FIT3205 Applied computer vision

When you finish this unit, you should be able to:

1. 1

   Design an end-to-end perception pipeline for a specified real-world application by defining task scope (e.g., classification, detection, segmentation), data requirements, evaluation strategy, and deployment constraints;

2. 2

   Select, apply and justify methods and tools for training, validating, optimising and deploying perception models, such as augmentation strategies, calibration techniques, subgroup validation and performance/latency tuning to meet real‑world constraints;

3. 3

   Implement and integrate vision models and preprocessing/postprocessing components into a reproducible pipeline, using appropriate software engineering practices to support experimentation and deployment;

4. 4

   Engineer and execute robust evaluation workflows, including systematic error analysis, calibration checks, and validation across relevant subgroups to characterise performance, failure modes, and uncertainty;

5. 5

   Diagnose and resolve complex performance and reliability issues by reasoning about data shift, spurious correlations, robustness to environmental variation, and trade-offs between accuracy, latency, and compute budgets, proposing justified mitigations such as augmentation or model optimisation/compression;

6. 6

   Assess and address ethical, safety, and societal risks when deploying perception systems, including bias and high-impact failure consequences, and articulate responsible limitations, monitoring needs, and governance controls appropriate to stakeholders and context.