CritterClassifier: Animal Detection

This project is a deep learning-based Animal Detection System that classifies images into 100 different animal species. The system uses a Convolutional Neural Network (CNN), a type of neural network designed specifically for image classification tasks. The model is trained using TensorFlow and Keras, and the application is deployed as a web interface using Streamlit, allowing users to easily upload images and get real-time predictions.

Features

Image Classification:

Classifies uploaded images into one of 100 animal species.

Real-time Predictions:

Provides predictions with confidence scores instantly upon image upload.

User-Friendly Interface:

Simple web app built using Streamlit for easy image uploads and viewing results.

Tech Stack

1. TensorFlow & Keras

• These are widely-used open-source libraries for building and training deep learning models.
• TensorFlow provides the low-level operations, and Keras simplifies the building and training of the Convolutional Neural Network (CNN).

2. Convolutional Neural Network (CNN)

• A CNN is a type of neural network that excels in image classification tasks. It automatically learns important features and patterns from images, which is perfect for recognizing animals in photos.
• The CNN in this project is trained to classify images into 100 different animal species.

3. Streamlit

• Streamlit is a framework for building interactive web applications for machine learning projects.
• It’s used here to create a simple web interface that allows users to upload animal images and receive predictions from the trained CNN model.

4. NumPy

• NumPy is a library for handling arrays and numerical operations efficiently.
• It is used for manipulating image data and performing necessary calculations before feeding images into the model for classification.

5. Pillow (PIL)

• Pillow is the Python Imaging Library used to open, manipulate, and save various image formats.
• In this project, Pillow is used to load and preprocess the uploaded animal images, resizing them and converting them into a format that the model can work with.

6. H5 (Model Saving Format)

• The trained CNN model is saved in the H5 format, which is commonly used for saving and sharing deep learning models.
• This allows the model to be reused later for predictions without retraining.

User Flow

Data Flow

Architecture