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Cat vs. Dog Image Classification with SVM

This repository contains a Python notebook demonstrating a simple image classification task using a Support Vector Machine (SVM) classifier. The TensorFlow "cats_vs_dogs" dataset is utilized for training and testing. To address potential memory issues, a percentage of the dataset is loaded, and images are resized and flattened on-the-fly to reduce memory consumption. Hyperparameter optimization is performed using random search with cross-validation. The SVM model is trained, and its accuracy is evaluated on a test set.

Dependencies

  • Python 3.x
  • TensorFlow
  • TensorFlow Datasets
  • scikit-learn
  • OpenCV (opencv-python)

Install dependencies using (if not installed):

pip install tensorflow tensorflow-datasets scikit-learn opencv-python

Usage

  1. Clone the repository:

    git clone https://github.com/dipeshbabu/PRODIGY_ML_03.git
  2. Navigate to the project directory:

    cd PRODIGY_ML_03
  3. Run the notebook:

    jupyter notebook PRODIGY_ML_03.ipynb

    Make sure to run the notebook in an environment that supports GPU acceleration for faster execution.

Configuration

  • Adjust the percentage of the dataset loaded in the split argument of tfds.load based on your available memory.
  • Modify hyperparameter ranges and distribution in the param_dist dictionary for random search in the code based on your experimentation.

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