mlsanityData to 14 Oct 2026

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

arXiv 2305.17786 ↗First posted 28 May 2023cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 4 references that are in the map. First posted on arXiv 28 May 2023.

Builds on

4 of 4 references.

References · 4

  1. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  2. 2017Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learningcited by 950
  3. 2014Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognitioncited by 720
  4. 2017Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Ratescited by 199

Built on by

Not cited yet.