This project classifies images of animals into 10 categories: dog, horse, elephant, butterfly, chicken, cat, cow, sheep, squirrel, and spider.

• data/: Contains datasets.
• models/: Stores trained models.
• main.py: Script to train the model.
• ui.py: Script for the tkinter user interface.
• README.md: Overview and instructions.
• requirements.txt: Project dependencies.

1. Clone the repository:

   git clone https://github.com/Mayokun-Sofowora/Animal-Image-Recognition.git
   cd project
   
   

pip install venv .venv\Scripts\activate # This command should be run Initially to create the virtual environment command line. pip install -r requirements.txt

After making changes

pip freeze > requirements.txt deactivate

1. Define the scope of the project: The objective is to recognize different animal species from images. Input: Images of animals. Output: Predicted animal species.

2. Data collection and preparation: Collect a dataset of animal images (e.g. from Kaggle). Preprocessed the images (resize, normalize, etc.). Split the dataset into training, validation, and test sets.

3. Build the neural network model: Design a Convolution Neural Network (CNN) for image recognition. Implement the model using a deep learning framework like TensorFlow or PyTorch.

4. Implement evolutionary algorithm: Use evolutionary algorithms to optimize the neural network architecture and hyperparameter. Implement the genetic algorithm (GA) to evolve the best model configurations.

5. Train and evaluate the model: Train the CNN model on the training dataset. Use the validation dataset to fine-tune the model. Evaluate the model performance on the test dataset.

6. Deploy the model: Deploy the trained model for interface. Create a simple user interface to upload images and display predictions.

7. Documentation and reporting: Document the code and methodology. Prepare a report and presentation detailing the project.