irfanali1995/wildlife-cnn-vs-mobilenet

This project implements and compares a custom-built CNN with a transfer learning approach using MobileNetV2 to classify African wildlife images. # Wildlife Image Classification: Custom CNN vs MobileNetV2 This project focuses on classifying African wildlife images using deep learning techniques. It compares the performance of a custom Convolutional Neural Network (CNN) with a transfer learning approach using MobileNetV2. ## 📁 Dataset The dataset consists of labeled images of African wildlife. It was split into training, validation, and test sets using the `splitfolders` library in the following ratio: - 70% Training - 20% Validation - 10% Testing ## 🛠️ Model Architectures ### 🔹 Custom CNN - 4 Convolutional layers with ReLU activation - MaxPooling and Dropout for regularization - Flatten + Dense layers for classification - Trained from scratch ### 🔹 MobileNetV2 - Pretrained model used for feature extraction - GlobalAveragePooling + Dense layers on top - Fine-tuned for the dataset ## 🧪 Evaluation Metrics - Confusion Matrix - Classification Report ## 🔍 Key Findings - MobileNetV2 significantly outperformed the custom CNN in terms of accuracy and generalization. - Transfer learning enables faster convergence and better feature extraction, especially when the dataset is limited. ## 🚀 Libraries & Tools - TensorFlow / Keras - splitfolders - Matplotlib / Seaborn - Scikit-learn ## 📌 How to Run 1. Install required libraries: ```bash pip install tensorflow split-folders ``` 2. Run the notebook step-by-step in a Jupyter environment or on Kaggle. 3. Ensure dataset is placed in the correct path (update `base_dir` if needed).