Speeds up the development process but needs to evolve more to stay relevant
What is our primary use case?
We used this solution to make a face recognition system that uses gender and age prediction. We have to recognize and register faces for security reasons. Since we don't know all the people that are passing by our cameras, we track them and assign a unique ID for each face. We keep tracking them as long as they are visible within the camera field. After that, we predict the age and gender of those people, and then we send them to our database system to produce statistics. Finally, there is a second-team that analyses the statistics. We also use it for transfer learning, which is a style… more »
Pros and Cons
"Caffe has helped our company become up-to-date in the market and has helped us speed up the development process of our projects."
"The concept of Caffe is a little bit complex because it was developed and based in C++. They need to make it easier for a new developer, data scientist, or a new machine or deep learning engineer to understand it."
What other advice do I have?
On a scale from one to ten, I would give Caffe a rating of seven. If you're going to use this solution, then you have to learn C++. You need to understand how C++ works so that you'll have a better understanding of how the memory is handled and the resources are handled in C++. After that, you have to start learning Caffe step-by-step. Caffe is a new concept, especially the Blob. Caffe is based on Blob so you need to understand what that is and how it's implemented because, with Caffe, you have to make a transformation to fit that. You need to understand all of this before you start. After…