semantics
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.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-scholarSemantic_Segmantation_Datasetsbilingual-rlhf-financial-semantics
🧠 RLHF Semantic Contribution Dataset: Financial & Policy Bilingual Corpus
Dataset name: sunwang4gptplus/bilingual-rlhf-financial-semanticsCreated by: Sun WangLanguages: English, Mandarin ChineseLicense: MITTags: RLHF, bilingual, semantic_contribution, ux_tone, human_feedback, financial_policy, openai_auditTasks: text-classification, text-generation, reinforcement-learning
📦 Dataset Overview
This dataset was created by Sun Wang as part of a multi-day Reinforcement… See the full description on the dataset page: https://huggingface.co/datasets/sunwang4gptplus/bilingual-rlhf-financial-semantics.SemanticSTF
📌 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.