industrial
IndustryBench-MIPU
IndustryBench-MIPU: Benchmarking Multi-Image Attribute Value Extraction for Industrial Products
Multi-Image Industrial Product Understanding Benchmark — evaluating MLLMs on structured attribute extraction from real-world industrial product images.
Industrial product specifications are scattered across multiple heterogeneous images — specification tables, nameplates, technical drawings. IndustryBench-MIPU tests whether MLLMs can reliably recover them through four… See the full description on the dataset page: https://huggingface.co/datasets/alibaba-multimodal-industrial-ai/IndustryBench-MIPU.IndustrialDetectionStaticCamerasThe IndustrialDetectionStaticCameras dataset has been collected in order to validate the methodology presented in the paper entitled A few-shot learning methodology for improving safety in industrial scenarios through universal self-supervised visual features and dense optical flow. This dataset is divided into five main folders named videoY, where Y=1,2,3,4,5. Each videoY folder contains the following:
The video of the scene in .mp4 format: videoY.mp4
A folder with the images of each frame… See the full description on the dataset page: https://huggingface.co/datasets/jjldo21/IndustrialDetectionStaticCameras.vidore_v3_industrialViDoRe V3 : Industrial reports
This dataset, Industrial reports, is a corpus of technical documents on military aircrafts (fueling, mechanics...), intended for complex-document understanding tasks. It is one of the 10 corpora comprising the ViDoRe v3 Benchmark.
About ViDoRe v3
ViDoRe V3 is our latest benchmark for RAG evaluation on visually-rich documents from real-world applications. It features 10 datasets with, in total, 26,000 pages and 3099 queries, translated into 6… See the full description on the dataset page: https://huggingface.co/datasets/vidore/vidore_v3_industrial.AgiBotWorld-Beta_G1_task_422_Packaging_items_for_industrial_logistics
agibot_task_422
This dataset converts the AgiBot format uniformly into LeRobot V3.0.
Dataset Statistics
robot_name: G1
end_effector: 夹爪
task: 为工业物流包装物品
total_episodes: 2089
total_tasks: 1
size: 150G
Dataset Structure
├── data
│ └── chunk-xxx
│ ├── file-xxx.parquet
├── meta
│ ├── episodes
│ │ └── chunk-xxx
│ │ └── file-xxx.parquet
│ ├── info.json
│ ├── stats.json
│ └── tasks.parquet
└── videos
├──… See the full description on the dataset page: https://huggingface.co/datasets/BAAI-DataCube/AgiBotWorld-Beta_G1_task_422_Packaging_items_for_industrial_logistics.vidore_v3_industrial_mteb_format
Vidore3IndustrialRetrieval
An MTEB dataset
Massive Text Embedding Benchmark
Retrieve associated pages according to questions.
Task category
t2i
Domains
Academic
Reference
https://huggingface.co/blog/QuentinJG/introducing-vidore-v3
Source datasets:
vidore/vidore_v3_industrial
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_task("Vidore3IndustrialRetrieval")… See the full description on the dataset page: https://huggingface.co/datasets/vidore/vidore_v3_industrial_mteb_format.industrial_cart_2