Vision-Language Model Evaluation Pipeline

Built a reproducible evaluation pipeline for benchmarking vision-language models across standardized multimodal question-answering datasets.

The system uses PySpark and Parquet to normalize benchmark records, provides model interfaces for BLIP-2, InstructBLIP and LLaVA-style workflows, performs answer-ranking evaluation and produces comparative results and heatmaps.