Daha-hussein/Somali-Amphibians

# Somali Amphibian Image Classification using Convolutional Neural Networks (CNN) ## Overview This project aims to classify images of Somali amphibians using deep learning techniques, specifically Convolutional Neural Networks (CNNs). The model is trained to recognize and classify different species of amphibians native to Somalia based on input images. ## Dataset The dataset consists of a collection of images featuring various species of Somali amphibians. The images are labeled with their corresponding species names, facilitating supervised learning for classification tasks. The dataset has been curated and annotated to ensure accurate training and evaluation of the CNN model. ## Model Architecture The CNN model architecture is designed to effectively learn and discriminate between different features present in amphibian images. It comprises multiple convolutional layers followed by max-pooling layers for feature extraction and dimensionality reduction. The final layers include fully connected (dense) layers for classification. ## Training and Evaluation The model is trained on a portion of the dataset and validated on a separate subset to monitor its performance and prevent overfitting. Training metrics such as accuracy, loss, and validation accuracy are tracked to assess the model's progress during training. Additionally, the model's performance is evaluated on a test set to measure its ability to generalize to unseen data. ## Requirements - Python 3.x - TensorFlow - NumPy - Pandas - Matplotlib ## Usage 1. Clone this repository: ``` git clone github.com ``` 2. Install the required dependencies: ``` pip install -r requirements.txt ``` 3. Prepare your dataset or use the provided dataset. 4. Train the CNN model using the provided script or notebook. 5. Evaluate the trained model's performance on test data. 6. Make predictions on new amphibian images using the trained model. ## Credits - Dataset: Image of Somali Amphi …