The Image Classification Project aims to classify images into different categories using machine learning techniques. This project focuses on categorizing images based on emotions, specifically classifying them as “happy” or “sad.”
• Image Classification: Classifies images into predefined categories using a trained model.
• Data Preprocessing: Prepares images for training by resizing and normalizing.
• Model Training: Utilizes deep learning techniques to build an effective classifier.
• Jupyter Notebook: An interactive notebook (Getting Started.ipynb) for exploring the model training process.
📦ImageClassification ┣ 📜README.md # Project documentation ┣ 📜Getting Started.ipynb # Jupyter notebook for model development ┣ 📂data # Folder containing training images ┃ ┣ 📂happy # Images classified as happy ┃ ┗ 📂sad # Images classified as sad ┗ 📜models # Folder to save trained models
1. Data Preparation:
• Images are organized into folders labeled happy and sad.
• The images are preprocessed (resized and normalized) for training.
2. Model Training:
• The project utilizes convolutional neural networks (CNNs) to classify images based on the provided dataset.
• The model is trained to learn the features of each category.
3. Evaluation:
• The trained model is evaluated on a separate validation set to assess its accuracy and performance.
1. Jupyter Notebook:
• Run the Getting Started.ipynb to load the dataset, preprocess the images, train the model, and evaluate its performance.