---
license: mit
task_categories:
- zero-shot-classification
size_categories:
- n<1K
---

# MMVP-VLM Benchmark Datacard

## Basic Information

**Title:** MMVP-VLM Benchmark

**Description:** The MMVP-VLM (Multimodal Visual Patterns - Visual Language Models) Benchmark is designed to systematically evaluate the performance of recent CLIP-based models in understanding and processing visual patterns. It distills a subset of questions from the original MMVP benchmark into simpler language descriptions, categorizing them into distinct visual patterns. Each visual pattern is represented by 15 text-image pairs. The benchmark assesses whether CLIP models can accurately match these image-text combinations, providing insights into the capabilities and limitations of these models.

## Dataset Details

- **Content Types:** Text-Image Pairs
- **Volume:** Balanced number of questions for each visual pattern, with each pattern represented by 15 pairs. 
- **Source of Data:** Subset from MMVP benchmark, supplemented with additional questions for balance
- **Data Collection Method:** Distillation and categorization of questions from MMVP benchmark into simpler language

## Usage

### Intended Use

- Evaluation of CLIP models' ability to understand and process various visual patterns.