keras_frcnn | Keras Implementation of faster-rcnn | Computer Vision library

by jinfagang Python Version: Current License: No License

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kandi X-RAY | keras_frcnn Summary

keras_frcnn is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Tensorflow, Keras applications. keras_frcnn has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

Keras Implementation of faster-rcnn

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keras_frcnn has a low active ecosystem.

It has 510 star(s) with 277 fork(s). There are 12 watchers for this library.

It had no major release in the last 6 months.

There are 63 open issues and 14 have been closed. On average issues are closed in 43 days. There are 17 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of keras_frcnn is current.

Quality

keras_frcnn has 0 bugs and 0 code smells.

Security

keras_frcnn has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

keras_frcnn code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

keras_frcnn does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

keras_frcnn releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

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

keras_frcnn saves you 821 person hours of effort in developing the same functionality from scratch.

It has 1885 lines of code, 78 functions and 21 files.

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

Top functions reviewed by kandi - BETA

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

Train Kitti model
Calculate the II angle of the image
Calculate the size of the image
Return the intersection of two intersects
Intersection between two arrays
Convert rpn layer to ROI
Return a list of non - max suppression
Helper function to apply rgr
Runs the model on an image
Predict a single image
Run prediction
Format an image
Calculate the thumbnail region of the image
Calculates the RPN for the network
Augment image data
Checks if the current class is in img_data
Compute the probability map for each bounding box
Base function
Convolution block
Computes the identity block
Predict class
Visualize detections
Generate images
Classifier for classifier
Classifier layer
Parse command line arguments

Get all kandi verified functions for this library.

keras_frcnn Key Features

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keras_frcnn Examples and Code Snippets

No Code Snippets are available at this moment for keras_frcnn.

Community Discussions

Trending Discussions on keras_frcnn

my own implementation of FastRCNN cannot perform well on balanced data

QUESTION

my own implementation of FastRCNN cannot perform well on balanced data

Asked 2020-Jun-09 at 06:06

2020.06.09

There are 700 images for training, each of them extract 64 rois and make a mini-batch, when batch-size is set to 2, it cast 350 steps to complete training, but for RCNN, each target is extracted as a single image resized to 224*224, there will be 64*700=44800 images, each of which contains more information and features than a 7*7 pooled feature map and I guess that's why it seems under-fitting though RCNN could be train well on same data.

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Use fully balanced data, and acc drops to 0.53 (training data)

...

ANSWER

Answered 2020-Jun-09 at 06:06

Damn, now I know what problem it is:

In ROI_Pooling.py:

Source https://stackoverflow.com/questions/62239980

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

Vulnerabilities

No vulnerabilities reported

Install keras_frcnn

You can download it from GitHub.
You can use keras_frcnn 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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