The Animal Classification Using CNN project aims to classify animals using Convolutional Neural Networks (CNNs). It provides a simple and effective solution for detecting and categorizing animals based on input images.
This project utilizes deep learning techniques, specifically CNNs, to classify animals into predefined categories. By training on a dataset containing images of various animals, the model learns to differentiate between different species. The system is capable of accurately classifying animals, making it useful for a wide range of applications.
- Dataset: Dataset containing images of animals used for training and validation.
- Animal_Classification_model.h5: Pre-trained CNN model weights saved in HDF5 format.
- README.md: This file, providing an overview of the project.
- Testing.ipynb: Jupyter Notebook containing the code for testing the trained model.
- Training.ipynb: Jupyter Notebook containing the code for training the CNN model.
The Training.ipynb notebook contains the code for training the CNN model using the provided dataset. It includes data preprocessing, model architecture definition, training process configuration, and model evaluation.
The Testing.ipynb notebook demonstrates how to use the trained model for animal classification. It loads the pre-trained model weights and provides a user interface for uploading images to classify. The system then predicts the animal species present in the image and displays the result.
This project requires the following dependencies:
- Python 3.x
- TensorFlow
- Keras
- NumPy
- Matplotlib
- OpenCV
- tkinter
- PIL (Python Imaging Library)
You can install these dependencies using pip:
pip install tensorflow keras numpy matplotlib opencv-python pillow
Gulam Kibria Chowdhury
Software Developer || Competitive Programmer
Sylhet, Bangladesh
Gmail: gkchowdhury101@gmail.com