BELEBELE
Belebele is a multiple-choice machine reading comprehension (MRC) AI benchmark that is fully parallel across 122 language variants, meaning the same questions, passages, and answer choices are translated into…
AI Benchmarks
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BLINK
BLINK is an AI benchmark that evaluates the core visual perception abilities of multimodal large language models (MLLMs).
CIFAR-10
CIFAR-10 is a labeled dataset of 60,000 small color images sorted into 10 mutually exclusive object categories, with 6,000 images per class, used as a standard benchmark for image classification.
CLIP Score
CLIP Score (also written CLIPScore or CLIP-S) is a reference-free automatic evaluation metric that measures how well a text caption matches an image, computed as the rescaled cosine similarity of the image and…
EgoSchema
EgoSchema is a diagnostic benchmark for evaluating very long-form video language understanding, introduced by Karttikeya Mangalam, Raiymbek Akshulakov, and Jitendra Malik at UC Berkeley.
Fox (benchmark)
Fox is an evaluation suite for fine-grained, multi-page document understanding by large vision-language models.
Frechet Inception Distance
The Frechet Inception Distance (FID) is the standard metric for measuring the quality of images produced by generative models: it computes the Frechet distance between two multivariate Gaussian distributions…
GenAI-Bench
GenAI-Bench is an AI benchmark for evaluating compositional text-to-image and text-to-video generation, introduced in 2024 by researchers from Carnegie Mellon University and Meta AI .
GenEval
GenEval is an object-focused benchmark for evaluating how well text-to-image models follow the content of a prompt.
Image classification
Image classification is the task of assigning a whole image to one category drawn from a fixed set of labels.
MM-Vet
MM-Vet is an AI benchmark for evaluating large multimodal models (LMMs, also called multimodal large language models or MLLMs) on tasks that require combining several core vision-language skills at once.
MMMU-Pro
MMMU-Pro is a rigorous benchmark for evaluating multimodal AI systems on college-level, expert questions that genuinely require seeing an image, built as a harder and more robust version of the original MMMU…
PASCAL VOC
PASCAL VOC (Pattern Analysis, Statistical Modelling and Computational Learning Visual Object Classes) is a long-running benchmark dataset and annual challenge for object recognition, object detection…
T2I-CompBench
T2I-CompBench is an AI benchmark for evaluating compositional text-to-image generation, introduced in the paper "T2I-CompBench: A Comprehensive Benchmark for Open-world Compositional Text-to-image Generation"…
Video-MME
Video-MME (Video Multi-Modal Evaluation) is a benchmark for testing how well multimodal large language models (MLLMs) understand video, built from 900 manually selected videos totaling 254 hours and 2,700…
Visual Question Answering Models
Visual question answering models are AI systems that take an image and a natural language question about that image and return a natural language answer.
Visual question answering
Visual question answering (VQA) is the task of producing a natural-language answer to a natural-language question about an image.
WISE
WISE (World Knowledge-Informed Semantic Evaluation) is an AI benchmark that tests whether a text-to-image model actually possesses and correctly applies real-world knowledge when it draws a scene
ZeroBench
ZeroBench is a visual reasoning benchmark built to be effectively impossible for current frontier large multimodal models, which score 0.0% on its main questions.