【ML Paper】YOLO: Unified Real-Time Object Detection part10

This time, I'll explain the YOLO image detection model with paper.
This is part 10, and part 11 will be published soon.

Original paper: https://arxiv.org/abs/1506.02640

10. Real-Time Detection In The Wild

YOLO is a fast, accurate object detector, making it ideal for computer vision applications.
After connecting the YOLO to the webcam including the time to fetch images from the camera and display the detections, the resulting system is interactive and engaging.
While YOLO processes images individually, when attached to a webcam it functions like a tracking system, detecting objects as they move around and change in appearance.

11. Conclusion

They introduce YOLO, a unified model for object detection that is simple to construct and can be trained directly on full images. Unlike classifier-based approaches, YOLO is trained on a loss function that directly corresponds to detection performance, with the entire model trained jointly. Fast YOLO stands as the fastest general-purpose object detector in the literature, and YOLO advances the state of the art in real-time object detection. Additionally, YOLO generalizes well to new domains, making it ideal for applications that require fast, robust object detection.