Felis Image Classification: Project Overview

This project builds a deep learning model to automatically classify various Felis species from their images.

• Utilizing the Felis Taxonomy Image Classification dataset from Kaggle.
• Performing data preprocessing and image augmentation to enhance model generalization.
• Employing transfer learning technique on a pre-trained model for improved accuracy.
• Developing a user-friendly Flask API for real-time species prediction based on image inputs.

Code and Resources Used

Python Version: 3.10
Packages: numpy, pandas, matplotlib, tensorflow, tensorflow_datasets, flask, pillow
Flask API Setup:

• pip install -r requirements.txt
• conda env create -n <ENVNAME> -f environment.yaml (Anaconda Environment)

Dataset: https://www.kaggle.com/datasets/datahmifitb/felis-taxonomy-image-classification/data

Getting Data

The project utilizes the Felis Taxonomy Image Classification dataset from Kaggle, containing 519 JPG images of seven Felis species:

• Domestic cat (F. catus)
• European wildcat (F. silvestris)
• Jungle cat (F. chaus)
• African wildcat (F. lybica)
• Black-footed cat (F. nigripes)
• Sand cat (F. margarita)
• Chinese mountain cat (F. bieti)

Data Preprocessing

• Data Split: The dataset was divided into 80% for training and 20% for testing to ensure the model learns well and is tested fairly..
• Image Preprocessing:
  • Resizing: All images were resized to 224x224 pixels to keep them consistent.
• Image Data Augmentation: To make the model more adaptable and prevent it from overfitting, these techniques were applied:
  • Image Rotation
  • Image Translation
  • Image Flipping
  • Contrast Adjustment

Model Building

The model is based on a pre-trained EfficientNetB0 model adapted through transfer learning with the following architecture:

Model Evaluation

The model's performance is measured using categorical cross-entropy and optimized using the ADAM algorithm. The results are as follows:

Productionization

A user-friendly API is developed using Flask. The API receives image inputs and returns the predicted Felis species.