Amazon Rekognition vs. Pytorch

Amazon Rekognition is well suited for all image and videos analysis. Also, deep learning projects for image and video can easily be done using this. It is very easy to use so a beginner can also use it.

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They have created Pytorch Lightening on top of Pytorch to make the life of Data Scientists easy so that they can use complex models they need with just a few lines of code, so it's becoming popular. As compared to TensorFlow(Keras), where we can create custom neural networks by just adding layers, it's slightly complicated in Pytorch.

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• Image tagging is very good.
• Object detection is precise.
• Video tagging has become very easy using Rekognition.

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• Provides Benchmark datasets to test your custom algorithm
• Provides with a lot of pre-coded neural net components to use for your flow
• Gives a framework to write really abstract code.

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• The cost is bit more for small scale companies.
• For text processing, OCR is not available in amazon rekognition.
• Only facial search is available in image search.

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• Distributed data parallel still seems to be complicated
• Support for easy deployment to servers
• Torchvision to have support for latest models with pertained weights

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Very much suitable for many applications where the image processing features are secondary and independent of any domain. This makes it a general solution and the recognition features are returned in a JSON object in response to the API called made which is a very simple process.

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Support of Amazon is great. So far we are having a great experience using it.

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It's easy to implement and its documentation is great compared to CNTK.

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As I described in previous statements, Pytorch is much better suited than TensorFlow from a software development look. This Pythonic idea was then taken and repeated by all the other frameworks. You can get to better performance models by better understanding the deep learning model code, so I think the choice of Pytorch is easy and simple.

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• The best thing it made our work fast.
• It reduces the chance of failure of a project which is based on image or video processing.
• We can assign anyone in our team to work on this as it very easy to use.

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• I'd estimate I can build a model 50% faster on pytorch vs other frameworks

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