Welcome to the Object Detection YOLO Workshop! This repository contains a simplified implementation of object detection using the YOLO (You Only Look Once) algorithm. The goal is to provide an educational resource for understanding the fundamentals of YOLO-based object detection.
- Simplified YOLO-based object detection implementation
- Custom IoU (Intersection over Union) metric for model evaluation
- Step-by-step workshop agenda for hands-on learning
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Setting Up Kaggle and Downloading Car Plate Detection Dataset
- Install Kaggle API for dataset download
- Upload Kaggle API key
- Download the car plate detection dataset from Kaggle
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Data Preprocessing and YOLO Labeling
- Load and preprocess the dataset
- Implement YOLO labeling for object detection
- Split the data into training and testing sets
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Custom IoU Metric Function
- Implementation of a custom IoU metric function
- Understanding IoU threshold and its significance
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YOLO-based Object Detection Model Architecture
- Designing the architecture of a YOLO-based object detection model
- Introduction to probability and bounding box prediction
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Train the Model
- Compilation of the model with a custom IoU metric
- Training the YOLO-based object detection model
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Test the Model
- Evaluation of the trained model on test data
- Visualization of model predictions
- Discussion of results and potential improvements
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Workshop challenge
- The challenge is to implement the
calculate_grid_for_pointfunction, which takes a point's x and y coordinates along with the grid size and returns the corresponding grid indices.
- The challenge is to implement the
- Basic knowledge of Python and deep learning concepts.
- Google Colab account for an interactive learning experience.
- Clone this repository.
- Follow the workshop agenda step by step.
- Link to Kaggle dataset
- Additional resources and references will be provided during the workshop.
This project is licensed under the MIT License - see the LICENSE file for details.