神经网络算法学习---获取常用训练数据集

最新推荐文章于 2025-07-10 10:13:17 发布

非文艺小燕儿_Vivien

最新推荐文章于 2025-07-10 10:13:17 发布

阅读量2.9w

点赞数 8

CC 4.0 BY-SA版权

分类专栏： Deep Learning 文章标签： Deep Learning

本文链接：https://blog.youkuaiyun.com/fuwenyan/article/details/53899363

本文介绍了深度学习中常用的两个数据集：MNIST手写数字数据集和CIFAR-10/CIFAR-100。提供了下载链接，并提及了MNIST数据集的具体处理方式，参考作者的另一篇博客。CIFAR数据集的详细信息则来自网络转载。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

1.MNIST手写数字数据集

下载网址：http://yann.lecun.com/exdb/mnist/index.html

具体下载和处理方式请参照我的第一篇博客http://blog.youkuaiyun.com/fuwenyan/article/details/53415029

2.CIFAR-10，CIFAR-100

下载网址：http://www.cs.toronto.edu/~kriz/cifar.html

以下为转载他人内容（出处未知）

1， http://www.multitel.be/cantata/

这个网址提供了大量的视频和图像的数据库下载索引，并有相应的介绍，强烈推荐！大家慢慢去找寻自己的惊喜吧

2， http://www.cvpapers.com/datasets.html

CVDatasets on the web ，主要好像是直立行人检测....

3， http://www.cvc.uab.es/adas/site/?q=node/7

里面又有好几种数据库可以下载：CVC Virtual Pedestrian Dataset、CVC-01Pedestrian Dataset、CVC-02 PedestrianDataset

4， http://www.cis.upenn.edu/~jshi/ped_html/

Databasedescription：This is an image database containing images that are usedfor pedestrian detection in the experiments reported in [1]. The images are taken from scenes around campus

and urban street. Theobjects we are interested in these images are pedestrians. Each image will haveat least one pedestrian in it.

Theheights of labeled pedestrians in this database fall into [180,390] pixels. Alllabeled pedestrians are straight up.

Thereare 170 images with 345 labeled pedestrians, among which 96 images are takenfrom around University of Pennsylvania, and other 74 are taken from aroundFudan University.

5， http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/

Caltech Pedestrian Detection Benchmark：The Caltech Pedestrian Dataset consists of approximately10 hours of 640x480 30Hz video taken from a vehicle

driving through regulartraffic in an urban environment. About 250,000 frames (in 137 approximatelyminute long segments) with a total of 350,000 bounding boxes and 2300 uniquepedestrians were annotated. The annotation includes temporal correspondencebetween

bounding boxes and detailed occlusion labels. More information can befound in our

PAMI2011 and

CVPR2009 benchmarking papers.

6， http://www.edgar-seemann.de/pd/datasets.py

PedestrianDetection

7， http://www.wisdom.weizmann.ac.il/~vision/SpaceTimeActions.html#Database

Weizmann 人体行为库

http://www.nada.kth.se/cvap/actions/

KTH人体行为数据库

http://4drepository.inrialpes.fr/public/viewgroup/6 INRIA XMAX多视角视频库

http://vision.eecs.ucf.edu/data. html

UCF Sports 数据库