If you’ve ever tried to explain how computer vision works to your friends,
family or colleagues, you probably know that it can be hard to do. This is
especially
Creating a computer vision model, at the outset, seems like a pretty involved
task. Even if you’re using an end-to-end solution
[https://blog.roboflow.com/what-does-end-to-end-really-mean/] like Roboflow, the
Computer vision, on the whole, is an ambitious undertaking.
We are developing technology that can see the world as we see it - to recognize
simple objects like trees and
“You could do what Roboflow does yourself but…why would you?”
-Jack Clark, Co-Founder of Anthropic [https://www.anthropic.com/], former Policy
Directory at OpenAI [https://openai.com/],
It’s
Developing, deploying and optimizing computer vision models used to be a
cumbersome, painful process. With Roboflow, we sought to democratize this
technology, which (first and foremost) meant knocking down the
If deployment is the magic of computer vision, then the act of training a model
is the proverbial wave of that wand. Training a computer vision model is the
process
Last week, I attended the 2021 Startup Summit
[https://startupgrind.tech/conference/?utm_source=website&utm_medium=navigation]
from my home office in Des Moines, Iowa.
Perhaps one of
