This project focuses on building a robust model for classifying images of cats and dogs. It employs various machine learning and deep learning techniques, ranging from traditional algorithms to advanced techiniques as CNN and ResNet50
notebooks/: Jupyter notebooks with detailed explanations and code for all the different methods.models/: Source code for the models and data processing scripts.reports/: Contains project reports and visualizations.README.md: This file.
- Clone the repository:
git clone https://github.com/yourusername/cat-dog-classification.git cd cat-dog-classification - Create a virtual environment and activate it:
python -m venv venv source venv/bin/activate # On Windows use `venv\Scripts\activate`
- Istall the required packeges:
pip install -r requirements.txt
Run the Jupyter notebooks to explore the data and train models. Use the provided scripts in the src/ directory to preprocess data and train models from the command line.
HOG + SVM Histogram of Oriented Gradients (HOG) features are extracted and fed into a Support Vector Machine (SVM) for classification. This method achieved an accuracy of 78%.
Random Forest A Random Forest classifier is used with pixel values as features, resulting in a lower accuracy compared to HOG + SVM.
Bag of Visual Words (BOVW) Uses a vocabulary of BRISK features to classify images with a K-Nearest Neighbors (KNN) classifier. The accuracy achieved was 67.5%.
Convolutional Neural Networks (CNN) A CNN was implemented using TensorFlow and Keras, achieving high accuracy by learning hierarchical representations of the images.
ResNet-50 Utilizes the ResNet-50 architecture pre-trained on ImageNet, fine-tuned for the cat and dog classification task. This model provided the highest accuracy.
- HOG + SVM: 78% accuracy
- Random Forest: Lower accuracy
- BOVW: 67.5% accuracy
- CNN: High accuracy with advanced fitting and regularization techniques
- ResNet-50: Highest accuracy among all models
- Alexander Sandoval
- Ryan Goldberg
- Blanche Horbach
- Valentina Samboni
Raschka, Sebastian, et al. Machine Learning with PyTorch and Scikit-Learn: Develop Machine Learning and Deep Learning Models with Python. Packt, 2022. Shalev-Shwartz, Shai, and Shai Ben-David. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, 2014. Hastie, Trevor, et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. Springer, 2009.
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third-party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. The naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results-oriented visualizations
│ └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
Project based on the cookiecutter data science project template. #cookiecutterdatascience