Keras-FasterRCNN | keras implementation of Faster R-CNN | Machine Learning library

by you359 Python Version: Current License: MIT

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kandi X-RAY | Keras-FasterRCNN Summary

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

keras implementation of Faster R-CNN

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Keras-FasterRCNN has a low active ecosystem.

It has 290 star(s) with 217 fork(s). There are 12 watchers for this library.

It had no major release in the last 6 months.

There are 60 open issues and 14 have been closed. On average issues are closed in 118 days. There are 5 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of Keras-FasterRCNN is current.

Quality

Keras-FasterRCNN has 0 bugs and 54 code smells.

Security

Keras-FasterRCNN has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Keras-FasterRCNN code analysis shows 0 unresolved vulnerabilities.

There are 4 security hotspots that need review.

License

Keras-FasterRCNN 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

Keras-FasterRCNN 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.

Keras-FasterRCNN saves you 1016 person hours of effort in developing the same functionality from scratch.

It has 2308 lines of code, 83 functions and 19 files.

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

Top functions reviewed by kandi - BETA

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

Base function
Resnet block
Convolution block layer
Batch normalization layer
Classifier
Time distributed block layer
Time distributed convolution layer
The inception resnet block
Calculate anchors for the given image
Skip sample from img_data
Augment image
Calculate the rpn feature ratio
Calculate the IOU image
Calculate the intersection between two arrays
Compute the intersection of two intersecting boxes
Calculate image size
Convert an RPN layer into a ROI
Generate a list of non - max suppression
Function to apply rgr
Export a pre - trained model
Construct an Inception Resnet V2 model
Compute the probability map for each bounding box
Format an image
Write a single log to the callback

Get all kandi verified functions for this library.

Keras-FasterRCNN Key Features

No Key Features are available at this moment for Keras-FasterRCNN.

Keras-FasterRCNN Examples and Code Snippets

No Code Snippets are available at this moment for Keras-FasterRCNN.

Community Discussions

Trending Discussions on Keras-FasterRCNN

Special function on feature maps of convolutional layer

QUESTION

Special function on feature maps of convolutional layer

Asked 2019-Jan-25 at 17:02

In Short:

How do I pass feature maps from convolutional layer defined in Keras to a special function (region proposer) which is then passed to other Keras layers (e.g Softmax classifier)?

Long:

I'm trying to implement something like Fast R-CNN (not Faster R-CNN) in Keras. The reason for this is because I'm trying to implement a custom architecture as seen in the figure below:

Here's the code for the figure above (excluding candidates input):

...

ANSWER

Answered 2019-Jan-25 at 17:02

To my best understanding, selective-search take an input and return n no of patches of different (H,W). So in your case, feature-map is of dims (164,164,96), you can assume (164,164) as the input for selective-search and it will give you n number of patch, for exp as (H1,W1), (H2,W2),.... So you can now append all the channel as it is, to that patch, so it becomes as of dims (H1,W1,96),(H2,W2,96),.....

Note: But there is downside of doing this too. Selective-Search algorithm use the strategy in which it breaks the image in grids and then re-join those patch as per the heatmap of the object. You would not be able to do that on feature-map. But you can use random search method on that and it can be useful.

Source https://stackoverflow.com/questions/54366838

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

Vulnerabilities

No vulnerabilities reported

Install Keras-FasterRCNN

You can download it from GitHub.
You can use Keras-FasterRCNN 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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