The following are three publicly available datasets for experiments related to federated learning or machine learning.Availability of Data and Materials: The datasets used to support the findings of this study are publicly available on Internet as follow: MNIST: http://yann.lecun.com/exdb/mnist/; SVHN: https://gas.graviti.cn/dataset/data-decorators/SVHN; CIFAR-10: https://www.cs.toronto.edu/~kriz/cifar.html.

COCO is a large-scale object detection, segmentation, and captioning dataset.

CelebFaces Attributes Dataset (CelebA) is a large-scale face attributes dataset with more than 200K celebrity images, each with 40 attribute annotations. The images in this dataset cover large pose variations and background clutter. CelebA has large diversities, large quantities, and rich annotations, including 10,177 number of identities,202,599 number of face images, and 5 landmark locations, 40 binary attributes annotations per image.

The Comprehensive Cars (CompCars) dataset contains data from two scenarios, including images from web-nature and surveillance-nature. The web-nature data contains 163 car makes with 1,716 car models. There are a total of 136,726 images capturing the entire cars and 27,618 images capturing the car parts. The full car images are labeled with bounding boxes and viewpoints.

We have created a 17 category flower dataset with 80 images for each class. The flowers chosen are some common flowers in the UK. The images have large scale, pose and light variations and there are also classes with large varations of images within the class and close similarity to other classes. The categories can be seen in the figure below. We randomly split the dataset into 3 different training, validation and test sets. A subset of the images have been groundtruth labelled for segmentation.