clean-set | deep assignment alternative to the object spread operator

by fwilkerson JavaScript Version: 1.1.2 License: MIT

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

kandi X-RAY | clean-set Summary

clean-set is a JavaScript library. clean-set has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can install using 'npm i clean-set' or download it from GitHub, npm.

A deep assignment alternative to the object spread operator and Object.assign

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Quality

Security

License

Reuse

Support

clean-set has a low active ecosystem.

It has 165 star(s) with 8 fork(s). There are 2 watchers for this library.

It had no major release in the last 12 months.

There are 2 open issues and 3 have been closed. On average issues are closed in 3 days. There are 7 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of clean-set is 1.1.2

Quality

clean-set has 0 bugs and 0 code smells.

Security

clean-set has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

clean-set code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

clean-set is licensed under the MIT License. This license is Permissive.

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

Reuse

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

Deployable package is available in npm.

Installation instructions, examples and code snippets are available.

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clean-set Key Features

No Key Features are available at this moment for clean-set.

clean-set Examples and Code Snippets

No Code Snippets are available at this moment for clean-set.

Community Discussions

Trending Discussions on clean-set

Trouble evaluating test data with Tensorflow's Object Detection API

QUESTION

Trouble evaluating test data with Tensorflow's Object Detection API

Asked 2020-Mar-28 at 03:20

Overview

I'm working on an object detector using Tensorflow's Object Detection API using custom training data. My understanding so far has been that I can feed my training data into the model and then use a trained model[0] to evaluate test data, and that following that evaluation, I'd be able to see a set of images like the following to figure out what the trained model was able to detect in each image in my test data.

What I've done so far

Operating with this assumption, I've been able to create a training dataset in .tfrecord format, and I've been able to feed this into my model trainer using the following command:

...

ANSWER

Answered 2019-Jan-10 at 01:24

My understanding was wrong. The model_main.py program is self-contained; it performs both the training and evaluation step, and I didn't need to do anything with that directory afterward. All that is needed to see the output of the test is to attach a TensorBoard instance to the model directory.

Source https://stackoverflow.com/questions/54098247

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

Vulnerabilities

No vulnerabilities reported

Install clean-set

Includes builds for commonjs, umd, and esm and is less than ~~200b~~ 182b gzip (thanks to @lukeed).

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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