🐒 Monkey Species Classifier with CNN

This project is a deep learning model designed to classify different species of monkeys using images. Built with Convolutional Neural Networks (CNN) in Python using TensorFlow and Keras, it demonstrates a complete pipeline from data preprocessing to model evaluation and prediction.

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📌 Project Overview

• 🧠 Uses a custom CNN architecture for image classification
• 🔍 Trained on a dataset of labeled monkey species images
• 🧼 Includes image preprocessing and data augmentation (ImageDataGenerator)
• 🎯 Evaluates model performance using accuracy and loss metrics
• 🔁 Supports testing with new images for prediction

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🧰 Tech Stack

• Python 3
• TensorFlow / Keras
• NumPy, Matplotlib
• Jupyter Notebook

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📂 Project Structure

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📊 Model Architecture (Simplified)

• Conv2D + ReLU + MaxPooling
• Dropout layers to reduce overfitting
• Flatten + Dense layers
• Softmax output layer for multi-class prediction

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🧪 Example Results

After training, the model achieves high accuracy on both training and validation sets. You can test the model by running a single image through it:

model.predict(processed_image)

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🚀 How to Run

1. Clone the repo
2. Make sure your dataset is correctly structured
3. Run the notebook monkeys.ipynb step by step
4. (Optional) Save and reload the model with model.save() and load_model()

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🔮 Future Improvements

• Use transfer learning (e.g. with MobileNet or ResNet)
• Expand dataset for more robustness
• Add web interface for live predictions

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👩‍💻 Author

Sara Sánchez García LinkedIn | sara.sanchez.g29@gmail.com