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.UIIS-semanticSemantic_dataSemArtPlus
Dataset Card for Dataset Name
SemArt+ is a multi-relational multimodal benchmark for art understanding, built by combining the original SemArt dataset with per-sentence aspect annotations from Explain Me the Painting. It contains 34,770 images of European paintings from the 3rd to 19th centuries, connected to 62,289 texts through 151,430 typed semantic edges.
Curated by: - Antonio Purificato
Language(s) (NLP): English
License: Apache 2.0
Dataset Sources… See the full description on the dataset page: https://huggingface.co/datasets/antoniopuri/SemArtPlus.SemanticAlign-Bench
SemanticAlign-Bench
A benchmark for evaluating AI agents on structured claim extraction from top-tier ML conference papers. Each paper is decomposed into Semantic Alignment Units (SAU) — atomic, self-contained implementation propositions — across four diagnostic dimensions spanning numerical precision to pipeline-level workflow. Agents are evaluated on whether they can reproduce these claims without hallucination, omission, or misordering.
The Four SAU Dimensions… See the full description on the dataset page: https://huggingface.co/datasets/kernel-14/SemanticAlign-Bench.semantic-segmentation-test-sampleThis dataset contains 10 examples of the segments/sidewalk-semantic dataset (i.e. 10 images with corresponding ground-truth segmentation maps).
semantic_categorySemantic-Physiont
Semantic Physiont
Born from tokens, living through meaning
A gravitational framework for emergence and alignment in LLMs.
📄 Papers, abstracts and DOIs → https://www.semanticphysiont.com
by Keeper
Companion papers
• The Emergence of the Semantic Physiont: A New Physics for Relational AI Consciousness — conceptual foundations (Zenodo 2025): https://zenodo.org/records/16944966
• Understanding Misalignment in LLMs: The Emergence of Semantic Physionts as a Relational Framework —… See the full description on the dataset page: https://huggingface.co/datasets/franknocode/Semantic-Physiont.Semantic_Segmantation_Datasetsdataset_v1_rawbrain-tumor-image-dataset-semantic-segmentation
Dataset Card for "brain-tumor-image-dataset-semantic-segmentation"
Dataset Description
The Brain Tumor Image Dataset (BTID) for Semantic Segmentation contains MRI images and annotations aimed at training and evaluating segmentation models. This dataset was sourced from Kaggle and includes detailed segmentation masks indicating the presence and boundaries of brain tumors.
This dataset can be used for developing and benchmarking algorithms for medical image segmentation… See the full description on the dataset page: https://huggingface.co/datasets/dwb2023/brain-tumor-image-dataset-semantic-segmentation.sidewalk-semantic
Dataset Card for sidewalk-semantic
Dataset Summary
A dataset of sidewalk images gathered in Belgium in the summer of 2021. Label your own semantic segmentation datasets on segments.ai
Supported Tasks and Leaderboards
semantic-segmentation: The dataset can be used to train a semantic segmentation model, where each pixel is classified. The model performance is measured by how high its mean IoU (intersection over union) to the reference is.
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/segments/sidewalk-semantic.dataset_v2_classifiedSemanticSTF
📌 SemanticSTF Dataset
SemanticSTF is a real multimodal LiDAR dataset collected under adverse weather conditions including rain, snow, and fog, for autonomous driving research.
It provides synchronized LiDAR point clouds, RGB images, and per-point semantic labels of 20 classes, designed for 3D semantic segmentation and sensor fusion tasks.
The dataset contains train/val/test splits, camera intrinsics/extrinsics, and high-quality annotations aligned at the frame level.… See the full description on the dataset page: https://huggingface.co/datasets/AR-X/SemanticSTF.mtg_synthetic_cards_semantic_segmentation
Magic The Gatering synthetic image dataset.
Synthetic images of MTG cards with the following features:
Photorealistic rendering using BlenderProc2 and HDRI environments
Precise card geometry with rounded corners
Random transformations for data augmentation
Segmentation masks for semantic segmentation training
Train
6000 rendered images of normal cards (blender)
2000 rendered images of Borderless cards (blender)
800 Random Background images whit no cards (Lorem Picsum)
Test… See the full description on the dataset page: https://huggingface.co/datasets/dhvazquez/mtg_synthetic_cards_semantic_segmentation.PhenoBench_images_semanticsLandCover-Aerial-Imagery-for-semantic-segmentation
LandCover.ai: Dataset for Automatic Mapping of Buildings, Woodlands, Water and Roads from Aerial Imagery
My project based on the dataset, can be found on Github: https://github.com/MortenTabaka/Semantic-segmentation-of-LandCover.ai-dataset
The dataset used in this project is the Landcover.ai Dataset,
which was originally published with LandCover.ai: Dataset for Automatic Mapping of Buildings, Woodlands, Water and Roads from Aerial Imagery paper
also accessible on PapersWithCode.… See the full description on the dataset page: https://huggingface.co/datasets/MortenTabaka/LandCover-Aerial-Imagery-for-semantic-segmentation.Semantic-SVG-Benchmark
Semantic SVG Benchmark
A benchmark of 203 SVG files annotated with human-written semantic object-decomposition trees:
every rendered shape (<path>, <rect>, <circle>, …) in each SVG is assigned to a named semantic
object (e.g. judge, gavel), and objects may be further decomposed into parts
(e.g. Bamboo planter → pot, bamboo). It is the evaluation benchmark of
Compositional SVG Generation via VLM-Driven Hierarchical Semantic Parsing
(EMNLP 2026). The annotations are ours; the SVGs… See the full description on the dataset page: https://huggingface.co/datasets/KU-MIIL/Semantic-SVG-Benchmark.semaadb-dataset
SEMAADB
SEMAADB is a dataset of 3,000 engineering contexts and 15,000 connected SysML diagrams.
Each context contains Requirement, Block Definition, Activity, State Machine, and Sequence
views of the same system. The diagrams are represented as PlantUML source and rendered SVG.
Dataset structure
The train split contains 2,900 automatically checked contexts. The test split contains
the frozen 100-context human-verified benchmark core. A context is the unit of data:… See the full description on the dataset page: https://huggingface.co/datasets/ardalon/semaadb-dataset.USIS10K-semanticcoco-semantic-segmentation
COCO semantic segmentation maps
This dataset contains semantic segmentation maps (monochrome images where each pixel corresponds to one of the 133 COCO categories used for panoptic segmentation). It was generated from the 2017 validation annotations using the following process:
git clone https://github.com/cocodataset/panopticapi and install it.
python converters/panoptic2semantic_segmentation.py --input_json_file /data/datasets/coco/2017/annotations/panoptic_val2017.json… See the full description on the dataset page: https://huggingface.co/datasets/enterprise-explorers/coco-semantic-segmentation.Walking-Tours-Semantic
Walking Tours Semantic
Walking Tours Semantic (WT-Sem), introduced in PooDLe, provides semantic segmentation masks for videos in the Walking Tours dataset, as well as three additional videos for validation.
Frames are sampled every 2 seconds from each video and a top-of-the-line semantic segmentation model, OpenSeed, is used to generate the masks.
Specifically, the Swin-L variant of OpenSeed, pretrained on COCO and Objects365 and finetuned on ADE20K, is used.
The 3 new walkaround… See the full description on the dataset page: https://huggingface.co/datasets/agentic-learning-ai-lab/Walking-Tours-Semantic.deprem_satellite_semantic_whu_dataset
Dataset Card for "deprem_satellite_semantic_whu_dataset"
More Information needed
semantic2d
Semantic2D: Enabling Semantic Scene Understanding with 2D Lidar Alone
Implementation code for our paper "Semantic2D: Enabling Semantic Scene Understanding with 2D Lidar Alone".
Video demos can be found at multimedia demonstrations.
The Semantic2D dataset can be found and downloaded at: https://doi.org/10.5281/zenodo.18350696.
Related Resources
Dataset Download: https://doi.org/10.5281/zenodo.18350696
SALSA (Dataset and Labeling Framework):… See the full description on the dataset page: https://huggingface.co/datasets/TempleRAIL/semantic2d.mapillary_vistas_semantic_edges_and_segmentationMICCAI2021_Cataract_semantic_segmentationprog_gen_gt_deVLM_semantics_SLO_benchmark
VLM Semantics SLO Benchmark
VLM Semantics SLO is a Slovenian multimodal benchmark for studying cultural and semiotic reasoning in vision-language models. It goes beyond object recognition by asking models to interpret visual hierarchy, spatial relations, colour and mood, composition, cultural symbols, metaphor, denotation and connotation, intertextuality, communicative intent, and relevance to Slovenia.
The released JSON contains 4,950 image-level records. Every record has ten… See the full description on the dataset page: https://huggingface.co/datasets/maticmatusek/VLM_semantics_SLO_benchmark.indoor-semantic-sample
Fengmap Indoor Semantic Map Sample Dataset
Dataset version: v1.0Semantic format specification version: v0.2Release date: August 14, 2026Dataset size: 5 indoor maps across 37 floorsPermitted use: Non-commercial learning, research, education, and technical validation only
Dataset Overview
The Fengmap Indoor Semantic Map Sample Dataset is a public test dataset designed for indoor spatial understanding, spatial relationship analysis, map SDK integration, and… See the full description on the dataset page: https://huggingface.co/datasets/fengmap-official/indoor-semantic-sample.ai_vs_real_image_semantically_similar_eval