【ML Paper】YOLOv2: part3

This time, I'll introduce the YOLOv2 with the paper by Joseph Redmon and Ali Farhadi. Let's focus and see the difference from yolov1.

This article is part 3. Part 2 is here.

Original Paper: https://arxiv.org/abs/1612.08242

3. Better

3.1 Methodology

YOLO (You Only Look Once) faces several limitations compared to contemporary detection systems, including localization errors and lower recall rates than region proposal-based methods like Fast R-CNN.

The goal with YOLOv2 is to improve recall and localization accuracy while preserving the model’s efficiency, without scaling up the network’s complexity or size.
YOLOv2 combines techniques from prior work with novel ideas to create a faster, more accurate detector.

3.2 Key Enhancements

3.2.1 Batch Normalization

Incorporating batch normalization into all convolutional layers results in faster convergence and reduces the need for additional regularization techniques.
This adjustment leads to an improvement of over 2% in mean Average Precision (mAP), allowing the model to eliminate dropout layers without causing overfitting.

3.2.2 High-Resolution Classifier

Unlike previous methods where classifiers are typically trained on low-resolution images (around 224x224), YOLOv2 first fine-tunes the classifier at a higher 448x448 resolution on the ImageNet dataset for 10 epochs,
allowing the network’s filters to adjust to high-resolution inputs.
This high-resolution classifier contributes to an additional mAP increase of nearly 4%.

3.3 Results

The changes introduced in YOLOv2 achieve improved localization accuracy and higher recall, thereby addressing YOLO’s primary limitations. This modified network maintains classification accuracy while offering significant performance gains, as summarized in Table 2.

3.4 Summary

By integrating batch normalization and adopting a high-resolution classifier, YOLOv2 becomes more accurate without the need for larger or deeper architectures, balancing speed and precision effectively for real-time applications.
These strategies allow YOLOv2 to remain a competitive option for efficient object detection in computer vision tasks.