emoji-scavenger-hunt | Emoji Scavenger Hunt is an experiment | Machine Learning library

by google TypeScript Version: Current License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | emoji-scavenger-hunt Summary

emoji-scavenger-hunt is a TypeScript library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. emoji-scavenger-hunt has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

Emoji Scavenger Hunt is an experimental web based game that makes use of TensorFlow.js to identify objects seen by your webcam or mobile camera in the browser. We show you emojis ️ and you have to find those objects in the real world before your timer runs out . Find out how we built this experiment by reading our article on the Tensorflow blog or try it for yourself at g.co/emojiscavengerhunt.

Support

Quality

Security

License

Reuse

Support

emoji-scavenger-hunt has a medium active ecosystem.

It has 795 star(s) with 198 fork(s). There are 38 watchers for this library.

It had no major release in the last 6 months.

There are 7 open issues and 17 have been closed. On average issues are closed in 31 days. There are 11 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of emoji-scavenger-hunt is current.

Quality

emoji-scavenger-hunt has 0 bugs and 0 code smells.

Security

emoji-scavenger-hunt has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

emoji-scavenger-hunt code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

emoji-scavenger-hunt 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

emoji-scavenger-hunt releases are not available. You will need to build from source code and install.

Installation instructions, examples and code snippets are available.

It has 1149 lines of code, 0 functions and 17 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

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emoji-scavenger-hunt Key Features

No Key Features are available at this moment for emoji-scavenger-hunt.

emoji-scavenger-hunt Examples and Code Snippets

No Code Snippets are available at this moment for emoji-scavenger-hunt.

Community Discussions

Trending Discussions on emoji-scavenger-hunt

model.predict is not a function while using TensorflowJS

TensorFlow.js returning TypeError: Cannot read property 'concat' of undefined when loading models

QUESTION

model.predict is not a function while using TensorflowJS

Asked 2018-May-29 at 07:57

I have scoured the far reaches of the internet like the following:

All of them have a similar way of predicting from the model:

...

ANSWER

Answered 2018-May-29 at 07:57

loadFrozenModel() returns a FrozenModel, not a tf.model, so as you can see in this example, FrozenModels use execute() instead of predict()

Source https://stackoverflow.com/questions/50576110

QUESTION

TensorFlow.js returning TypeError: Cannot read property 'concat' of undefined when loading models

Asked 2018-May-25 at 07:09

Trying to recreate the emoji scavenger hunt from google and it returned me the following error:

Here is my only code that is running at the moment:

...

ANSWER

Answered 2018-May-25 at 07:09

Discovered that the error was being caused by an upgraded version of tfjs-core and tfjs-converter. I downgraded it according to the demo and seems to be working. a long term solution as it still doesnt solve the problem for new developers. Raised it in GitHub too

Source https://stackoverflow.com/questions/50521961

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

Vulnerabilities

No vulnerabilities reported

Install emoji-scavenger-hunt

You can build your own image recognition model by running a Docker container. Dockerfiles are in training directory. Prepare images for training by dividing them into directories for each label name that you want to train. For example: the directory structure for training cat and dog will look as follows assuming image data is stored under data/images. Once the sample images are ready, you can kickstart the training by building and running the Docker container.
tensorflowjs_model.pb (the dataflow graph)
weights_manifest.json (weight manifest file)
group1-shard*of* (collection of binary weight files)

Support

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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