PANet Paper Walkthrough: When Feature Pyramids Go Bottom-Up

2026-07-05 · Source: Towards Data Science · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Intermediate, long

Summary

PANet, or Path Aggregation Network, enhances the Feature Pyramid Network (FPN) architecture to improve object detection, particularly for large objects. While FPN effectively enriches shallower feature maps with semantic information for small object detection, it leaves deeper feature maps deficient in spatial information. Proposed by Liu *et al.* in 2018, PANet addresses this by introducing a bottom-up path augmentation. This new pathway creates shorter routes for spatial information from shallower layers to reach deeper layers, minimizing degradation caused by long convolutional stacks. The architecture integrates 3x3 convolution layers with stride 2 for downsampling, element-wise summation for feature combination, and ReLU activation functions, ensuring all feature maps maintain 256 channels. The article provides a PyTorch implementation, demonstrating the sequential integration of a dummy CNN backbone, FPN, and PANet to generate output tensors (N2, N3, N4, N5) suitable for a detection head.

Key takeaway

For computer vision engineers optimizing object detection models, integrating PANet into your FPN-based architecture can significantly enhance performance. You should consider implementing PANet's bottom-up path augmentation to provide deeper layers with crucial spatial information, complementing FPN's semantic enrichment. This approach improves detection accuracy for both small and large objects, offering a robust feature pyramid for your detection head.

Key insights

PANet augments FPN with a bottom-up path to enrich deeper feature maps with spatial information, improving object detection accuracy.

Principles

• Bidirectional information flow improves feature representation.
• Shorter paths preserve spatial information better.
• Combine semantic and spatial features for robust detection.

Method

PANet integrates a bottom-up path augmentation after FPN, using 3x3 convolutions with stride 2 for downsampling, element-wise summation, and ReLU activations to pass spatial information from shallower to deeper layers.

In practice

• Implement PANet with a CNN and FPN backbone.
• Use 256 channels for all PANet feature maps.
• Apply ReLU after each PANet convolution.

Topics

• PANet
• Feature Pyramid Networks
• Object Detection
• Instance Segmentation
• Convolutional Neural Networks
• PyTorch Implementation

Code references

• MuhammadArdiPutra/medium_articles

Best for: Computer Vision Engineer, Machine Learning Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards Data Science.