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Talking about the google vision
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Andre Henriques 2023-12-20 16:07:27 +00:00
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main.bib
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@ -132,3 +132,18 @@ author={
note = {[Online; accessed 18. Dec. 2023]},
url = {https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/training-model.html#tm-console}
}
@misc{google-vision-price-sheet,
title = {{Pricing {$\vert$} Vertex AI Vision {$\vert$} Google Cloud}},
journal = {Google Cloud},
year = {2023},
month = dec,
note = {[Online; accessed 20. Dec. 2023]},
url = {https://cloud.google.com/vision-ai/pricing}
}
@misc{google-vision-product-recognizer-guide,
title = {{Product Recognizer guide}},
year = {2023},
month = dec,
note = {[Online; accessed 20. Dec. 2023]},
url = {https://cloud.google.com/vision-ai/docs/product-recognizer}
}

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@ -1,5 +1,4 @@
Non papers, industry resources:
- https://docs.aws.amazon.com/rekognition/latest/dg/what-is.html
- https://www.clarifai.com/company/customers
- https://cloud.google.com/vision

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report/report.tex
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@ -91,18 +91,17 @@
%Amazon provides bespoque machine learning services that if were contacted would be able to provide image classification services. Amazon provides general machine learning services \cite{amazon-machine-learning}.
Amazon provides an image classification service called rekognition \cite{amazon-rekognition}. This services provides multiple services from face regonition, celebrity regonition, object regonition and others. One of this services is called custom labels \cite{amazon-rekognition-custom-labels} which provides the most similiar service, to the one this project is about. The custom labels service allows the users to provide custom datasets and labels and using AutoML the rekognition service would generate a model that allows the users to classify images acording to the generated model.
Amazon provides an image classification service called ''Rekognition`` \cite{amazon-rekognition}. This services provides multiple services from face regonition, celebrity regonition, object regonition and others. One of this services is called custom labels \cite{amazon-rekognition-custom-labels} which provides the most similiar service, to the one this project is about. The custom labels service allows the users to provide custom datasets and labels and using AutoML the rekognition service would generate a model that allows the users to classify images acording to the generated model.
The models generated using Amazon's rekognition dont provide ways to update the number of labels that were originaly created without generating a new project which will envolve retraining a large part of the model which would envolve large downtime between being able to add new classes. Training models also could take 30 minutes to 24 hours \cite{amazon-rekognition-custom-labels-training} which cloud result in up to 24 hours of lag between the need of creating a new label and beeing able to classify that label. A problem also arrises when the uses needs to add more than one label at the same time, for example the user sees the need to create a new label and starts a new model training, but while the model is traning a new label is also needed the user now either stops the training of the new model and retrains a new one or waits until the one currently running stops and trains a new one. This is not very efficient.
The models generated using Amazon's rekognition dont provide ways to update the number of labels that were originaly created without generating a new project which will envolve retraining a large part of the model which would envolve large downtime between being able to add new classes. Training models also could take 30 minutes to 24 hours \cite{amazon-rekognition-custom-labels-training} which cloud result in up to 24 hours of lag between the need of creating a new label and beeing able to classify that label. A problem also arrises when the uses needs to add more than one label at the same time, for example the user sees the need to create a new label and starts a new model training, but while the model is traning a new label is also needed the user now either stops the training of the new model and retrains a new one or waits until the one currently running stops and trains a new one. If new classification classes are required with frequency this might not be the best platform to choose.
%https://aws.amazon.com/machine-learning/ml-use-cases/
%https://aws.amazon.com/rekognition/image-features/
Similarly Google also has ''Cloud Vision Api`` \cite{google-vision-api} which provides similiar services to Amazon's Rekognition. But Google's Vision Api apears to be more targetd at videos than images, as indicated by their proce sheet \cite{google-vision-price-sheet}. They have tag and product idetifiers, where every image only has one tag or product. The product identififer system seams to work diferently than the Amazon's regonition and worked based on K neighorings giving the user similar products on not classification labels \cite{google-vision-product-recognizer-guide}.
This method is more effective at allowing users to add new types of products but as it does not give defined classes as the output the system does not give the target functionality that this project is hoping to achive.
\subsection{Alternatives to my Project}
There currently exist systems that do image classification, like Google Vision AI \cite{google-vision-api}, and Amazon's Rekoginition \cite{amazon-rekognition}.