Skip to content

sanjeev309/deep_bbox_regression_keras

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

4 Commits
 
 
 
 
 
 

Repository files navigation

Deep Bounding Box Regression using Keras

A Keras model to infer bounding box coordinates of synthetic black images with white boxes

Getting Started

  • Clone the repo into your folder

    git clone https://github.com/sanjeev309/deep_bbox_regression_keras.git

  • Launch jupyter notebook in the repo directory and open the notebook keras_bbox_regression.ipynb

Prerequisites

Keras, Matplotlib, OpenCV 3.2

Summary

Deep Models are good for tasks of classification. However they can also be utilised to solve regression problems.

Using Deep regression, a CNN model with Dense end nodes can be utilised to predict the 4 coordinates

of a bounding box namely (x1,y1) and (x2,y2)

For example:

Given the input image (100 * 100):

The ideal model predicts the bounding box coordinates :

[0.37, 0.31, 0.91, 0.81]

which when rescaled to the image corresponds to

[37, 31, 91, 81]

    i.e. (x1,y1) = [37,31] {Top-Left Coordinate}

         (x2,y2) = [91,81] {Bottom-Right Coordinate}

Author

License

This project is licensed under the MIT License - see the LICENSE file for details

Acknowledgements

  • The database is generated by the tool available here : synthetic_bbox_regression_db_tool
  • The loss function has been used from a different repo that I cannot locate. This Ack will be updated soon.

About

Deep bounding box regression built using keras framework

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published