tf-object-detection | Simpler app for tensorflow object detection API | Computer Vision library

by KleinYuan Python Version: Current License: MIT

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

kandi X-RAY | tf-object-detection Summary

tf-object-detection is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Tensorflow applications. tf-object-detection has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However tf-object-detection build file is not available. You can download it from GitHub.

This is a repo for implementing object detection with pre-trained models (as shown below) on tensorflow.

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tf-object-detection has a low active ecosystem.

It has 91 star(s) with 37 fork(s). There are 5 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 11 have been closed. On average issues are closed in 302 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of tf-object-detection is current.

Quality

tf-object-detection has 0 bugs and 0 code smells.

Security

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

tf-object-detection code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

tf-object-detection 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

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

tf-object-detection has no build file. You will be need to create the build yourself to build the component from source.

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

tf-object-detection saves you 187 person hours of effort in developing the same functionality from scratch.

It has 462 lines of code, 23 functions and 12 files.

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

Top functions reviewed by kandi - BETA

kandi has reviewed tf-object-detection and discovered the below as its top functions. This is intended to give you an instant insight into tf-object-detection implemented functionality, and help decide if they suit your requirements.

Example example
Runs prediction on the given image
Display filtered results
Return the status of the task
Load the label map
Convert a label map to a list of categories
Create a category index
Load a label map
Loads a label map

Get all kandi verified functions for this library.

tf-object-detection Key Features

No Key Features are available at this moment for tf-object-detection.

tf-object-detection Examples and Code Snippets

No Code Snippets are available at this moment for tf-object-detection.

Community Discussions

Trending Discussions on tf-object-detection

How do I load the two stages of a saved Faster R-CNN separately in TF Object Detection 2.0?

QUESTION

How do I load the two stages of a saved Faster R-CNN separately in TF Object Detection 2.0?

Asked 2020-Jul-27 at 20:20

I trained a Faster R-CNN from the TF Object Detection API and saved it using export_inference_graph.py. I have the following directory structure:

...

ANSWER

Answered 2020-Jul-27 at 20:20

This was more difficult when Object Detection was only compatible with TF1, but is now pretty simple in TF2. There's a good example in this colab.

Source https://stackoverflow.com/questions/62227717

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

Vulnerabilities

No vulnerabilities reported

Install tf-object-detection

You can download it from GitHub.
You can use tf-object-detection like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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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