ImageNet是根据WordNet层次结构组织的图像数据集。在ImageNet中，目标是为了说明每个synset提供平均1000幅图像。 每个concept图像都是质量控制和人为标注的(quality-controlled and human-annotated)。 在完成之后，希望ImageNet能够为WordNet层次结构中的大多数concept提供数千万个干净整理的图像。 数据集目录可参看本项目代码仓ReadMe（https://git.openi.org.cn/Open_Dataset/imagenet）

MNISTData数据集是由10类28∗28的灰度图片组成，训练数据集包含60000张图片，测试数据集包含10000张图片。 对于想要在现实世界数据上尝试学习技术和模式识别方法，同时在预处理和格式化上花费最少的精力的人来说，这是一个很好的数据库。

MNISTData数据集是由10类28∗28的灰度图片组成，训练数据集包含60000张图片，测试数据集包含10000张图片。

MNISTData数据集为模型训练数据集

CIFAR-10数据集包含10个类别的60000个32x32彩色图像，每个类别6000个图像。 有50000张训练图像和10000张测试图像。http://www.cs.toronto.edu/~kriz/cifar.html?usg=alkjrhjqbhw2llxlo8emqns-tbk0at96jq

该数据集有100个类别，每个类别包含600张图像。 每个课程有500张训练图像和100张测试图像， CIFAR-100中的100个类别分为20个超类。 https://www.cs.toronto.edu/~kriz/cifar.html

SVHN是一个现实世界的图像数据集，用于开发机器学习和对象识别算法，超过600,000位数的图像。SVHN是从Google街景图像中的门牌号获得的。http://ufldl.stanford.edu/housenumbers/

WIDER包含61个事件类别和大约50574个用事件类标签注释的图像。 http://yjxiong.me/event_recog/WIDER/

德国交通标志基准测试是在IJCNN 2011上举行的多类，单图像分类挑战。数据集包含：40多个类，总共50,000多张图像。http://benchmark.ini.rub.de/?section=gtsrb

imagenet2012 tiny. train and val. train with 1000 classes and each class has 20 images. val with 1000 classes and each class has 5 images.

imagenet2012 small. train and val. train with 1000 classes and each class has 100 images. val with 1000 classes and each class has 10 images.

imagenet2012 whole dataset

SKU级别的商品图像数据集

Leeds Sports Pose数据集，有1k张训练、1k张验证组成，目录结构为LSP：TRAIN、VAL

本数据集集华为云垃圾分类数据集、各垃圾分类公开数据集及网络爬虫等于一身，经机器、人工多重高质量清洗筛选整合而成。本数据拥有训练集：43685张；验证集：5363张；测试集：5363张；总类别数：158类。另外，本数据集格式为ImageNet格式，符合多数主流api接口。

CVPR2022-track1-p2

Cifar10 binary dataset.

MNIST

军事图像数据集

MNIST

cifar10数据集

imagenet

哈哈哈

The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size 28x28 image.

城市风景数据集

天池零基础入门语义分割-地表建筑物识别

ModelNet40 dataset contains 12,311 pre-aligned shapes from 40 categories, which are split into 9,843 (80%) for training and 2,468 (20%) for testing. The CAD models are in Object File Format (OFF). Matlab functions to read and visualize OFF files are provided in Princeton Vision Toolkit (PVT). To build the core of the dataset, a list of the most common object categories in the world was compiled, using the statistics obtained from the SUN database. Once a vocabulary for objects was established, 3D CAD models belonging to each object category was collected using online search engines by querying for each object category term. Then, human workers on Amazon Mechanical Turk were hired to manually decide whether each CAD model belonged to the specified cateogries, using an in-house designed tool with quality control. To obtain a very clean dataset, 10 popular object categories were chosen while manually deleted the models that did not belong to these categories. Furthermore, manual alignment of orientation of CAD

gaitset

无

imagenet-tiny