IslamMesabah/myopia-benchmark

MYOPIA: Understanding the Limits of Tree-Based Reasoning in Vision-Language Models MYOPIA is a benchmark for testing whether vision-language models (VLMs) classify images better when they follow a human-designed decision tree, one question at a time, than when they are asked for the label directly (zero-shot). It provides: Four fixed evaluation subsets: GTSRB (901 images, 43 classes), MNIST (1,000, 10), CIFAR-10 (1,000, 10), and single-object MSCOCO (850, 90). One decision… See the full description on the dataset page: https://huggingface.co/datasets/IslamMesabah/myopia-benchmark.

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