datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
FUSU-Fine_grained_Urban_Semantic_Understanding
About:
FUSU dataset covers 5 whole urban areas, 847 km^2 located in the north and south of China, with 17 land use and land cover (LULC) classes and over 170K images and 30 billion pixels of annotations, supporting segmentation, change detection and domain adaptation tasks. This data comprises 2 parts:
Bi-temporal high-resolution satellite RGB images with fine-grained annotations.
Monthly revisited Sentinel-2 and Sentinel-1 images.
Details:
1.… See the full description on the dataset page: https://huggingface.co/datasets/sp-juni/FUSU-Fine_grained_Urban_Semantic_Understanding.kitchen-workspace-understanding-safe-manipulation
Kitchen Workspace Understanding & Safe Manipulation
Generated by datapack-import.ts
This dataset mirrors public data-pack render outputs from Physicl.
Each row represents one render view. The image column contains a stable URL to the primary render image uploaded under /data; image_path stores the relative repository path and data_commit_sha pins the Hugging Face dataset commit used by those URLs. Files are uploaded as downloaded unless optional PNG recompression is enabled by… See the full description on the dataset page: https://huggingface.co/datasets/physicl/kitchen-workspace-understanding-safe-manipulation.IMAGE_UNDERSTANDINGA key question for understanding multimodal performance is analyzing the ability for a model to have basic
vs. detailed understanding of images. These capabilities are needed for models to be used in
real-world tasks, such as an assistant in the physical world. While there are many dataset for object detection
and recognition, there are few that test spatial reasoning and other more targeted task such as visual prompting.
The datasets that do exist are static and publicly available, thus… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/IMAGE_UNDERSTANDING.form_understanding_in_noisy_scanned_documents_plus
Dataset Card for Form Understanding in Noisy Scanned Documents Plus
This is a FiftyOne dataset with 1026 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("Voxel51/form_understanding_in_noisy_scanned_documents_plus")
# Launch the App… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/form_understanding_in_noisy_scanned_documents_plus.multi-view-bathroom-scene-understanding-camera-relocalization
Multi-View Bathroom Scene Understanding & Camera Relocalization
Generated by datapack-import.ts
This dataset mirrors public data-pack render outputs from Physicl.
Each row represents one render view. The image column contains a stable URL to the primary render image uploaded under /data; image_path stores the relative repository path and data_commit_sha pins the Hugging Face dataset commit used by those URLs. Files are uploaded as downloaded unless optional PNG recompression is… See the full description on the dataset page: https://huggingface.co/datasets/physicl/multi-view-bathroom-scene-understanding-camera-relocalization.physical-ai-bench-understanding
Physical AI Bench - Understanding
PAI-Bench (Physical AI Bench) is a comprehensive benchmark designed to evaluate physical AI generation and understanding capabilities across various real-world scenarios. This particular dataset, PAI-Bench-U, focuses specifically on Video Understanding tasks, comprising 2,808 real-world cases with task-aligned metrics.
Paper: PAI-Bench: A Comprehensive Benchmark For Physical AI
Code: GitHub Repository
Citation
If you use Physical AI… See the full description on the dataset page: https://huggingface.co/datasets/shi-labs/physical-ai-bench-understanding.synthetic-code-understanding
SYNTHETIC-1
This is a subset of the task data used to construct SYNTHETIC-1. You can find the full collection here
Agentic-Long-Context-Understanding-QA 📖 Agentic Long Context Understanding 📖
Self-Taught Agentic Long Context Understanding (Arxiv).
AgenticLU refines complex, long-context queries through self-clarifications and contextual grounding, enabling robust long-document understanding in a single pass.
Installation Requirements
This codebase is largely based on OpenRLHF and Helmet, kudos to them.
The requirements are the same
pip install openrlhf
pip install -r ./HELMET/requirements.txt… See the full description on the dataset page: https://huggingface.co/datasets/yzhuang/Agentic-Long-Context-Understanding-QA.Thai-understanding
Thai-Understanding: Thai-SUP & XLSR-Thai
Overview
Thai-Understanding is an open-source repository that provides a solution for speech understanding in the Thai language. This repository includes:
Thai-SUP: The first open-source Thai speech understanding dataset, which includes over 1,000 hours of data across three tasks: Intent Classification (IC), Named Entity Recognition (NER), and Speech Rephrasing (SR).
XLSR-Thai: The first large-scale self-supervised learning (SSL)… See the full description on the dataset page: https://huggingface.co/datasets/mcshao/Thai-understanding.data-for-my-binary-understanding-loraanime-understanding-dataset
Anime Understanding Benchmark (WIP)
Evaluate anime knowledge found in existing LLMs. We hope to provide an easy to run evaluation on knowledge understanding in anime/manga. Better understanding in anime/manga knowledge should resulted in task such as waifu role play.
Any suggestion is open in discussion tab.
Currently in the works
[] Eval on popular models such as gpt, hermes, dolphin, llama base model
[] Add more metadata regarding of anime/manga year span
[] Suggestions… See the full description on the dataset page: https://huggingface.co/datasets/theblackcat102/anime-understanding-dataset.CUAD_v1_Contract_Understanding_clause_classification
Dataset Card for Contract Understanding Atticus Dataset (CUAD) Clause Classification
This dataset contains 13,155 labeled clauses extracted from 509 commercial legal contracts from the original CUAD dataset. One of the original 510 contracts was removed due to being a scanned copy.
The text was cleaned using clean-text.
You can easily and quickly load it:
dataset = load_dataset("dvgodoy/CUAD_v1_Contract_Understanding_clause_classification")
Dataset({
features: ['file_name'… See the full description on the dataset page: https://huggingface.co/datasets/dvgodoy/CUAD_v1_Contract_Understanding_clause_classification.indoor-scene-state-environmental-context-understanding-next-pack-0c894b1f-effc97c5
Indoor Robot Navigation and Toy Grasping
Training dataset for a mobile robot in home environments (kids rooms and playrooms). Renders show furniture to navigate between and toys to grasp, with metric depth and world-space normals for contact geometry, plus per-frame annotations, at 1024x1024 with the environments' authored lighting.
This dataset mirrors public data-pack render outputs from Physicl.
Each row represents one render view. The image column contains a stable URL to… See the full description on the dataset page: https://huggingface.co/datasets/physicl-community/indoor-scene-state-environmental-context-understanding-next-pack-0c894b1f-effc97c5.CUAD_v1_Contract_Understanding_PDF
Dataset Card for Contract Understanding Atticus Dataset (CUAD) PDF
This dataset contains the PDFs and the full text of 509 commercial legal contracts from the original CUAD dataset. One of the original 510 contracts was removed due to being a scanned copy.
The extracted text was cleaned using clean-text.
The PDFs were encoded in base64 and added as the pdf_bytes_base64 feature.
You can easily and quickly load it:
dataset = load_dataset("dvgodoy/CUAD_v1_Contract_Understanding_PDF")… See the full description on the dataset page: https://huggingface.co/datasets/dvgodoy/CUAD_v1_Contract_Understanding_PDF.agenticvbench-understanding-materialssentiment-understanding-corpusSentiment Corpus
Distilling Fine-grained Sentiment Understanding from Large Language Models
Fine-grained sentiment analysis (FSA) aims to extract and summarize user opinions from vast opinionated text. Recent studies demonstrate that large language models (LLMs) possess exceptional sentiment understanding capabilities. However, directly deploying LLMs for FSA applications incurs high inference costs. Therefore, this paper investigates the distillation of fine-grained sentiment… See the full description on the dataset page: https://huggingface.co/datasets/Gporrt/sentiment-understanding-corpus.sroie_document_understanding
Dataset Card for "sroie_document_understanding"
Dataset Description
This dataset is an enriched version of SROIE 2019 dataset with additional labels for line descriptions and line totals for OCR and layout understanding.
Dataset Structure
DatasetDict({
train: Dataset({
features: ['image', 'ocr'],
num_rows: 652
})
})
Data Fields
{
'image': PIL Image object,
'ocr': [
# text box 1
{
'box':… See the full description on the dataset page: https://huggingface.co/datasets/arvindrajan92/sroie_document_understanding.ScaleCUA-Data-UnderstandingSafe_Unsafe_Test-Understanding-text-qwen-embs
Dataset Card for pjramg/Safe_Unsafe_Test
This is a FiftyOne dataset with 40 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("pjramg/Safe_Unsafe_Test-Understanding-text-qwen-embs")
# Launch the App
session = fo.launch_app(dataset)… See the full description on the dataset page: https://huggingface.co/datasets/pjramg/Safe_Unsafe_Test-Understanding-text-qwen-embs.physical-ai-bench-understanding-evalICDAR_2025_Handwritten_Notes_Understanding_Challengefinnlp2026-subtask3-polyfiqa
FinNLP 2026 Subtask 3 — PolyFiQA
Files:
train.parquet: 76 public labeled PolyFiQA examples supplied by the organizers.
test.parquet: 76 hidden-answer participant examples with stable row IDs.
SOURCE_DATASET_CARD.md: source PolyFiQA dataset documentation and citation.
task_id identifies a source financial-report document and repeats four times. The added id column is the unique submission key. Test answers remain in the private competition repository.
VLM-Video-Understanding
VLM-Video-Understanding
A minimalistic demo for image inference and video understanding using OpenCV, built on top of several popular open-source Vision-Language Models (VLMs). This repository provides Colab notebooks demonstrating how to apply these VLMs to video and image tasks using Python and Gradio.
Overview
This project showcases lightweight inference pipelines for the following:
Video frame extraction and preprocessing
Image-level inference with VLMs
Real-time… See the full description on the dataset page: https://huggingface.co/datasets/prithivMLmods/VLM-Video-Understanding.Spatial_Understanding
Purpose
Spatial intelligence is a fundamental component of both Artificial General Intelligence (AGI) and Embodied AI, encompassing multiple cognitive levels — Perception, Understanding, and Extrapolation (referring to the work).
We construct a composite benchmark derived from several prior works and this testbed is designed to measure the Understanding level of spatial intelligence of AI models within the given visual cues.
Overview
The benchmark integrates three… See the full description on the dataset page: https://huggingface.co/datasets/LLDDSS/Spatial_Understanding.understanding_fablesThis task aims to measure the ability of computational models to understand short narratives, by identifying the most
appropriate moral for a given fable from a set of five alternatives.mckinsey_state_of_ai_doc_understanding
Mckinsey State Of Ai Doc Understanding
This dataset was generated using YourBench (v0.3.1), an open-source framework for generating domain-specific benchmarks from document collections.
Pipeline Steps
ingestion: Read raw source documents, convert them to normalized markdown and save for downstream steps
summarization: Perform hierarchical summarization: chunk-level LLM summaries followed by combine-stage reduction
chunking: Split texts into token-based single-hop and… See the full description on the dataset page: https://huggingface.co/datasets/yourbench/mckinsey_state_of_ai_doc_understanding.KnowCoder-Schema-Understanding-Data
KnowCoder: Coding Structured Knowledge into LLMs for Universal
Information Extraction
📃 Paper
|
🤗 Resource (Schema • Data • Model)
|
🚀 Try KnowCoder (coming soon)!
Schema Understanding Data
The schema understanding data includes schema definition codes and schema instance codes.
Schema Definition Codes
The schema definition codes are built based on a schema library, with statistical results as follows.
Due to data protection concerns, here… See the full description on the dataset page: https://huggingface.co/datasets/golaxy/KnowCoder-Schema-Understanding-Data.Intelligent-Content-Understanding
Intelligent Content Understanding
Empowering Advanced Thinking, Deep Understanding, Diverse Perspectives, and Creative Solutions Across Disciplines
By fostering a richly interconnected knowledge ecosystem, ICU (Intelligent Content Understanding) aims to elevate language models to unparalleled heights of understanding, reasoning, and innovation.
This ambitious project lays the groundwork for developing an 'internal knowledge map' within language models, enabling… See the full description on the dataset page: https://huggingface.co/datasets/WeMake/Intelligent-Content-Understanding.Audio-Understanding-Bitrate-Eval-0426
Audio Understanding — MP3 Bitrate Evaluation (April 2026)
Empirical eval measuring how MP3 compression bitrate affects transcription accuracy across every audio-input LLM available on OpenRouter.
📝 Blog post: MP3 Bitrate Sensitivity in Audio-Multimodal LLMs
💻 Code & methodology: github.com/danielrosehill/Audio-Understanding-Bitrate-Eval-0426
TL;DR
Ran a benchmark across 12 OpenRouter audio-multimodal models × 4 dictation samples × 5 MP3 bitrates (16/24/32/48/64 kbps)… See the full description on the dataset page: https://huggingface.co/datasets/danielrosehill/Audio-Understanding-Bitrate-Eval-0426.agenticvbench-understanding-materials