Question 9
Consider an object detection system evaluated on a dataset consisting of 1000 images. The system makes 1500 predictions across these images, and for each image, there are annotated ground truth bounding boxes.
The system’s precision-recall curve is calculated, and the precision at different recall levels for one of the classes is as follows:
| Recall | Precision |
|---|---|
| 0.1 | 0.90 |
| 0.2 | 0.85 |
| 0.3 | 0.80 |
| 0.4 | 0.75 |
| 0.5 | 0.70 |
| 0.6 | 0.65 |
| 0.7 | 0.60 |
| 0.8 | 0.55 |
| 0.9 | 0.50 |
| 1.0 | 0.45 |
Calculate the Average Precision (AP) for this class using the 11-point interpolation method, which averages the precision values at recall levels {0.0, 0.1, 0.2, ..., 1.0}. The precision at recall 0.0 can be assumed to be 1.0.
Additionally, the system’s AP values for the other two classes are as follows:
- AP for class 2: 0.78
- AP for class 3: 0.72
Based on these AP values, what is the mean Average Precision (mAP) across all three classes?
0.70
0.69
0.73
0.76