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YOLOv1 from Scratch

This repository implements the YOLOv1 (You Only Look Once) object detection algorithm from scratch. It includes a tool for generating anchor boxes using k-means clustering on bounding box data, which is an essential step for the YOLO algorithm.

Features

• Anchor Box Generation: Uses k-means clustering to generate optimal anchor boxes from a set of bounding box dimensions.
• Interactive Interface: Built using Streamlit, allowing users to upload their data and visualize the results in real-time.
• Data Visualization: Plots the generated anchor boxes and calculates the Average Intersection over Union (IoU).

How It Works

1. Anchor Box Generation: The algorithm uses k-means clustering to generate anchor boxes based on the width and height of input bounding boxes.
2. CSV File Input: Users upload a CSV file with bounding box dimensions (width, height) for training.
3. Visualization: The results, including the generated anchor boxes and Average IoU, are visualized using Matplotlib and Seaborn.

Installation

1. Clone this repository:

   git clone https://github.com/your-username/yolov1-from-scratch.git
   cd yolov1-from-scratch
   

2. Install the required dependencies:

   pip install -r requirements.txt
   

3. Run the Streamlit app:

   streamlit run streamline.py
   

Usage

1. Upload a CSV file containing bounding box data (columns: width, height).

2. Select the number of anchor boxes you want to generate.

3. Click "Generate Anchor Boxes" to process the data.

4. View the results:

   • Anchor Boxes: List of generated anchor boxes.
   • Average IoU: The average Intersection over Union score of the generated anchor boxes.
   • Plot: A visual representation of bounding boxes and anchor boxes.

Example CSV Format

The CSV file should contain at least the following columns:

• xmin: The x-coordinate of the top-left corner of the bounding box.
• ymin: The y-coordinate of the top-left corner.
• xmax: The x-coordinate of the bottom-right corner.
• ymax: The y-coordinate of the bottom-right corner.

Note: The script will automatically calculate the width and height from the xmin, ymin, xmax, and ymax columns.

Requirements

• pandas
• numpy
• matplotlib
• seaborn
• streamlit
• pillow

License

This project is licensed under the MIT License - see the LICENSE file for details.

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