Making computers identify and understand what they are looking at in digital images is an ongoing challenge. Recent years have seen notable increases in the accuracy and speed of object detection due to deep learning and new applications of neural networks. In order to make it easier for developers to take advantage of these techniques Tryo Labs built Luminoth. In this interview Joaquín Alori explains how how Luminoth works, how it can be used in your projects, and how it compares to API oriented services for computer vision.
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- Your host as usual is Tobias Macey and today I’m interviewing Joaquín Alori about Luminoth, a deep learning toolkit for computer vision in Python
- How did you get introduced to Python?
- What is Luminoth and what was your motivation for creating it?
- Computer vision has been a focus of AI research for decades. How do current approaches with deep learning compare to previous generations of tooling?
- What are some of the most difficult problems in visual processing that still need to be solved?
- What are the limitations of Luminoth for building a computer vision application and how do they differ from the capabilities of something built with a prior generation of tooling such as OpenCV?
- For someone who is interested in using Luminoth in their project what is the current workflow?
- How do the capabilities of Luminoth compare with some of the various service based options such as Rekognition for Amazon or the Cloud Vision API from Google?
- What are some of the motivations for using Luminoth in place of these services?
- What are some of the highest priority features that you are focusing on implementing in Luminoth?
- When is Luminoth the wrong choice for a computer vision application and what are some of the strongest alternatives at the moment?
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