EE‑655 Course Project: Complex YOLO

Course: EE‑655 – Computer Vision & Deep Learning

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🔍 Overview

This repository presents the implementation of a YOLO-based object detection system adapted for complex-valued input. The project is a part of the EE‑655 course curriculum and explores how complex data representations can influence model performance compared to traditional real-valued inputs.

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📁 Repository Structure

• Complex YOLO Implementation.ipynb – Core notebook implementing complex-input YOLO model.
• Implementation_Demo.ipynb – Notebook demonstrating inference results using trained models.
• Course_Project.ipynb – End-to-end pipeline integrating data preprocessing, training, and evaluation.
• LICENSE – MIT License for open-source use.
• README.md – Project description and usage guide (this file).

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🚀 Getting Started

1. Clone the Repository

git clone https://github.com/naman065/EE-655-Course-Project.git
cd EE-655-Course-Project

2. Install Dependencies

Make sure Python 3.7+ is installed. Then install the necessary libraries:

pip install torch torchvision numpy matplotlib opencv-python

Other common tools like scikit-learn or tqdm may also be required depending on notebook usage.

3. Run the Notebooks

Open JupyterLab or Jupyter Notebook and run:

• Complex YOLO Implementation.ipynb to train and evaluate the complex-valued model.
• Course_Project.ipynb to follow the complete workflow.
• Implementation_Demo.ipynb to visualize model inference.

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⚙️ Project Workflow

1. Data Preprocessing

   • Load and format dataset.
   • Generate both real and complex input representations.

2. Model Training

   • Implement YOLO architecture.
   • Train on complex-valued and real-valued input data.

3. Evaluation

   • Assess model accuracy using standard metrics (e.g., mAP, IoU).
   • Compare performance of complex vs. real input pipelines.

4. Inference & Visualization

   • Visualize detection outputs on test images.
   • Observe qualitative and quantitative differences.

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📚 References

• EE‑655 Lecture Slides & Notes
• YOLOv5 Documentation – https://github.com/ultralytics/yolov5
• Research papers on complex-valued neural networks and object detection

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Contributors

Name	Roll No
Ayush Badgujar	230259
Ashish Kumar Jha	230227
Naman Mohan Singh	230678
Prasun Shrivastav	230778

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