This page lists of datasets suitable for research using image and video-based datasets.

Fashion-MNIST | Dataset to detect different clothing items

Fashion-MNIST consists of 60,000 training images and 10,000 test images. It is an MNIST-like fashion product database. The developers believe MNIST has been overused so they created this as a direct replacement for that dataset. Each image is in greyscale and associated with a label from 10 classes.

10 Labels — 60,000 Images
Link : Fashion MNIST

CelebA | Labeled Dataset to Detect Celebrity

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.

10,177 Labels — 202,599 Images
Link : CelebA

YouTube 8M | Labeled Dataset Understanding Video Content

Labeled dataset for action recognition from YouTube videos that are between 2–10mins have at least 1000 views. It collects human-verified labels on about 1000 classes to detect a certain element in a video such as Barbie or unboxing videos.

1000 Labels — 8 Million Videos
Link : YouTube 8M

Sports-1M Dataset | Database for classifying sports

This dataset consists of 1 million YouTube videos belonging to a taxonomy of 487 classes of sports. The entire list of the sports labeled and the thumbnail from the database can be foundhere.

487 Labels — 1 Million Videos
Link : Sports 1M

  • image_dataset.txt
  • Last modified: 2019/08/04 15:45
  • by waag