Caffe Deep Learning Framework

Caffe Deep Learning Framework

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Score 7.0 out of 100
Caffe Deep Learning Framework

Overview

What is Caffe Deep Learning Framework?

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research and by community contributors.
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What is Caffe Deep Learning Framework?

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

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Product Details

What is Caffe Deep Learning Framework?

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

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Reviews and Ratings

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Score 4 out of 10
Vetted Review
Verified User
Caffe was chosen by us, only for the experimental purposes of trying out some DL frameworks, as it is one of the earliest DL frameworks, and is dedicated for vision, image recognition and classification. We wanted to see how it may be used in the commercial invoice image auto-classification use case. As soon as TensorFlow was introduced, Caffe was not used anymore.
  • Caffe is good for traditional image-based CNN as this was its original purpose.
  • 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.
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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