The tutorial presents an end-to-end evaluation workflow for PerceptionBench, a multimodal benchmark measuring fine-grained visual perception across tasks including OCR, counting, localization, contextual reasoning, comparison, depth understanding, and hallucination detection. The process begins with configuring a Colab-compatible environment, installing required libraries, and loading a balanced dataset subset via a multi-stage streaming and download strategy. Images are decoded from base64, placeholders are parsed, and examples are normalized into a consistent record format. The workflow analyzes capability distribution, image requirements, answer types, and source benchmarks. A unified evaluation harness supports a blind-prior baseline, OpenAI-compatible multimodal APIs, and local Hugging Face vision-language models. Judging includes rule-based and optional LLM-assisted methods, with bootstrap confidence intervals calculated. Performance is examined across difficulty slices, and capability profiles are compared with the included leaderboard. Reproducible prediction and reporting artifacts are exported. Configuration parameters include the dataset repository "moonshotai/PerceptionBench", split "train", 12 examples per category, a maximum scan of 1200, seed 0, stream load mode, blind backend, API base "https://api.openai.com/v1", model "gpt-4o-mini", 4 API workers, 512 maximum API tokens, local model "HuggingFaceTB/SmolVLM2-2.2B-Instruct", 128 local maximum new tokens, maximum image side 1024, JPEG quality