Introduction The Anole Classifier leverages machine learning techniques to accurately classify anole species based on images. This project aims to aid the Stroud Lab's middle schooler citizen scientist program in identifying these reptiles.

Installation:

bash cd anole-classifier Install the required dependencies:

bash pip install -r requirements.txt

Usage To use the classifier, follow these steps:

Prepare your dataset and ensure it is organized as specified in the Dataset section.

Train the models:

bash python train.py python train_ensemble.py python train_detection.py

Use the trained model to classify new images:

bash python test.py

Dataset The dataset used for this project consists of labeled images of different anole species. The dataset should be organized into subfolders for each species, with images in each subfolder. To download the dataset use the utility.py file. The file path must be changed to one of the csv files and the invoktion uncommented.

This dataset is generated using iNaturalist export: https://www.inaturalist.org/observations/export Using queries: ?quality_grade=research&identifications=any&field%3Abanded=yes&taxon_ids%5B%5D=36488%2C36391%2C36455, ?quality_grade=research&identifications=any&field%3Abanded=yes&taxon_ids%5B%5D=11646%2C36514

Example structure:

project/ Raw/ taxon_001.jpg taxon_002.jpg ... Raw-Test/ taxon_001.jpg taxon_002.jpg ...

Model The classifier uses a convolutional neural network (CNN) architecture. The model is built using TensorFlow and Keras. The detailed architecture can be found in the train.py, train_ensemble.py, and train_detection.py files.

Results The trained model achieves an accuracy of [X%] on the test set.