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🤖 This repository houses a collection of image classification models for various purposes, including vehicle, object, animal, and flower classification. Each classifier is built using deep learning techniques and pre-trained models to accurately identify and categorize images based on their respective classes. Also includes a sample Flask backend!
This is an end-to-end animal face classification model with Keras, KerasTuner, Mlflow, SQLite, Streamlit, and FastAPI which can classify animal faces as either cat, dog or wildlife
An animal classification system developed using transfer learning with the ResNet50 convolutional neural network pre-trained on ImageNet. Designed to distinguish between three classes of animals—cats, dogs, and snakes—the system demonstrates a high accuracy of approximately 98.67% on a balanced dataset comprising 3,000 images.
Custom trained YOLOv8 model (98.72% accuracy) to identify two wildlife species : Fishing Cat and Hyena with frontend in HTML and backend using FastAPI.