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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    Caffe Deep Learning Framework

    Score7 out of 10
    N/ACaffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research and by community contributors.N/A
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    Caffe Deep Learning Framework
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    Caffe Deep Learning Framework
    Free Trial
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    Entry-level Setup FeeNo setup fee
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    Community Pulse
    Caffe Deep Learning Framework
    Considered Both Products
    Open Source
    Chose Caffe Deep Learning Framework
    TensorFlow is kind of low-level API most suited for those developers who like to control the details, while Keras provides some kind of high-level API for those users who want to boost their project or experiment by reusing most of the existing architecture or models and the …
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    Would buy again
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    Delivers good value for the price
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    Happy with the feature set
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    Lived up to sales and marketing promises
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    Implementation went as expected
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    Best Alternatives
    Caffe Deep Learning Framework
    Small Businesses
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    Medium-sized Companies
    Google Cloud AI
    Score8.7 out of 10
    Enterprises
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    Score8.7 out of 10
    All AlternativesView all alternatives
    User Ratings
    Caffe Deep Learning Framework
    Likelihood to Recommend
    4.0
    (1 ratings)
    User Testimonials
    Caffe Deep Learning Framework
    Likelihood to Recommend
    Open Source
    Caffe is only appropriate for some new beginners who don't want to write any lines of code, just want to use existing models for image recognition, or have some taste of the so-called Deep Learning.
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    Pros
    Open Source
    • Caffe is good for traditional image-based CNN as this was its original purpose.
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    Cons
    Open Source
    • Caffe's model definition - static configuration files are really painful. Maintaining big configuration files with so many parameters and details of many layers can be a really challenging task.
    • Besides imagine and vision (CNN), Caffe also gradually adds some other NN architecture support. It doesn't play well in a recurrent domain, so we have to say variety is a problem.
    • Caffe's deployment for production is not easy. The community support and project development all mean it is almost fading out of the market.
    • The learning curve is quite steep. Although TensorFlow's is not easy to master either, the reward for Caffe is much less than the TensorFlow can offer.
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    Alternatives Considered
    Open Source
    TensorFlow is kind of low-level API most suited for those developers who like to control the details, while Keras provides some kind of high-level API for those users who want to boost their project or experiment by reusing most of the existing architecture or models and the accumulated best practice. However, Caffe isn't like either of them so the position for the user is kind of embarrassing.
    Incentivized
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    Return on Investment
    Open Source
    • Since we stopped using Caffe before it can reach the production phase, there is no clear ROI that can be defined.
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