Faster-RCNN-TensorFlow | TensorFlow implementation of Faster RCNN | Computer Vision library

by walsvid Python Version: Current License: MIT

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

kandi X-RAY | Faster-RCNN-TensorFlow Summary

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

TensorFlow implementation of Faster RCNN for Object Detection

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Faster-RCNN-TensorFlow has a low active ecosystem.

It has 13 star(s) with 3 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 7 open issues and 8 have been closed. On average issues are closed in 6 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Faster-RCNN-TensorFlow is current.

Quality

Faster-RCNN-TensorFlow has no bugs reported.

Security

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

License

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

Faster-RCNN-TensorFlow 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.

Top functions reviewed by kandi - BETA

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

Setup network
Adds tensor
Batch normalization
Calculate the average pool
An anchor layer
Unmap a numpy array
Compute ground truth accuracy for ground - truth images
Transform bounding box
Load selective search ROI
Load region proposal network
Locate CUDA configuration
Load region proposal box
Load selective search roidb
Parse command line arguments
Perform selective search
Detects the poses of the image
Create a config from a list
Decorator to define a layer
Create a proposal target layer
Setup the network
Generate a proposal layer
Compute the bounding boxes for each image
Load region proposal box
Generate a demo image
Append flipped images to the image file
Load the ROID of the region proposal network

Get all kandi verified functions for this library.

Faster-RCNN-TensorFlow Key Features

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

Faster-RCNN-TensorFlow Examples and Code Snippets

No Code Snippets are available at this moment for Faster-RCNN-TensorFlow.

Community Discussions

Trending Discussions on Faster-RCNN-TensorFlow

Visual Studio keeps asking for Python source file when running a Python extension project

QUESTION

Visual Studio keeps asking for Python source file when running a Python extension project

Asked 2018-Aug-20 at 15:30

I use Python3.6 Generate tensorflow's Faster-RCNN's (on github: https://github.com/dBeker/Faster-RCNN-TensorFlow-Python3.5 ) .pb file and run this .pb file OK with Python3.6.

I also write an MFC program (Win10 + VS2015 + C++ + Tensorflow1.8 both GPU and CPU version) to read and test this .pb file, but when I run the code :

...

ANSWER

Answered 2018-Aug-20 at 15:30

The Visual Studio's debugger is going to examine Python's source code for whatever reason (from what I can see, some C-level error occurs) and is asking for its location.

To provide it, you need to get the source exactly equivalent to whatever your copy of Python was made from. The bogus path that the dialog provides by default is the path saved inside the executable. This is where the sources were on the build machine where and when your copy of Python was built.

Anaconda apparently can't be bothered to provide the corresponding sources for their binary packages (a trait shared by many private packaging ecosystems; for software covered by (L)GPL, this is a license violation, but Python's license allows that), leaving you on your own here.

Fortunately, Python leaves some pointers in its executable on which sources you need. E.g., for Anaconda's Python 3.6.5:

Source https://stackoverflow.com/questions/51414013

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

Vulnerabilities

No vulnerabilities reported

Install Faster-RCNN-TensorFlow

Compile cython and roi_pooling_op, you may need to modify make.sh for your platform.
Clone the Faster R-CNN repository
Build the Cython modules ROOT = Faster-RCNN-TensorFlow cd ${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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