tswift-detection | TensorFlow Object Detection API and Cloud ML Engine | Computer Vision library

by sararob JavaScript Version: Current License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | tswift-detection Summary

tswift-detection is a JavaScript library typically used in Artificial Intelligence, Computer Vision, Tensorflow applications. tswift-detection has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

This repo contains the code from [this blog post] explaining how I built a Taylor Swift detector using the [TensorFlow object detection API] [Cloud ML Engine] and the Firebase SDKs for Cloud Functions and Cloud Storage. It looks like this:. See the blog post for details and follow the steps below to build, train, and serve your detector.

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tswift-detection has a low active ecosystem.

It has 151 star(s) with 43 fork(s). There are 8 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 1 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of tswift-detection is current.

Quality

tswift-detection has no bugs reported.

Security

tswift-detection has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

tswift-detection is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

tswift-detection releases are not available. You will need to build from source code and install.

Installation instructions are not available. Examples and code snippets are available.

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tswift-detection Key Features

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tswift-detection Examples and Code Snippets

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Community Discussions

Trending Discussions on tswift-detection

Performance difference of Tensorflow lite in Android and iOS

QUESTION

Performance difference of Tensorflow lite in Android and iOS

Asked 2020-Jun-22 at 16:22

I've trained a model to detect custom objects to be used in mobile devices (Android and iOS), my code is based in the tensorflow's examples for iOS and Android. During my tests I've been noticing a difference in performande on Android app and iOS app.

Some examples of performance (number of objects detected):

IMG - iOS - Android

img1 - 57 - 74

img2 - 9 - 33

img3 - 43 - 78

img4 - 17 - 25

I'm using a confidence thresh of 70% in both platforms. The real number of objects is a bit more than Android's result.

I did transfer learning using the ssd_mobilenet_v2_quantized_coco from the tensorflow model zoo and samples anotated by labelImg. The training process I did on google cloud following this tutorial.

My question is: What should I investigate to know the reason of the performance difference and fix it? My model should give the same result for the customer in both mobile platforms.

If it's something unclear please let me know, any help would be great. Thanks!

...

ANSWER

Answered 2020-Jun-22 at 16:22

As far as could research, the problem is with the tensorflow example app. The Android version works fine, but the iOS version has something wrong with preprocessing logic. For floating-point models, the problem has been solved in this github issue some days ago, but for quantized models it's still not solved (my case). If someone is interested in contribute or be in touch with more details on this, chek out the issue I've opened on github.

Source https://stackoverflow.com/questions/62411220

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

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For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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