# Real-time Object Detection: YOLOv1 Re-Implementation in PyTorch

Source: [Real-time Object Detection: YOLOv1 Re-Implementation in PyTorch](<https://www.sota2.com/research/paper/68fa6f791e209131cea45d2f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:21:27.030Z

Authors: Michael Shenoda

Published: 2023-05-28T18:17:31.000Z

## Abstract

Real-time object detection is a crucial problem to solve when in comes to computer vision systems that needs to make appropriate decision based on detection in a timely manner. I have chosen the YOLO v1 architecture to implement it using PyTorch framework, with goal to familiarize with entire object detection pipeline I attempted different techniques to modify the original architecture to improve the results. Finally, I compare the metrics of my implementation to the original.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2305.17786>)

[PDF](<https://arxiv.org/pdf/2305.17786.pdf>)