Benchmark scores in a label export provide valuable insights into the performance of a model or system in a standardized format, allowing for easy comparison and evaluation. These scores typically include various metrics that assess the quality and accuracy of the generated labels or predictions. Here’s an overview of common benchmark scores that may be included in a label export:
- Accuracy Metrics:
- Overall Accuracy: The proportion of correctly predicted labels among all samples.
- Precision: The ratio of correctly predicted positive labels to the total predicted positive labels.
- Recall (Sensitivity): The ratio of correctly predicted positive labels to the total actual positive labels.
- F1 Score: The harmonic mean of precision and recall, providing a balance between the two metrics.
- Class-Specific Metrics:
- Per-Class Accuracy: Accuracy calculated separately for each class or category.
- Precision-Recall Curve: A curve that shows the trade-off between precision and recall for different thresholds.
- Confusion Matrix:
- A matrix that summarizes the counts of true positive, true negative, false positive, and false negative predictions for each class.
- ROC Curve and AUC:
- Receiver Operating Characteristic (ROC) curve plots the true positive rate against the false positive rate for different thresholds.
- Area Under the Curve (AUC) quantifies the performance of the model across all possible classification thresholds.
- Mean Average Precision (mAP):
- The average precision calculated for each class and then averaged across all classes.
- Intersection over Union (IoU) or Jaccard Index:
- Measures the overlap between the predicted and ground truth bounding boxes or segmentation masks.
- Other Custom Metrics:
- Depending on the specific task or application, custom metrics may be included in the benchmark scores, such as Mean Squared Error (MSE) for regression tasks or BLEU score for machine translation.
These benchmark scores provide a comprehensive evaluation of the model’s performance across different aspects, helping users understand its strengths and weaknesses. Including benchmark scores in a label export ensures transparency and reproducibility, enabling users to make informed decisions about model selection, fine-tuning, and deployment.