Data Augmentation and Visualization Pipeline

This repository provides tools for preprocessing, splitting, augmenting, and visualizing image datasets for machine learning tasks.

Features

• Dataset Splitting: Automatically splits datasets into training, validation, and test sets.
• Image Augmentation: Applies transformations like rotation, flipping, cropping, color jitter, and noise addition.
• Visualization: Generates bar charts, heatmaps, and sample image displays for dataset analysis.

File Overview

• augmentation_to_image.py: Script for augmenting images in the training set and saving the results.
• dataset_visualization.ipynb: Notebook for splitting datasets, generating visualizations, and displaying sample images.
• .gitignore: Specifies files and directories to exclude from version control.

How to Use

0. Download Dataset

Use the dataset_visualization.ipynb notebook to:

• Download dataset

1. Dataset Splitting

1. Place your dataset in the dataset/ folder, structured with subfolders for each class.
2. Run dataset_visualization.ipynb to split the dataset into train, validation, and test sets.

2. Image Augmentation

Run the augmentation_to_image.py script:

python augmentation_to_image.py

Augmented images will be saved in split_data/augmented_train.

3. Visualization

Use the dataset_visualization.ipynb notebook to:

• Generate bar charts and heatmaps for dataset distribution.
• Display random sample images from each dataset split.

4. Training

For folders containing main.py, use the script to train models and generate outputs.

• Note: Ensure the dataset paths in main.py are adjusted according to your directory structure before running.

5. Evaluation

To evaluate the results:

1. Run the load_and_test.py script in the respective folders.
2. This script loads the trained model and evaluates its performance on the test dataset.

Dependencies

Install the required Python libraries with:

pip install -r requirements.txt

Directory Structure

code_repo/
├── augmentation_to_image.py
├── dataset_visualization.ipynb
├── .gitignore
├── requirements.txt
├── split_data/
│   ├── train/
│   ├── validation/
│   ├── test/
│   ├── augmented_train/
└── dataset/
    └── Labeled Data/

Notes

• Modify directory paths in scripts as needed for your environment.
• Ensure the dataset folder is organized with subfolders for each class.

License

This project is licensed under the MIT License.