gtoutin / WildIA

UT Austin Computational Engineering Senior Project

WildIA

UT Austin Computational Engineering Senior Project

Huge thanks goes to Alex Witt for his help on this project.

Out-of-the-box usage

Out of the box, this model provides 70% accuracy on classifying images of swift parrots and 50% accuracy or under on sulphur-crested cockatoos, gang-gang cockatoos, rainbow lorikeets, alexandrine parakeets, and images without birds. For this reason, it is recommended that this model be used out-of-the-box solely on swift parrots as the other categories are not very reliable as of now.

System requirements

You must be running Linux or MacOS to use the CLI. Windows Subsystem for Linux 1 is currently unsupported, and so is Windows itself.

Installing the CLI

To install the CLI, cd into the root folder of this project and install the dependencies. An output folder is required.

cd WildIA
pip install -r requirements.txt
mkdir Output

Using the CLI

To use the CLI, stay in the project folder and run python3 WildIA_CLI.py predict The model will be loaded and soon after a small window will appear to allow you to select the input images and output CSV file.
After the CLI is finished running, you will have your top-1 classification for each image in the CSV file.