How should we handle deep learning projects in a professional way?
This article is not about deep learning frameworks, architectures or even about applications. It is about common problems that appear all the time for teams developing such solutions. Let’s start with an illustrative example of the kind of things discussed when working on such a project:
- Hey John, where is the most up-to-date dataset for training our solution for our current project?
- I have a copy from site A, but I think that Alice has some new images from site B.
- Ok, I will ask her to upload it to our shared disk tomorrow. Today she is working remotely. By the way, the model currently in production was trained only with images from site A, right? …
The goal of Zillin.io is to provide an easy way to mark objects or defects on images used for training deep learning models. The new version is a transformation of the platform from a basic image annotation tool to a powerful dataset collaboration platform.
Deep learning projects brought new quality, but also new challenges to the machine vision market. Many companies are now trying to answer tricky questions about project management like how to store image datasets in a way that would be both secure and convenient? Which tools to use to prepare training data, like defect annotations? How to exchange these things between team members as well as with the customers or how to keep track of versions of datasets and projects, so that it is always clear which model has been trained with which data? As an answer to all these questions, the Zillin.io web portal has been created. It is an online collaboration platform for teams creating solutions based on deep learning for image analysis. After registration at the platform, you will come directly to the main screen that shows your default workspace with an initially empty list of projects. …
Annotation is a way of data labeling images. Typically, it is an extracted vectors feature where the annotation words attempt to apply annotations automatically to new images. It is helpful for further references.
This is done with the help of keywords used on the text or image area. This is highly used by students and scientists in the AI and ML field for the purposes of training machines.
How is image annotation done?
Image annotation is normally done by humans manually using the software or tools. The annotated images are training data sets, and it is crucial to label or annotate the images with appropriate texts. This is basically the work of data scientists so that the machines easily recognize and it can be employed to computer images. …