AI Engineering/Phase 19/Lesson 63

Multimodal Evaluation

Training is half the loop. The other half is measurement. This lesson builds three evaluation surfaces from primitives: image-caption retrieval reported as R@1, R@5, R@10; visual question answering reported as exact match accuracy; and image captioning reported as BLEU-4. Each metric is a function over the model's outputs and a synthetic eval suite that runs in seconds.

Build 1.5 hr Python

Prerequisites: Phase 19 lessons 58-62 (Track E foundations: encoder, transformer, projection, cross-attention fusion, pretraining)

#Learning Objectives

Compute Recall@K from a similarity matrix between image and caption embeddings.
Compute exact-match VQA accuracy from a model that maps (image, question) pairs to a fixed answer vocabulary.
Compute BLEU-4 from generated and reference token sequences without any external library.
Run all three evals against a synthetic suite built on top of the trained model from lesson 62.

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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.