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Caffe Deep Learning Framework vs. Apache Derby

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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

    Apache Derby

    Score7 out of 10
    N/AApache Derby is an embedded relational database management system, originally developed by IBM and called IBM Cloudscape.N/A
    Pricing
    Caffe Deep Learning FrameworkApache Derby
    Editions & Modules
    No answers on this topic
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    Offerings
    Pricing Offerings
    Caffe Deep Learning FrameworkApache Derby
    Free Trial
    NoNo
    Free/Freemium Version
    NoNo
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeNo setup feeNo setup fee
    Additional Details——
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    Best Alternatives
    Caffe Deep Learning FrameworkApache Derby
    Small Businesses
    TensorFlow
    Score7.6 out of 10
    No answers on this topic
    Medium-sized Companies
    Google Cloud AI
    Score8.7 out of 10
    No answers on this topic
    Enterprises
    Google Cloud AI
    Score8.7 out of 10
    No answers on this topic
    All AlternativesView all alternativesView all alternatives
    User Ratings
    Caffe Deep Learning FrameworkApache Derby
    Likelihood to Recommend
    4.0
    (1 ratings)
    7.0
    (3 ratings)
    User Testimonials
    Caffe Deep Learning FrameworkApache Derby
    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.
    Incentivized
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    Apache
    If you need a SQL-capable database-like solution that is file-based and embeddable in your existing Java Virtual Machine processes, Apache Derby is an open-source, zero cost, robust and performant option. You can use it to store structured relational data but in small files that can be deployed right alongside with your solution, such as storing a set of relational master data or configuration settings inside your binary package that is deployed/installed on servers or client machines.
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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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    Apache
    • Apache Derby is SMALL. Compared to an enterprise scale system such as MSSQL, it's footprint is very tiny, and it works well as a local database.
    • The SPEED. I have found that Apache Derby is very fast, given the environment I was developing in.
    • Based in JAVA (I know that's an obvious thing to say), but Java allows you to write some elegant Object Oriented structures, thus allowing for fast, Agile test cases against the database.
    • Derby is EASY to implement and can be accessed from a console with little difficulty. Making it appropriate for everything from small embedded systems (i.e. just a bash shell and a little bit of supporting libraries) to massive workstations.
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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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    Apache
    • It may not scale as well as some more mature database products.
    • Used it primarily from the command line with openjpa and jdbc, and from third-party clients such as Squirrel.
    • May benefit by providing more sophisticated tools to optimize query performance.
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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.
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    Apache
    SQLite is another open-source zero-cost file-based SQL-capable database solution and is a good alternative to Apache Derby, especially for non-Java-based solutions. We chose Apache Derby as it is Java-based, and so is the solution we embedded it in. However, SQLite has a similar feature set and is widely used in the industry to serve the same purposes for native solutions such as C or C++-based products.
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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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    Apache
    • Being Open source, the resources spent on the purchase of the product are ZERO.
    • Contrary to popular belief, open source software CAN provide support, provided that the developers/contributors are willing to answer your emails.
    • Overall, the ROI was positive: being able to experiment with an open source technology that could perform on par with the corporate products was promising, and gave us much information about how to proceed in the future.
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