Caffe vs TensorFlow comparison

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Caffe Logo
270 views|196 comparisons
100% willing to recommend
TensorFlow Logo
6,254 views|3,925 comparisons
100% willing to recommend
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Executive Summary

We performed a comparison between Caffe and TensorFlow based on real PeerSpot user reviews.

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Quotes From Members
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Here are some excerpts of what they said:
Pros
"Caffe has helped our company become up-to-date in the market and has helped us speed up the development process of our projects."

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"It is also totally Open-Source and free. Open-source applications are not good usually. but TensorFlow actually changed my view about it and I thought, "Look, Oh my God. This is an open-source application and it's as good as it could be." I learned that TensorFlow, by sharing their own knowledge and their own platform with other developers, it improved the lives of many people around the globe.""It provides us with 35 features like patch normalization layers, and it is easy to implement using the Kras library when the Kaspersky flow is running behind it.""Optimization is very good in TensorFlow. There are many opportunities to do hyper-parameter training.""I would rate the solution an eight out of ten. I am not a developer but more of an account manager. I can find what I want with TensorFlow. I haven’t contacted technical support for any issues. Since TensorFlow is vastly documented on the internet, I usually find some good websites where people exchange their views about the solution and apply that.""It's got quite a big community, which is useful.""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.""Our clients were not aware they were using TensorFlow, so that aspect was transparent. I think we personally chose TensorFlow because it provided us with more of the end-to-end package that you can use for all the steps regarding billing and our models. So basically data processing, training the model, evaluating the model, updating the model, deploying the model and all of these steps without having to change to a new environment.""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."

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Cons
"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."

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"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 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.""Personally, I find it to be a bit too much AI-oriented.""JavaScript is a different thing and all the websites and web apps and all the mobile apps are built-in JavaScript. JavaScript is the core of that. However, TensorFlow is like a machine learning item. What can be improved with TensorFlow is how it can mix in how the JavaScript developers can use TensorFlow.""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.""The solution is hard to integrate with the GPUs.""TensorFlow Lite only outputs to C.""There are connection issues that interrupt the download needed for the data sets. We need to prepare them ourselves."

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Pricing and Cost Advice
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  • "TensorFlow is free."
  • "I think for learners to deploy a project, you can actually use TensorFlow for free. It's just amazing to have an open-source platform like TensorFlow to deploy your own project. Here in Russia no one really cares about licenses, as it is totally open source and free. My clients in the United States were also pleased to learn when they enquired, that licensing is free."
  • "We are using the free version."
  • "It is open-source software. You don't have to pay all the big bucks like Azure and Databricks."
  • "I did not require a license for this solution. It a free open-source solution."
  • "I am using the open-source version of TensorFlow and it is free."
  • "I rate TensorFlow's pricing a five out of ten."
  • "It is an open-source solution, so anyone can use it free of charge."
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    Top Answer: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.
    Top Answer:It is an open-source solution, so anyone can use it free of charge.
    Top Answer:The versatility of the concept is undeniable, but it can pose a challenge for developers unfamiliar with machine learning. For newcomers to the field, the learning curve can be steep, often requiring… more »
    Ranking
    17th
    Views
    270
    Comparisons
    196
    Reviews
    0
    Average Words per Review
    0
    Rating
    N/A
    4th
    Views
    6,254
    Comparisons
    3,925
    Reviews
    7
    Average Words per Review
    534
    Rating
    9.0
    Comparisons
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    Overview

    Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors.

    TensorFlow is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains.

    Sample Customers
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    Airbnb, NVIDIA, Twitter, Google, Dropbox, Intel, SAP, eBay, Uber, Coca-Cola, Qualcomm
    Top Industries
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    VISITORS READING REVIEWS
    Manufacturing Company14%
    Computer Software Company12%
    Educational Organization11%
    University9%
    Company Size
    No Data Available
    REVIEWERS
    Small Business57%
    Midsize Enterprise21%
    Large Enterprise21%
    VISITORS READING REVIEWS
    Small Business20%
    Midsize Enterprise15%
    Large Enterprise65%
    Buyer's Guide
    AI Development Platforms
    April 2024
    Find out what your peers are saying about Microsoft, Google, TensorFlow and others in AI Development Platforms. Updated: April 2024.
    770,292 professionals have used our research since 2012.

    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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