
- Real-time smoke detection in images and video streams
- High precision detection with 99.47% mAP@0.5
- Support for test-time augmentation
- Easy integration with existing PyTorch workflows
- Compatible with custom dataset training
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its exceptionally high accuracy in smoke detection, utilizing the efficient YOLOv5m architecture while maintaining real-time performance capabilities. Its pre-trained weights and easy-to-use interface make it ideal for quick deployment in production environments.
Q: What are the recommended use cases?
The model is particularly suited for fire safety systems, industrial monitoring, surveillance systems, and early warning systems where smoke detection is critical. It can be effectively deployed in both real-time monitoring systems and batch processing applications.
