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Attribute Ratings

  • Keras is rated higher in 1 area: Likelihood to Recommend

Likelihood to Recommend

4.0

Caffe Deep Learning Framework

40%
1 Rating
8.2

Keras

82%
6 Ratings

Usability

Caffe Deep Learning Framework

N/A
0 Ratings
7.7

Keras

77%
2 Ratings

Support Rating

Caffe Deep Learning Framework

N/A
0 Ratings
8.2

Keras

82%
2 Ratings

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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Open Source

Keras is quite perfect, if the aim is to build the standard Deep Learning model, and materialize it to serve the real business use case, while it is not suitable if the purpose is for research and a lot of non-standard try out and customization are required, in that case either directly goes to low level TensorFlow API or Pytorch
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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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Open Source

  • One of the reason to use Keras is that it is easy to use. Implementing neural network is very easy in this, with just one line of code we can add one layer in the neural network with all it's configurations.
  • It provides lot of inbuilt thing like cov2d, conv2D, maxPooling layers. So it makes fast development as you don't need to write everything on your own. It comes with lot of data processing libraries in it like one hot encoder which also makes your development easy and fast.
  • It also provides functionality to develop models on mobile device.
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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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Open Source

  • As it is a kind of wrapper library it won't allow you to modify everything of its backend
  • Unlike other deep learning libraries, it lacks a pre-defined trained model to use
  • Errors thrown are not always very useful for debugging. Sometimes it is difficult to know the root cause just with the logs
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Pricing Details

Caffe Deep Learning Framework

Starting Price

Editions & Modules

Caffe Deep Learning Framework editions and modules pricing
EditionModules

Footnotes

    Offerings

    Free Trial
    Free/Freemium Version
    Premium Consulting/Integration Services

    Entry-level set up fee?

    No setup fee

    Additional Details

    Keras

    Starting Price

    Editions & Modules

    Keras editions and modules pricing
    EditionModules

    Footnotes

      Offerings

      Free Trial
      Free/Freemium Version
      Premium Consulting/Integration Services

      Entry-level set up fee?

      No setup fee

      Additional Details

      Usability

      Open Source

      No answers on this topic

      Open Source

      I am giving this rating depending on my experience so far with Keras, I didn't face any issue far. I would like to recommend it to the new developers.
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      Support Rating

      Open Source

      No answers on this topic

      Open Source

      Keras have really good support along with the strong community over the internet. So in case you stuck, It won't so hard to get out from it.
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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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      Open Source

      Keras is good to develop deep learning models. As compared to TensorFlow, it's easy to write code in Keras. You have more power with TensorFlow but also have a high error rate because you have to configure everything by your own. And as compared to MATLAB, I will always prefer Keras as it is easy and powerful as well.
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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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      Open Source

      • Easy and faster way to develop neural network.
      • It would be much better if it is available in Java.
      • It doesn't allow you to modify the internal things.
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