Recently, I was looking for a toy dataset for my new book's chapter (you can subscribe to the updates here) on instance segmentation. And, I really wanted to have something like the Iris Dataset for Instance Segmentation so that I would be able to explain the model without worrying about the dataset too much. But, alas, it is not always possible to get a dataset that you are looking for. I actually ended up looking through various sources on the internet but inadvertently found that I would need to download a huge amount of data to get anything done. Given that is not at all the right way to go about any tutorial, I thought why not create my own dataset.

HyperLabel--a new desktop data labeling application for Machine Learning (ML) just announced by Sixgill, LLC--offers the fastest path to creating high-quality labeled datasets for better ML models. With HyperLabel, there's no need to upload files to an external service. Users retain complete ownership, privacy and control of their data, while accelerating project onboarding and completion with quick and easy usability anchored on the desktop. It's all cloud-free, highly scalable and locally installed. HyperLabel is designed to be fast, easy and accurate, from setup to label export.