Table of Contents

• Description
• Organization
• Application Stages
• Additional Setup
• Testing
• Windows Development

Description

Take a folder that contains photos of birds and other stuff, get a folder of just birds, no other stuff.

Organization

• architectures - neural network models
• assets - GUI assets
• config - hyperparameter configurations for models
• tests - unit tests
• utils - image utilities

Application Stages

1. Blur Detection: Can we clearly see subjects in the image?
2. Object Classification: Given a clear image, is there a bird in it?
3. Object Localization: Given an image containing a bird, where is the bird?
4. Image Comparison: Are there similiar images in this collection?
5. Image Manipulation: Can we crop this image in an aesthetically-pleasing manner?

Additional Setup

Due to GitHub's file size limit of 100MB, much of our object localization network configuration cannot be hosted directly on GitHub. To alleviate this process, we are using Git Large File Storage (GLFS) in order to provide a seamless Git experience. In the case of datasets, Google Drive was used to facilitate ease of transfers.

Testing

All unit tests can be found in the tests folder. The test suite can be run by using the following command:

python3 -m unittest discover

More information about testing can be found here.

Windows Development

If developing for this application on Windows, there are a number of issues of which one should be aware. Those issues, and their solutions, can be found here.