Project Description
This repository contains the final project submission for the Image Classification Project as part of the IDCamp 2024 program. The project focuses on classifying garbage images into distinct categories using machine learning techniques implemented in Jupyter Notebook.
Programming Language
- Jupyter Notebook
Key Features
- Data Collection and Preprocessing: Gathering a comprehensive dataset of garbage images and performing preprocessing steps such as resizing, normalization, and data augmentation.
- Deep Learning Models: Implementation of deep learning models, including Convolutional Neural Networks (CNNs), for image classification.
- Transfer Learning: Utilization of pre-trained models (Xception) to enhance classification accuracy and reduce training time.
- Model Training and Optimization: Training the models with various hyperparameters and optimization techniques to achieve the best performance.
- Evaluation and Validation: Evaluating model performance using metrics such as accuracy, precision, recall, and F1-score. Validating the model on a separate test dataset.
- Visualization: Visualizing training progress, loss curves, and model predictions to better understand model behavior and performance.
Technologies Used
- Jupyter Notebook: For interactive development, experimentation, and documentation of the machine learning workflow.
- TensorFlow and Keras: For building and training deep learning models.
- Pandas and NumPy: For data manipulation and preprocessing.
- Matplotlib and Seaborn: For creating informative visualizations of data and model performance.
- Scikit-learn: For additional machine learning tools and model evaluation metrics.
Project Highlights
- Comprehensive Workflow: The project covers the entire image classification pipeline from data collection to model evaluation, showcasing a robust understanding of deep learning techniques.
- Reproducibility: All steps and results are documented in Jupyter Notebooks, ensuring that the work can be easily reproduced and verified.
- Practical Application: Demonstrates practical application of deep learning algorithms to solve a real-world problem of garbage classification, reflecting a hands-on approach to learning.
- Performance Optimization: Highlighting the use of transfer learning and hyperparameter tuning to achieve high classification accuracy.
Links
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Repository: GitHub - alrescha79-cmd/garbage-classification
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Dataset: Kaggle - Garbage Classification
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Notebook: Kaggle Notebook - Submission Garbage Classification