https://github.com/bottomsnode/sct_ml_3

This repository contains code for classifying images of cats and dogs using a Support Vector Machine (SVM) classifier. The features for the classification are extracted using a pre-trained VGG16 model.
https://github.com/bottomsnode/sct_ml_3

data-science deep-learning kaggle-dataset keras-tensorflow machine-learning opencv-python svm-classifier

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This repository contains code for classifying images of cats and dogs using a Support Vector Machine (SVM) classifier. The features for the classification are extracted using a pre-trained VGG16 model.

• Host: GitHub
• URL: https://github.com/bottomsnode/sct_ml_3
• Owner: BottomsNode
• License: mit
• Created: 2024-07-06T16:40:54.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-07-06T17:21:04.000Z (6 months ago)
• Last Synced: 2024-07-07T17:55:28.290Z (6 months ago)
• Topics: data-science, deep-learning, kaggle-dataset, keras-tensorflow, machine-learning, opencv-python, svm-classifier
• Language: Jupyter Notebook
• Homepage:
• Size: 21.8 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Cats vs Dogs Image Classification

![Cats vs Dogs Image Classification](https://github.com/BottomsNode/SCT_ML_3/blob/main/Task%20ML%203.png)

This repository contains code for classifying images of cats and dogs using a Support Vector Machine (SVM) classifier. The features for the classification are extracted using a pre-trained VGG16 model.

## Dataset

The dataset used for this project is the [Dogs vs. Cats dataset](https://www.kaggle.com/c/dogs-vs-cats/data) from Kaggle.

## Project Structure

- `train/`: Directory containing the training images (cats and dogs).

- `test1/`: Directory containing the test images.

- `sampleSubmission.csv`: Sample submission file from Kaggle.

- `train_features.pkl`: Pickle file containing extracted features for training data.

- `test_features.pkl`: Pickle file containing extracted features for test data.

- `submission.csv`: Output file containing predictions for the test data.

- `svm_classifier.pkl`: Pickle file containing the trained SVM classifier.

## Requirements

- `Python 3.x`

- `NumPy`

- `pandas`

- `scikit-learn`

- `OpenCV`

- `Keras`

- `TensorFlow`

Install the required libraries using:

```bash

pip install numpy pandas scikit-learn opencv-python keras tensorflow

```

## Usage

  - `/`Training and Predicting

  - `/`Extract Features: Extract features from the images using the VGG16 model.

  - `/`Train SVM Classifier: Train the SVM classifier using the extracted features.

  - `/`Predict: Predict the labels for the test images.

## Predicting a Single Image

  To classify a single image, use the predict_image function included in the code.

  This function takes an image path as input and returns whether the image is of a cat or a dog.

## Acknowledgements

  This project uses the Dogs vs. Cats dataset from Kaggle. Special thanks to the Kaggle community for providing such a valuable dataset.

## License  

  This project is licensed under the MIT License.

  

### How to Use

  - Save this content in a file named `README.md` in the root directory of your project.