Metrics¶
The following metrics are tracked during model training and validation to help evaluate OCR model performance at both character level and plate level granularity.
Available Metrics¶
During training, you will see the following metrics:
-
plate_acc: Compute the number of license plates that were fully classified. For a single plate, if the ground truth is
ABC123and the prediction is alsoABC123, it would score 1. However, if the prediction wasABD123, it would score 0, as not all characters were correctly classified. -
cat_acc: Calculate the accuracy of individual characters within the license plates that were correctly classified. For example, if the correct label is
ABC123and the prediction isABC133, it would yield a precision of 83.3% (5 out of 6 characters correctly classified), rather than 0% as in plate_acc, because it's not completely classified correctly. -
top_3_k: Calculate how frequently the true character is included in the top-3 predictions (the three predictions with the highest probability).
-
plate_len_acc: Measures how often the predicted length of the license plate matches the ground truth. For example, if the target plate has 6 characters and the prediction also has 6, it scores 1 (regardless of content).
Example Cases¶
| Ground Truth | Prediction | plate_acc | char_acc | plate_len_acc | Notes |
|---|---|---|---|---|---|
ABC123 |
ABC123 |
100% | 100% | 100% | Perfect match |
ABC123 |
ABD123 |
0% | 83.3% | 100% | 5 / 6 chars correct |
XYZ9 |
XYZ9 |
100% | 100% | 100% | Short plate, all correct |
XYZ9 |
XYZ99 |
0% | 75.0% | 0% | Length mismatch + one wrong |
ABC123 |
ABX1Y3 |
0% | 66.7% | 100% | Two chars wrong |