Computer vision is a complex field that requires specialized expertise and adherence to best practices recommended by software and hardware vendors. Often, a naïve approach to computer vision application development leads to inefficient solutions that are unreliable or require excessive hardware, resulting in quality or economic failures.
The problem is that it is too easy to cook it incorrectly: a promising proof-of-concept often evolves into a fragile application. This problem is caused not only by the above-mentioned field complexity but also by the industry’s emerging state: computer vision technology has not yet reached a level of development comparable to the traditional software development ecosystem that developers use to build classic desktop, server, or mobile applications. An intuitive approach often leads in the wrong direction.
In the article, we discuss frameworks and tools developers can use to build state-of-the-art computer vision applications, while also identifying conceptual problems that are often overlooked and lead to inferior, if not dramatic, outcomes. We focus primarily on computer vision applications serving rather than neural model training, because the former is often underestimated, while the latter is well established and mature. Both training and serving are crucial, but our observations show that serving is usually treated as an easy-to-deliver, secondary task that does not reflect reality on the ground.
The article comes in two parts: a landscape overview (this document) and a detailed walkthrough (available soon).
Continue reading Choosing the technology for a computer vision product in 2025 (Part 1)
