【ML Paper】YOLO: Unified Real-Time Object Detection part7

This time, I'll explain the YOLO image detection model with paper.
This is part 7, and part 8 will be published soon.

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

7. Experiments

7.1 Experiments overview

The study evaluates YOLO (You Only Look Once) against other real-time object detection systems using the PASCAL VOC 2007 and 2012 datasets.

7.2 Comparison with Other Real-Time Systems

YOLO stands out as the fastest detector on PASCAL VOC 2007, with Fast YOLO achieving 52.7% mAP at 155 FPS. The standard YOLO model improves accuracy to 63.4% mAP while maintaining 45 FPS. In contrast, other systems like DPM and Fast R-CNN either offer lower accuracy or much slower speeds. For example, Fast R-CNN reaches a higher mAP of 70.0% but only runs at 0.5 FPS, which is not suitable for real-time applications.

7.3 Error Analysis and Improvement

YOLO helps reduce background false positives when combined with Fast R-CNN detections. This collaboration enhances overall performance by leveraging YOLO's speed and accuracy to improve Fast R-CNN's results.

7.4 Performance on PASCAL VOC Datasets

YOLO maintains high accuracy and real-time performance on both PASCAL VOC 2007 and 2012 datasets. Fast YOLO achieves 52.7% mAP at 155 FPS, while the standard YOLO reaches 63.4% mAP at 45 FPS, outperforming other state-of-the-art methods in both speed and accuracy.

7.5 Generalization to New Domains

YOLO also performs well on new types of data, such as artwork datasets, demonstrating its ability to generalize better than other detectors.