> ## Documentation Index
> Fetch the complete documentation index at: https://arize-ax.mintlify.site/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Available Metrics

## 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