【ML Paper】YOLO explained part1

This time, I'll explain the YOLO image detection model with paper.
This is a part1, part2 will publish soon.

Original paper: https://arxiv.org/abs/1506.02640

1. Preface

First, YOLO has many versions(probably v11 is the latest in 2024/10/17), so we'll start with the first one.

2. Introduction

YOLO ("You Only Look Once") is an object detection system that reframes the detection task as a single regression problem, directly predicting bounding box coordinates and class probabilities from image pixels. Unlike traditional methods that rely on complex pipelines involving classifiers applied at various locations and scales (such as sliding windows or region proposals), YOLO uses a single convolutional neural network to simultaneously predict multiple bounding boxes and their associated class probabilities.

Key Advantages of YOLO:

1. Speed: YOLO is extremely fast, capable of processing images at 45 frames per second with its base network and over 150 fps with a faster version. This enables real-time processing of streaming video with minimal latency.

2. Global Reasoning: By training on full images, YOLO incorporates contextual information about object classes and their appearances, reducing background errors compared to methods that only consider local regions.

3. Generalization: YOLO learns generalizable object representations, outperforming top detection methods like DPM and R-CNN when applied to new domains such as artwork. This makes it less likely to fail with unexpected inputs.

Limitations:

• Accuracy: While YOLO is fast, it lags behind state-of-the-art detection systems in terms of precision, especially in localizing small objects within images.

Despite these limitations, YOLO's unified architecture simplifies the detection pipeline and makes it easier to optimize. Its open-source training and testing code, along with pre-trained models, are available for download, facilitating further research and application development.