YOLOv1 Implementation

• This repository contains a simple implementation of the YOLOv1 (You Only Look Once) object detection algorithm, inspired by the original paper:
• "You Only Look Once: Unified, Real-Time Object Detection" (CVPR 2016) by Joseph Redmon et al.

Files

• YOLOv1 Implementation.ipynb — Jupyter Notebook with the YOLOv1 code and explanations.
• Redmon_You_Only_Look_CVPR_2016_paper.pdf — Original research paper.

Features

• Custom implementation of the YOLOv1 architecture.
• Forward pass demonstration and prediction visualization.
• Step-by-step explanation inline in notebook (if available).
• Simple and beginner-friendly code structure.

Running the Code

1. Clone this repository: zsh git clone https://github.com/Salvatore-2512/YOLOv1-Implementation.git cd YOLOv1-Implementation

2. Open the notebook: jupyter notebook "YOLOv1 Implementation.ipynb"

3. Run each cell to execute the implementation.

YOLOv1 Summary

• Treats object detection as a regression problem.
• Single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation.
• Fast and efficient: real-time object detection on images and video.

Architecture

• The original YOLOv1 architecture uses 24 convolutional layers followed by 2 fully connected layers.

References

• Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection (arXiv)