semantic
Datasets
All datasets matching “semantic”semantic-vad-eot
Semantic-VAD EOT
End-of-turn (semantic VAD) turns built from word-level forced alignments, schema-compatible
with livekit/eot-bench-data.
Each row is one user turn: an audio clip (16 kHz mp3), its words, and ordered
silence_spans. Per the eot-bench convention the last silence span is the true
end-of-turn (eot); earlier spans are mid-turn hold pauses (labels positional, not stored).
Splits
For every data type, all shards except the last form the train base; that… See the full description on the dataset page: https://huggingface.co/datasets/Scicom-intl/semantic-vad-eot.SemanticKITTIsemantic_patch_cache
HeatTok Semantic Patch Cache
Precomputed .pt caches for HeatTok. Use with HEATTOK_SEMANTIC_CACHE_DIR=/path/to/cache.
Filename pattern: {image_hash}_g1_s28.pt or {image_hash}_g1_gd1_s28.pt
semantic_patch_cache_vrsbench
Dataset: VRSBench (512×512)
Caches do not store precomputed global tokens or patch orientations.
Gaussian parameters and patch metadata are stored.
No need to regenerate .pt files — HeatTok computes global tokens and orientations online at load… See the full description on the dataset page: https://huggingface.co/datasets/Yingying11/semantic_patch_cache.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.semanticspray-plusplus
Dataset Card for SemanticSpray++ Multimodal (MCAP)
A FiftyOne build of the SemanticSpray++ dataset (Piroli, Dallabetta, Kopp,
Walessa, Meissner & Dietmayer; Institute of Measurement, Control, and
Microtechnology, Ulm University, with BMW AG), a multimodal labeled dataset
for testing camera, LiDAR, and radar perception in wet-surface "vehicle
spray" conditions. This build repackages the 36-scene labeled subset
(SemanticSpray++'s own contribution on top of the earlier… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/semanticspray-plusplus.DSLO_v0.6_Semantic_Substrate_Specification.pdfDSLO v0.7 → v0.8 Continuity Metadata Block Release Alignment: MODE_A_PUBLIC_SAFE Substrate Depth: SURFACE_ONLY
Scientific Orientation Point
DSLO v0.8 Scientific Overview DOI: 10.5281/zenodo.22181245
Orientation Class: Overview_DSLO (Root Manifold)
Continuity Rule: v0.7 → v0.8 (Registry v0.8 DOI)
Core v0.8 Scientific Surfaces
Geometry v0.8 — 10.5281/zenodo.21970123
Domain v0.8 — 10.5281/zenodo.22179299
Formatting v0.8 — 10.5281/zenodo.22179509
Registry v0.8 — 10.5281/zenodo.22181076
Machine… See the full description on the dataset page: https://huggingface.co/datasets/DSLO/DSLO_v0.6_Semantic_Substrate_Specification.pdf.