PyTorch Competitors and Alternatives

Competitor
# Comparisons
Rating
Find out what your peers are saying about OpenVINO vs. TensorFlow and other solutions. Updated: January 2021.
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Read reviews of PyTorch competitors and alternatives

Raed Lafi
Machine/Deep Learning Engineer at UpWork Freelancer
Real User
Nov 26, 2020
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…
Gabir Yousef
Machine Learning Engineer at Upwork
Real User
Top 20
Dec 25, 2020
Easy to set up with great documentation and good stability

What is our primary use case?

I primarily used the solution for computer vision applications, for example, detection and segmentation, and OCR. We used an architecture from a published paper. It was based on TensorFlow and we upgraded it and developed on it. I also worked on face verification and likeness detection. We are working on anti-spoofing detection. We did some things around face verification and likeness detection. I used TensorFlow specifically. I've also used the solution to detect hands, tracking customers in the supermarkets, and using the solution for detecting the pickup and dropping of objects from shelf… more »

Pros and Cons

  • "Google is behind TensorFlow, and they provide excellent documentation. It's very thorough and very helpful."
  • "I know this is out of the scope of TensorFlow, however, every time I've sent a request, I had to renew the model into RAM and they didn't make that prediction or inference. This makes the point for the request that much longer. If they could provide anything to help in this part, it will be very great."

What other advice do I have?

When we did the utilization applications, we were deploying on digital ocean servers. For the projects that I'm working on now, we are planning to deploy it on its own port attached to the robot. We haven't done it, yet. We are finishing the project right now. For deploying the solutions, I deploy them on the digital ocean. I'd recommend the solution. I'd also recommend users considering the solution do a bit of studying. There are some great courses on Coursera and there's a recent one called DeepLearning.AI that is extremely useful. Overall, as I use the product pretty much everywhere, I…
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