Amazon Rekognition vs. Infilect InfiViz

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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If any customers want a highly scalable solution as compared to manual
audits that are cost-effective and real-time per-store-level execution insights,
then InfiViz is a well-suited platform. Extremely useful for GT/cluttered environments

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

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• High level of accuracy (>95%)
• Very fast to set up & use (2 weeks)
• very cost effective
• can be used for GT & MT

• 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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• Need merchandizers to be trained (in local language)
• Data management is a challenge that clients have to manage

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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Cost effective, Real-time insights (

• 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.

Incentivized

Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

• 2-5% sales uplifts in same store growth
• 20x RoI on spends
• Visibility into lot more stores (esp in GT)