We performed a comparison between Caffe and TensorFlow based on real PeerSpot user reviews.
Find out what your peers are saying about Microsoft, Google, TensorFlow and others in AI Development Platforms."Caffe has helped our company become up-to-date in the market and has helped us speed up the development process of our projects."
"Google is behind TensorFlow, and they provide excellent documentation. It's very thorough and very helpful."
"It is open-source, and it is being worked on all the time. You don't have to pay all the big bucks like Azure and Databricks. You can just use your local machine with the open-source TensorFlow and create pretty good models."
"The most valuable features are the frameworks and the functionality to work with different data, even when we have a certain quantity of data flowing."
"The most valuable feature of TensorFlow is deep learning. It is the best tool for deep learning in the market."
"Edge computing has some limited resources but TensorFlow has been improving in its features. It is a great tool for developers."
"It empowers us to seamlessly create and deploy machine learning models, offering a versatile solution for implementing sophisticated environments and various types of AI solutions."
"TensorFlow improves my organization because our clients get a lot of investment from their investors and we are progressively improving the products. Every six months we release new features."
"Optimization is very good in TensorFlow. There are many opportunities to do hyper-parameter training."
"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."
"It would be nice if the solution was in Hungarian. I would like more Hungarian NAT models."
"The solution is hard to integrate with the GPUs."
"TensorFlow deep learning takes a lot of computation power. The more systems you can use, the easier it is. That's a good ability, if you can make a system run immediately at the same time on the same task, it's much faster rather than you having one system running which is slower. Running systems in parallel is a complex situation, but it can improve. There is a lot of work involved."
"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."
"There are connection issues that interrupt the download needed for the data sets. We need to prepare them ourselves."
"It doesn't allow for fast the proto-typing. So usually when we do proto-typing we will start with PyTorch and then once we have a good model that we trust, we convert it into TensorFlow. So definitely, TensorFlow is not very flexible."
"However, if I want to change just one thing in the implementation of TensorFlow functions I have to copy everything that they wrote and I change it manually if indeed it can be amended. This is really hard as it's written in C++ and has a lot of complications."
"There are a lot of problems, such as integrating our custom code. In my experience model tuning has been a bit difficult to edit and tune the graph model for best performance. We have to go into the model but we do not have a model viewer for quick access."
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Caffe is ranked 17th in AI Development Platforms while TensorFlow is ranked 4th in AI Development Platforms with 16 reviews. Caffe is rated 7.0, while TensorFlow is rated 9.0. The top reviewer of Caffe writes "Speeds up the development process but needs to evolve more to stay relevant". On the other hand, the top reviewer of TensorFlow writes "Effective deep learning, free to use, and highly stable". Caffe is most compared with PyTorch, whereas TensorFlow is most compared with Microsoft Azure Machine Learning Studio, Google Vertex AI, OpenVINO, IBM Watson Machine Learning and Hugging Face.
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