YOLO architecture concept in Computer Vision - Model Metrics & Evaluation

Metrics & Evaluation - YOLO architecture concept

Which metric matters for YOLO and WHY

YOLO is used for object detection. It finds objects and draws boxes around them. The key metrics are Precision, Recall, and Mean Average Precision (mAP).

Precision tells us how many detected boxes are correct. High precision means few false alarms.

Recall tells us how many real objects were found. High recall means few missed objects.

mAP combines precision and recall over all object classes and detection thresholds. It shows overall detection quality.

We want a balance: find most objects (high recall) and be accurate (high precision).

Confusion matrix for object detection (simplified)

          | Detected Object | No Object Detected
    ------|-----------------|------------------
    Object Present | True Positive (TP) | False Negative (FN)
    No Object     | False Positive (FP) | True Negative (TN)
    

For YOLO, TP means correctly detected objects. FP means boxes where no object exists. FN means missed objects. TN is less used because background is large.

Precision vs Recall tradeoff with examples

If YOLO is set to be very strict, it detects fewer boxes but most are correct. This means high precision but low recall. It misses some objects but has few false alarms.

If YOLO is set to detect many boxes, it finds most objects (high recall) but also many wrong boxes (low precision).

Example: In self-driving cars, missing a pedestrian (low recall) is dangerous, so recall is more important. In a photo app tagging objects, false alarms (low precision) annoy users more.

What good vs bad metric values look like for YOLO

Good: Precision and recall above 0.8, mAP above 0.7 means YOLO detects most objects correctly.

Bad: Precision or recall below 0.5 means many false alarms or many missed objects. mAP below 0.4 shows poor detection.

Very high precision but very low recall means many objects are missed. Very high recall but low precision means many false boxes.

Common pitfalls in YOLO metrics

• Accuracy paradox: High accuracy can be misleading if most images have no objects.
• Data leakage: Testing on images similar to training can inflate metrics.
• Overfitting: Very high training mAP but low test mAP means model memorizes training data.
• Ignoring IoU threshold: Detection counts only if box overlaps enough with true object (IoU). Wrong threshold skews metrics.

Self-check question

Your YOLO model has 98% accuracy but only 12% recall on detecting pedestrians. Is it good for safety?

Answer: No. The model misses 88% of pedestrians (low recall). This is dangerous for safety. High accuracy is misleading because most image parts have no pedestrians.

Key Result

For YOLO, balancing precision and recall with a high mAP is key to good object detection.