Appen vs. Caffe Deep Learning Framework

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Appen
Score 7.0 out of 10
N/A
The Appen platform combines human intelligence from over one million people all over the world with models to create training data for ML projects. Appen users can upload data to the Appen platform, and they provide the annotations, judgments, and labels needed to help create ground truth for models.N/A
Caffe Deep Learning Framework
Score 7.0 out of 10
N/A
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.N/A
Pricing
AppenCaffe Deep Learning Framework
Editions & Modules
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Offerings
Pricing Offerings
AppenCaffe Deep Learning Framework
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
AppenCaffe Deep Learning Framework
Top Pros

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Top Cons

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Best Alternatives
AppenCaffe Deep Learning Framework
Small Businesses
Jupyter Notebook
Jupyter Notebook
Score 9.2 out of 10
Jupyter Notebook
Jupyter Notebook
Score 9.2 out of 10
Medium-sized Companies
Posit
Posit
Score 9.8 out of 10
Posit
Posit
Score 9.8 out of 10
Enterprises
Posit
Posit
Score 9.8 out of 10
Posit
Posit
Score 9.8 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
AppenCaffe Deep Learning Framework
Likelihood to Recommend
10.0
(1 ratings)
4.0
(1 ratings)
User Testimonials
AppenCaffe Deep Learning Framework
Likelihood to Recommend
Appen
It is well suited for the users and potential employee who are free of any job perspective and need their free time to be utilized. Users can use their free time to be used for submission of interesting tasks.
Whereas the number of tasks are very less and processing time is also very extensive and recruitment takes time more.
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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
Appen
  • Project listing
  • Hiring of the potential and qualified users
  • Tracking of the projects
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Open Source
  • Caffe is good for traditional image-based CNN as this was its original purpose.
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Cons
Appen
  • Selection procedure is bit .
  • The questionnaire need to be reviewed.
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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
Appen
Appen offers projects mostly related to my native language and also according to my expertise . It offers very interesting projects to be completed , which requires not very expertise and less time to be completed for each task. It is also very convenient to use after selection for the task and also well rewarding against the time consumed for the task completion.
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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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Return on Investment
Appen
  • It has Positive impact as it provides opportunity for new jobs in my area of expertise.
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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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