Faster-RCNN_TF | Faster-RCNN in Tensorflow | Computer Vision library

by smallcorgi Python Version: Current License: MIT

X-Ray Key Features Code Snippets(1)Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | Faster-RCNN_TF Summary

Faster-RCNN_TF is a Python library typically used in Artificial Intelligence, Computer Vision, Tensorflow applications. Faster-RCNN_TF has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However Faster-RCNN_TF build file is not available. You can download it from GitHub.

Faster-RCNN in Tensorflow

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Faster-RCNN_TF has a medium active ecosystem.

It has 2356 star(s) with 1142 fork(s). There are 89 watchers for this library.

It had no major release in the last 6 months.

There are 248 open issues and 73 have been closed. On average issues are closed in 63 days. There are 23 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of Faster-RCNN_TF is current.

Quality

Faster-RCNN_TF has 0 bugs and 367 code smells.

Security

Faster-RCNN_TF has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Faster-RCNN_TF code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Faster-RCNN_TF 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

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

Faster-RCNN_TF has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions, examples and code snippets are available.

Faster-RCNN_TF saves you 3129 person hours of effort in developing the same functionality from scratch.

It has 6735 lines of code, 355 functions and 61 files.

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

Top functions reviewed by kandi - BETA

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

Performs the forward computation
Get next minibatch indices
Returns the next minibatch
Shuffle the indices
Adds the bounding boxes to the image
Compute the predictions for the ground - truth test
Setup the transformer
Reshape the region
Load configuration from a file
Merge a b into b
Return the next minibatches
Get next minibatch index
Set the ROI index
Add a path to sys path

Get all kandi verified functions for this library.

Faster-RCNN_TF Key Features

No Key Features are available at this moment for Faster-RCNN_TF.

Faster-RCNN_TF Examples and Code Snippets

Faster_RCNN for text detection

Python

Lines of Code : 6

License : No License

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cd $FRCN_ROOT/lib
make

cd $FRCN_ROOT
python ./tools/demo.py --model model_path

cd $FRCN_ROOT
./experiments/scripts/faster_rcnn_end2end.sh  DEVICE   DEVICE_ID  VGG16 pascal_voc

Community Discussions

Trending Discussions on Faster-RCNN_TF

Converting plt.show to OpenCV imshow function

Faster RCNN, Why does conv's result can become the bbox_deltas?

How Gradient passed by tf.py_func

QUESTION

Converting plt.show to OpenCV imshow function

Asked 2018-Jan-24 at 05:56

This is the code snippet I am currently working on.

...

ANSWER

Answered 2018-Jan-24 at 05:56

I was able to convert matplotlib plt.show to openCV imshow function using the below links: http://www.icare.univ-lille1.fr/tutorials/convert_a_matplotlib_figure

Convert image from PIL to openCV format

Source https://stackoverflow.com/questions/48374982

QUESTION

Faster RCNN, Why does conv's result can become the bbox_deltas?

Asked 2017-Apr-10 at 03:01

Here and here are the code.

I'm confused that rpn_bbox_pred is just the result of convolution.

And rpn_bbox_pred is becoming the bbox_deltas.

I can not understand this.

...

ANSWER

Answered 2017-Apr-10 at 03:01

Suppose we have a (3,2,36) conv's result.

Note that:

Source https://stackoverflow.com/questions/43250683

QUESTION

How Gradient passed by tf.py_func

Asked 2017-Jan-08 at 18:19

This is Faster R-CNN implement in tensorflow.
The proposal_layer is implement by python

i am curious about if gradient can pass by tf.py_func
the weights and biases are keep changing
so I think the gradient deliver back successful

Then I do a small test

...

ANSWER

Answered 2017-Jan-08 at 17:56

Gradient of py_func is None (just check ops.get_gradient_function(y2.op)). There's this gist by @harpone which shows how to use gradient override map for py_func.

Here's your example modified to use that recipe

Source https://stackoverflow.com/questions/41535347

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

Vulnerabilities

No vulnerabilities reported

Install Faster-RCNN_TF

Clone the Faster R-CNN repository
Build the Cython modules cd $FRCN_ROOT/lib make

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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