Model Evaluation Guide#

Complete Model Evaluation Workflow with FiftyOne’s Evaluation API and Interactive Analysis

Level: Beginner | Estimated Time: 15-30 minutes | Tags: Model Evaluation, Classification, Detection, Performance Analysis, Metrics

This step-by-step guide will walk you through a complete model evaluation workflow using FiftyOne. You’ll learn how to:

  • Load datasets with predictions and ground truth labels

  • Perform comprehensive model evaluation using FiftyOne’s evaluation API

  • Analyze model performance with interactive visualization tools

  • Identify model weaknesses and failure modes

  • Generate detailed evaluation reports and metrics

Guide Overview#

This guide is broken down into the following sequential steps:

  1. Basic Model Evaluation - Learn how to evaluate model predictions against ground truth using FiftyOne’s evaluation APIs, computing precision, recall, and other metrics

  2. Advanced Evaluation Analysis - Use FiftyOne’s Model Evaluation Panel to visualize model confidence, sort by false positives/negatives, and filter samples by performance

Prerequisites#

Who Is This Guide For

This tutorial is designed for computer vision practitioners and data scientists who want to master model evaluation workflows using FiftyOne. Whether you’re evaluating classification models, detection models, or custom computer vision models, you’ll learn how to leverage FiftyOne’s powerful evaluation capabilities to gain deep insights into model performance.

Packages Used

The notebooks will automatically install the required packages when you run them. The main packages we’ll be using include:

  • fiftyone - Core FiftyOne library for dataset management and evaluation

  • torch & torchvision - PyTorch framework for deep learning operations

  • ultralytics - YOLOv8 implementation for object detection

  • numpy & scikit-learn - Numerical operations and evaluation metrics

  • matplotlib & plotly - Visualization and interactive plotting

Each notebook contains the necessary pip install commands at the beginning, so you can run them independently without any prior setup.

System Requirements

  • Operating System: Linux (Ubuntu 24.04), macOS

  • Python: 3.9, 3.11

  • Memory: 8GB RAM recommended for evaluation operations

  • Storage: 2GB free space for datasets and models

  • Notebook Environment: Jupyter, Google Colab, VS Code notebooks (all validated)

The Quickstart Dataset#

The Quickstart dataset is a curated subset of the MS COCO dataset that comes pre-loaded with ground truth annotations and model predictions. This dataset contains 200 diverse images with object detection annotations, making it perfect for learning evaluation concepts without the overhead of large datasets.

The dataset includes: - Ground truth bounding boxes and labels - Pre-computed model predictions with confidence scores - Multiple object categories for comprehensive evaluation - Real-world scenarios that demonstrate common evaluation challenges

Evaluation Metrics We’ll Explore#

Detection Metrics

  • mAP (mean Average Precision) - The gold standard for object detection evaluation

  • Precision & Recall - Fundamental metrics for understanding model performance

  • IoU (Intersection over Union) - Spatial accuracy measurement

  • Confidence Analysis - Understanding model uncertainty and calibration

Classification Metrics

  • Accuracy, Precision, Recall, F1-Score - Standard classification metrics

  • Confusion Matrix - Detailed breakdown of prediction vs. ground truth

  • ROC Curves & AUC - Performance across different thresholds

  • Per-Class Performance - Category-specific analysis

Advanced Analysis

  • Failure Mode Analysis - Identifying patterns in model mistakes

  • Confidence Calibration - Understanding model uncertainty

  • Edge Case Detection - Finding challenging samples

  • Performance Drift - Monitoring model degradation over time

Model Evaluation Workflow#

This tutorial demonstrates a complete evaluation workflow that combines:

  1. Data Preparation - Loading datasets with predictions and ground truth, ensuring proper format and structure

  2. Basic Evaluation - Computing standard metrics using FiftyOne’s evaluation API, understanding what each metric tells us about model performance

  3. Interactive Analysis - Using FiftyOne’s Model Evaluation Panel to visualize results, filter samples, and gain deeper insights

This integrated approach gives you the tools to not just evaluate models, but to understand their behavior, identify improvement opportunities, and make data-driven decisions about model deployment.

Ready to Begin?#

Click Next to start with the first step: Basic Model Evaluation with FiftyOne.