datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
sonata-dental-dataset
Sonata Dental Dataset
牙科疾病检测与分类数据集（基于 Sonata 采集数据整理）。
⚠️ 原始完整版（含 unknown 类）已备份，未随本仓库发布。
目录结构
Sonata/
├── image/ # 检测图像（1645 张）
├── label/ # 检测标注（labelme JSON，1645 个，与 image 一一对应）
└── Periodontal_Disease/ # 牙周病分类子集（按类别分目录）
├── gingival_diseases/ # 1103 张
├── non_periodontal_disease/ # 591 张
└── periodontitis/ # 661 张
1. 检测子集（Detection）
图像：image/，1645… See the full description on the dataset page: https://huggingface.co/datasets/Kellection/sonata-dental-dataset.S3simulator_Plus_Synthetic_Sonar_dataset
S3Simulator+ Synthetic Sonar Dataset (Mine)
Summary
A dataset of 4,180 synthetic side-scan sonar images of the mine class (cylinderical, Truncated cone),
generated with S3Simulator+, an extended version of the S3Simulator
simulator. The dataset is described in the paper cited below. It is intended
for research on underwater image analysis when labeled real sonar data is
scarce.
The ship and plane classes are released separately, generated with
S3Simulator: see… See the full description on the dataset page: https://huggingface.co/datasets/caicv/S3simulator_Plus_Synthetic_Sonar_dataset.S3simulator_Synthetic_Sonar_dataset
S3Simulator Synthetic Sonar Dataset (Ship and Plane)
Summary
A dataset of 7,721 synthetic side-scan sonar images of two target classes,
ship and plane, generated with S3Simulator and described in the paper
cited below. It is intended as a benchmark for underwater image analysis when
labeled real sonar data is scarce.
The mine class is released separately in a companion dataset generated with
S3Simulator+.
Dataset contents
Class
Images… See the full description on the dataset page: https://huggingface.co/datasets/caicv/S3simulator_Synthetic_Sonar_dataset.reys-ornaments-dataset
Historical Printed Ornaments: Dataset and Tasks
Official repository of the paper "Historical Printed Ornaments: Dataset and Tasks". We introduce Rey's Ornaments dataset which focuses on an XVIIIth century bookseller, Marc Michel Rey, providing a consistent set of ornaments with a wide diversity and representative challenges. We additinally highlight three complex tasks that are of critical interest to book historians: (a) clustering, (b) element discovery, and (c) unsupervised… See the full description on the dataset page: https://huggingface.co/datasets/sonatbaltaci/reys-ornaments-dataset.