vishal-ravindran/Animal_Classification

Mutli class Image classification of African animals # Image Classification README ## Overview: This repository contains code for an image classification task using TensorFlow/Keras. The code includes: 1. **Model Training**: A deep learning model is trained on a dataset of images to classify them into different categories. 2. **Evaluation**: The trained model is evaluated on a test dataset to assess its performance using metrics like accuracy, precision, recall, and F1-score. 3. **Visualization**: Confusion matrix and classification report are generated to visualize the model's performance. ## Files: - **train_model.ipynb**: Jupyter Notebook containing code for model training. - **evaluate_model.ipynb**: Jupyter Notebook containing code for model evaluation and visualization. - **requirements.txt**: List of dependencies required to run the code. ## Usage: 1. Clone the repository: `git clone github.com` `cd image-classification` 2. Install dependencies: `pip install -r requirements.txt` 3. Run the `train_model.ipynb` notebook to train the image classification model. 4. Run the `evaluate_model.ipynb` notebook to evaluate the trained model and visualize its performance. ## Dataset: - The dataset used for training and evaluation should be provided separately. - Ensure that the dataset is organized into appropriate directories, with each class of images stored in its respective folder. ## Dependencies: - TensorFlow - Keras - NumPy - Matplotlib - scikit-learn ## Screenshots: 1. **Random Samples** 2. **Data Augmentation** 3. **Plotting** 5. **Displaying Some Predictions** 6. **Confusion Matrix**