Hey there, fellow machine learning enthusiasts! I recently took on a self-driven project to reproduce YOLOv1 from scratch using PyTorch, with the goal of gaining a deeper understanding of object detection and research implementation. In this post, I’ll walk you through what I implemented and share my experience.
I started by implementing the YOLOv1 CNN architecture, following the original paper faithfully. I also created a custom loss function that includes localization, confidence, and classification. Additionally, I implemented IoU calculations and grid transformations, which are essential components of the YOLOv1 algorithm.
To visualize the results, I set up a forward pass and inference pipeline. I also created a modular structure and utilities to make the code more organized and reusable.
Although I haven’t trained the model yet (it’s taking a while on my GPU!), the pipeline is fully written and ready for VOC or a custom dataset. If you’re interested in checking out the code, I’ve made it available on GitHub.
This project was a great learning experience, and I hope it can inspire others to dive deeper into object detection and machine learning research.