IndustryBench-MIPU: Benchmarking Multi-Image Attribute Value Extraction for Industrial Products
Summary
IndustryBench-MIPU is a large-scale benchmark for multi-image industrial product understanding, evaluating 9 MLLMs and revealing a completeness gap where precision is high but attribute recovery is low.
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Paper page - IndustryBench-MIPU: Benchmarking Multi-Image Attribute Value Extraction for Industrial Products
Source: https://huggingface.co/papers/2606.14383 Hi everyone! We are excited to share our team’s latest work from Alibaba:IndustryBench-MIPU.
While MLLMs are increasingly deployed for general visual tasks, understanding complex industrial products requires assembling dense technical specifications scattered across multiple heterogeneous images, including specification tables, nameplates, and technical drawings. To bridge this gap, we built the first large-scale benchmark for multi-image industrial product understanding.
Key Highlights of our Benchmark:
**Massive Scale:**4,559 products across 27,652 images with 103,703 annotations spanning 18 industrial categories.
**Complex Challenges:**The task requires models to jointly perform text recognition, visual reasoning over technical drawings, domain knowledge interpretation, and cross-image evidence integration.
**Core Findings:**We evaluated 9 MLLMs and uncovered a stark completeness gap. While current models achieve high precision (86-94%), the absolute best model recovers only 49.9% of product-level attributes.
Our evaluation proves that multi-image completeness, rather than single-image accuracy, is the true bottleneck for real-world industrial AI. As we continue to push the boundaries of multimodal and industrial intelligence, we hope this dataset and benchmark serve as a valuable testbed for the community.
We would love to hear your thoughts, feedback, and see how your models perform!
**Paper:**arxiv.org/abs/2606.14383
**Dataset:**huggingface.co/datasets/alibaba-multimodal-industrial-ai/IndustryBench-MIPU
Code:github.com/alibaba-multimodal-industrial-ai/IndustryBench-MIPU
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