Ciro Santilli @cirosantilli 35

14 million images with more than 20k categories, typically denoting prominent objects in the image, either common daily objects, or a wild range of animals. About 1 million of them also have bounding boxes for the objects. The images have different sizes, they are not all standardized to a single size like MNIST^[ref].

Each image appears to have a single label associated to it. Care must have been taken somehow with categories, since some images contain severl possible objects, e.g. a person and some object.

In practice, the ILSVRC subset of ImageNet is the most commonly used dataset.

Official project page: www.image-net.org/

The data license is restrictive and forbids commercial usage: www.image-net.org/download.php. Also as a result you have to login to download the dataset. Super annoying.

How to visualize: datascience.stackexchange.com/questions/111756/where-can-i-view-the-imagenet-classes-as-a-hierarchy-on-wordnet

The categories are all part of WordNet, which means that there are several parent/child categories such as dog vs type of dog available. ImageNet1k only appears to have leaf nodes however (i.e. no "dog" label, just specific types of dog).

A major model that performed well on ImageNet starting on 2012 and became notable is AlexNet.

TODO vs COCO dataset.

As of v7:

The images and annotations are both under CC BY, with Google as the copyright holder.

OK, now we're talking, two liner and you get a window showing bounding box object detection from your webcam feed!The accuracy is crap for anything but people. But still. Well done. Tested on Ubuntu 22.10, P51.