1.收集超全面的开源数据集开源数据集网站数据集大小:312MB～440GBGraviti官网搜索「KITTI」查看更多相关数据集 获取地址:http://graviti.cn/open-dataset 2.CityScapes数据集 CityScapes是由奔驰自动驾驶实验室、马克思·普朗克研究所、达姆施塔特工业大学联合发布的公开数据集,专注于对城市街景的语义理解。该数据集包含50个不同的城市,在不同的季节和天气条件下https://blog.csdn.net/simon4055/article/details/130083989

2.mscocodatasetinfo@cocodataset.org Home People Dataset Tasks Evaluate Github Page Source Terms of Use https://www.cocodataset.org/

3.深度学习数据集大放送天池技术圈1. The H3D Dataset: 官网:https://usa.honda-ri.com/h3d 论文地址:https://arxiv.org/abs/1903.01568 简介:本田研究所于2019年3月发布其无人驾驶方向数据集。本数据集使用3D LiDAR扫描仪收集的大型全环绕3D多目标检测和跟踪数据集。 其包含160个拥挤且高度互动的交通场景,在27,721帧中共有100万个标记实例https://tianchi.aliyun.com/forum/post/134179

4.公开数据集本章的机器学习测试用例使用官网数据集,请从官网下载house、HIGGS、nytimes、Kosarak、DEEP1B、Mnist8m、Epsilon、MESH_DEFORM。下文所有的数据集下载解压上传均在server1节点进行。 下载官网house数据集 新建“/test/dataset/ml”目录,并进入该目录。 mkdir -p /test/dataset/ml cd /test/dataset/ml https://support.huawei.com/enterprise/zh/doc/EDOC1100356013/537ce661

5.DarmstadtNoiseDataset–DarmstadtNoiseDatasetHence, we present a novel denoising benchmark, the Darmstadt Noise Dataset (DND). It consists of 50 pairs of real noisy images and corresponding ground truth images that were captured with consumer grade cameras of differing sensor sizes. For each pair, a reference image is taken with the bashttps://noise.visinf.tu-darmstadt.de/

6.2D多人姿态估计入门指南(0):数据集使用指南(内含AIChallenger/"coco_url": "http://images.cocodataset.org/val2017/000000017905.jpg", "height": 640, "width": 480, "date_captured": "2013-11-16 18:01:33", "flickr_url": "http://farm1.staticflickr.com/44/173771776_53b9c22bb6_z.jpg", https://www.jianshu.com/p/4a05de702aa2

7.Kaggle:YourMachineLearningandDataScienceCommunityFruits-360 dataset Usability8.8· 3 GB A dataset with 123473 images of 180 fruits, vegetables, nuts and seeds International football results from 1872 to 2025 Usability10.0· 1 MB An up-to-date dataset of over 47,000 international football results https://www.kaggle.com/

8.NuSences数据集解析以及nuScenesdevkit的使用print("There are {} maps masks in the loaded dataset".format(len(nusc.map))) nusc.map[0] 2.3 nuScenes Basics NuScenes类包含几个表。每个表是一个记录列表,每个记录是一个字典。例如,类别表的第一条记录存储在 : nusc.category[0] 类别表很简单 : 它包含字段名称name和描述description。它还有一个http://zhangshiyu.com/post/95130.html

9.科学项目却没有数据集?25个数据集网站汇总? Youtube labeled Video Dataset ( https://research.google.com/youtube8m/ ) 几个月前,谷歌研究小组发布了 YouTube 标签数据集,该数据集由 800 万个 YouTube 视频 ID 和 4800 个视觉实体的相关标签组成。这来自数十亿帧的预先计算和最先进的视觉功能。 https://www.cda.cn/view/21857.html

10.Objects365Dataset> http://www.objects365.org/

11.nuscenesdatasetnuScenes is a public large-scale dataset for autonomous driving. It enables researchers to study challenging urban driving situations using the full sensor suite of a real self-driving car. NEWS Recent announcements, as well as key figures about the nuScenes dataset. https://www.nuscenes.org/

12.DIODEDatasetDIODE Dataset Dataset Download We have released the train and validation splits of DIODE depth and DIODE normal, including RGB images, depth maps, depth validity masks and surface normal maps. Download links: DIODE Depth (RGB images, Depth maps and Depth validity masks):https://diode-dataset.org/

13.TheSYNTHIAdatasetMenu Home Downloads Terms of use Bug Report Team 12345 SYNTHIA, TheSYNTHetic collection of Imagery and Annotations, is a dataset that has been generated with the purpose of aiding semantic segmentation and related scene understanding problems in the context of driving scenarios. SYNTHIA consists of https://synthia-dataset.net/

14.数据堂专业的人工智能数据服务提供商数据堂(Datatang)成立于2010年,是全球知名的人工智能训练数据服务企业。数据堂提供全栈式数据服务,包括版权数据集、数据定制、行业解决方案。数据堂已助力全球上万家企业提升模型表现力。 咨询数据服务试用数据标注平台 搜索 全部数据集 热门搜索: 文本数据方言普通话人脸 https://www.datatang.com/

15.dbpedia官网HomeBecome a Member Latest Core Releases Get the DBpedia latest core dataset here! It contains a small but useful subset of datasets from the DBpedia Extractions. With the help of Databus Latest-Core Collection, it is quite easy to fetch a fresh custom-tailored selection of DBpedia files for http://wiki.dbpedia.org/

16.中国科学数据A dataset of annual gross primary productivity in China’s terrestrial ecosystems during 2000-2020 推荐阅读 问渠哪得清如许,为有源头活水来——《中国科学数据》发刊词 作者:郭华东 出版时间:2016年6月1日 2022年黄河流域旅游资源空间分布数据集 作者:韩立钦,卢晓彤,刘慧聪,等 http://www.csdata.org/

17.TheCIFAR10datasetCIFAR10andCIFARDataset layout Python / Matlab versions data_batch_1 data_batch_2 data_batch_5 test_batch cPickle def unpickle(file): import cPickle with open(file, 'rb') as fo: dict = cPickle.load(fo) return dict data-- a 10000x3072numpyarray ofuint8s. Each row of the array stores a 32x32 http://www.cs.utoronto.ca/~kriz/cifar.html

18.NaturalEarthNatural Earth is a public domain map dataset available at 1:10m, 1:50m, and 1:110 million scales. Featuring tightly integrated vector and raster data, with Natural Earth you can make a variety of visually pleasing, well-crafted maps with cartography or GIS software. https://www.naturalearthdata.com/

19.ScienceDBDataset Resolution function data for n_TOF EAR1 Phase-3 Petar ?ugec, Marta Sabate Gilarte CSTR 31253.11.sciencedb.j00186.00697 DOI 10.57760/sciencedb.j00186.00697 PUBLIC 2025-04-18 Dataset Background, source test and simulation spectra or program for GNAS 朱铭浩, 王友宝 CSTR 31253.https://www.scidb.cn/

20.ClimateDataStoreClimate Data Store info 26 Sep 2024 Watch ourForumfor Announcements, news and other discussed topics. 1/2 Dive into this wealth of information about the Earth's past, present and future climate Cookie settings FunctionalTracking Cookies Cookies are small text files that are placed by your https://cds.climate.copernicus.eu/

21.ExtendedComplexSceneSaliencyDatasetAlthough images from MSRA-1000 have a large variety in their content, background structures are primarily simple and smooth. To represent the situations that natural images generally fall into, we extend our Complex Scene Saliency Dataset (CSSD) in [1] to a larger dataset (ECSSD) [2] with https://www.cse.cuhk.edu.hk/leojia/projects/hsaliency/dataset.html

22.berkeleydeepdrivedatasetDownload the Datasets Please download at User portal! Download Dataset Please go to our discussion board with any questions on the BDD100K dataset usage and contact Fisher Yu for other inquiries.http://bdd-data.berkeley.edu/