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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.