Skip to main content

​
Overview

Arize supports three main computer vision model types, each with specific metrics tailored to their unique characteristics:
  1. Object Detection - Detecting and localizing objects in images
  2. Image Classification - Classifying images into categories
  3. Image Segmentation - Pixel-level classification (Semantic and Instance Segmentation)

​
Object Detection Metrics

Object Detection models in Arize are designed to detect and localize multiple objects within images using bounding boxes.

​
Supported Metrics

Primary Metric
  • Accuracy - Multi-value accuracy metric that compares predicted bounding box labels with actual bounding box labels

​
Data Requirements

Object Detection models require the following data fields: Prediction Data:
  • prediction_object_detection_label - List of predicted object labels
  • prediction_object_detection_score - Confidence scores for each prediction
  • prediction_object_detection_coordinates - Bounding box coordinates
Actual Data:
  • actual_object_detection_label - List of ground truth object labels
  • actual_object_detection_coordinates - Ground truth bounding box coordinates

​
Image Classification Metrics

Image Classification models classify entire images into predefined categories. These models support comprehensive multi-class classification metrics.

​
Supported Metrics

Core Classification Metrics
  • Accuracy - Overall classification accuracy
  • Precision - Per-class and averaged precision metrics
  • Recall - Per-class and averaged recall metrics
  • F1 Score - Harmonic mean of precision and recall
  • Sensitivity - True positive rate
  • Specificity - True negative rate
  • False Positive Rate - Rate of incorrect positive predictions
  • False Negative Rate - Rate of incorrect negative predictions
  • False Negative Density - Density of missed predictions
Multi-Class Specific Metrics
  • Multi-Class Precision - Precision calculated per class (requires positive class specification)
  • Multi-Class Recall - Recall calculated per class (requires positive class specification)
  • Micro-Averaged Precision - Precision averaged across all classes
  • Macro-Averaged Precision - Precision averaged across all classes with equal weight
  • Micro-Averaged Recall - Recall averaged across all classes
  • Macro-Averaged Recall - Recall averaged across all classes with equal weight
Additional Metrics
  • AUC - Area Under the ROC Curve
  • PR-AUC - Area Under the Precision-Recall Curve
  • Log Loss - Cross-entropy loss for probabilistic predictions
  • Calibration - Model calibration quality
  • Cardinality - Number of unique classes

​
Data Requirements

Prediction Data:
  • prediction_labels - Predicted class labels
  • prediction_scores - Confidence scores (optional)
Actual Data:
  • actual_labels - Ground truth class labels

​
Image Segmentation Metrics

Arize supports two types of image segmentation: Semantic Segmentation and Instance Segmentation.

​
Semantic Segmentation

Semantic segmentation assigns a class label to every pixel in an image. Supported Metrics
  • Accuracy - Multi-value accuracy metric comparing predicted vs actual polygon labels
Data Requirements Prediction Data:
  • prediction_semantic_segmentation_polygon_labels - Predicted segmentation labels
  • prediction_semantic_segmentation_polygon_coordinates - Polygon coordinates
Actual Data:
  • actual_semantic_segmentation_polygon_labels - Ground truth segmentation labels
  • actual_semantic_segmentation_polygon_coordinates - Ground truth polygon coordinates

​
Instance Segmentation

Instance segmentation identifies and segments individual object instances, combining object detection with segmentation. Supported Metrics
  • Accuracy - Multi-value accuracy metric comparing predicted vs actual polygon labels
Data Requirements Prediction Data:
  • prediction_instance_segmentation_polygon_labels - Predicted instance labels
  • prediction_instance_segmentation_polygon_coordinates - Polygon coordinates
  • prediction_instance_segmentation_polygon_scores - Confidence scores
  • prediction_instance_segmentation_box_coordinates - Bounding box coordinates
Actual Data:
  • actual_instance_segmentation_polygon_labels - Ground truth instance labels
  • actual_instance_segmentation_polygon_coordinates - Ground truth polygon coordinates
  • actual_instance_segmentation_box_coordinates - Ground truth bounding box coordinates