Principle:Mlfoundations Open flamingo Benchmark Dataset Loading
Overview
Standardized dataset abstraction pattern that wraps diverse vision-language benchmark formats into a unified PyTorch Dataset interface for consistent evaluation.
Description
Vision-language model evaluation requires loading diverse benchmark datasets (captioning, VQA, classification) with different formats. OpenFlamingo provides four Dataset subclasses that normalize these formats:
- CaptionDataset — COCO, Flickr30K with Karpathy splits
- VQADataset — VQAv2, OK-VQA, VizWiz, TextVQA
- ImageNetDataset — 1000-class classification
- HatefulMemesDataset — binary classification with OCR text
Each returns standardized dictionaries with image, text, and ID fields.
Usage
When evaluating a vision-language model across multiple benchmarks; datasets must be downloaded and organized before loading.
Theoretical Basis
Uniform dataset interfaces enable benchmark-agnostic evaluation code. By normalizing different annotation formats (COCO JSON, VQA JSON, ImageNet folder structure, JSONL) into consistent dictionary outputs, the evaluation functions can operate on any dataset without format-specific logic. The train/test split distinction enables using training examples as few-shot demonstrations.