Fine-Tuning and Evaluating Vision AI Models

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Paid Certificate Available
Duration & workload

11 hours 30 minutes
Sessions

On-Demand
Level

Intermediate
Subtitles

English

Found in

Part of

Eyes on AI - Computer Vision Engineering

Overview

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Building high-performing computer vision systems requires more than training a model—it requires careful evaluation, reliable predictions, and continuous refinement. In this course, you'll learn how to fine-tune and evaluate computer vision models used in real-world AI systems. You'll begin by applying transfer learning techniques to improve model accuracy on domain-specific datasets and analyzing learning-rate schedules to understand training behavior. Next, you'll evaluate the calibration of classification models and apply post-hoc correction methods to improve prediction reliability. The course also explores data preparation and annotation practices for object detection. You'll analyze object-size distributions to configure anchor boxes and evaluate detector performance using standard metrics. Finally, you'll examine image segmentation models. You'll learn how to address class imbalance, analyze segmentation errors, and apply post-processing techniques to improve prediction quality. By the end of the course, you'll be able to evaluate, diagnose, and refine computer vision models across classification, detection, and segmentation tasks.

Syllabus

Optimize AI: Fine-Tune & Maximize Accuracy: Fine-Tuning ViT-B/16 with Transfer Learning for Domain-Specific Datasets

You’ll learn how to adapt a pre-trained ViT-B/16 model to a new domain using transfer learning. You’ll practice freezing and selectively unfreezing layers, explore how the model’s internal representations shift during fine-tuning, and document your choices in an experiment log. By the end, you’ll know how to unfreeze the final four transformer blocks, prepare your dataset effectively, and run a clean, reproducible training workflow that aligns with industry practice.

Optimize AI: Fine-Tune & Maximize Accuracy: Optimizing Training with Cosine and One-Cycle Learning-Rate Schedules

You’ll explore how learning-rate schedules shape the trajectory of model training. You’ll compare cosine decay and the one-cycle policy, analyze their signatures in training curves, and choose the schedule that maximizes validation accuracy while reducing training time. By the end, you’ll be able to interpret LR curves, diagnose plateaus or instability, and make informed decisions about training efficiency.

Calibrate and Serve Confident AI Predictions: Evaluate and Improve Model Calibration

You’ll assess how well a model’s predicted probabilities match real outcomes using ECE and reliability diagrams. By the end, you’ll compute calibration metrics, diagnose over/under-confidence, and apply temperature scaling to improve trust in predictions.

Calibrate and Serve Confident AI Predictions: Build and Deploy a Serverless Batch-Inference Pipeline

You’ll design a serverless batch-inference workflow using AWS S3, Lambda, and DynamoDB. By the end, you will configure an end-to-end pipeline that runs a calibrated model, processes batch files, and stores predictions for analytics.

Annotate and Analyze Objects for Vision: Build a Clean Dataset: Quality-Controlled Bounding-Box Annotation

You will walk through how annotation teams plan tasks, define rules, coach annotators, and measure dataset quality. You will practice reviewing examples, identifying inconsistencies, and applying a structured audit that produces a production-ready bounding-box dataset.

Annotate and Analyze Objects for Vision: Tune Detection Models: Anchor Boxes from Object-Size Clustering

You will examine how bounding-box dimensions reveal object scales in a dataset. You will run clustering to generate three anchor sets and understand how these values shape model training and performance.

Build & Evaluate Real-Time Object Detectors: Understanding Object Detection Metrics and KPIs

You will explore why evaluation metrics matter, what mAP represents, and how metric breakdowns guide improvement decisions. You will connect evaluation to real deployment KPIs, such as accuracy targets and latency constraints.

Build & Evaluate Real-Time Object Detectors: Designing and Integrating a Real-Time Detection Pipeline

You will explore the components of real-time detection, including model selection, preprocessing, inference optimization, tracking, and system-level constraints. You will evaluate trade-offs such as accuracy vs. speed, batch size vs. latency, and resolution vs. FPS.

Balance and Analyze Image Segmentation: Balancing Segmentation Data for Stable Model Training

You will explore why class imbalance disrupts training and practice applying class-balancing strategies, including focal-dice hybrid loss, weighting, and sampling. You will work through a realistic low-foreground medical dataset scenario and monitor recall after 15 epochs.

Balance and Analyze Image Segmentation: Detecting Systematic Errors in Segmentation Masks

You will quantify segmentation errors that arise in real deployments. Using skimage.measure, you will evaluate predicted masks and identify issues such as over-segmentation of elongated objects. You will write error logs that highlight recurring patterns.

Refine Segmentation: Boost Your AI Vision: Measure What Matters: Evaluating Segmentation Quality

You will learn how to evaluate segmentation results using metrics and visualizations. We explore IoU, Dice, class-wise breakdowns, and overlay inspections that reveal where and why your model struggles. You’ll practice generating and interpreting these outputs, just like teams diagnosing performance before deploying a model.

Refine Segmentation: Boost Your AI Vision: Refine and Improve: Building a Post-Processing Pipeline

You will design and test a lightweight refinement pipeline that improves segmentation quality. You will also explore CRFs, boundary smoothing, hole-filling, morphological filters, and noise cleanup. You will build a pipeline and measure before-and-after improvements.

Project: Vision Model Evaluation & Refinement Report

Modern vision systems often combine multiple model components such as classification, object detection, and segmentation. Preparing these systems for production requires more than training individual models. Engineers must evaluate fine-tuning strategies, analyze model confidence behavior, assess detection performance against operational KPIs, and diagnose segmentation errors that may affect reliability. In this project, you will act as a computer vision engineer responsible for evaluating a multi-task vision system before deployment. You will analyze fine-tuning decisions, examine model calibration reliability, interpret detection metrics, diagnose segmentation weaknesses, and assess dataset quality before approving deployment readiness. The project integrates several core evaluation activities used in real-world vision engineering workflows. You will interpret training behavior to assess transfer learning strategies, analyze calibration metrics to improve prediction reliability, evaluate detection performance using task-specific KPIs, and diagnose segmentation errors through metric analysis and qualitative inspection. Rather than optimizing a single component, the project requires you to assess the entire vision pipeline and recommend coordinated improvements across tasks. Your final deliverable will be a Vision Model Evaluation & Refinement Report, a structured technical analysis that identifies weaknesses, prioritizes corrective actions, and justifies engineering decisions across classification, detection, and segmentation modules. This project mirrors real-world responsibilities of computer vision engineers who must evaluate multiple model components simultaneously and communicate a clear production-readiness recommendation to engineering and product stakeholders.