Caffe Deep Learning Framework vs. OpenAI API

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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Evolving own Business.

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• Caffe is good for traditional image-based CNN as this was its original purpose.

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• Codes
• Text
• Images
• Sound

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• 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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• Restrictions are sometimes too strong

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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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Due to in part of difficult situation, OpenAI recognizes me as a researcher and supports my projects.

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• 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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• Big question about functionality

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