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2023年6月2日 · 1. FPN 1.1. FPN简介. 特征金字塔，全称Feature Pyramid Networks ，由Tsung-Yi Lin等2017年在论文《Feature Pyramid Networks for Object Detection》中提出，它的主要目标是解决在不同尺度上进行目标检测和分割时的信息丢失和分辨率不匹配的问题。 FPN的框架可以总结为，为了在多尺度上建立高级语义特征映射（high-level ...

其实在这篇论文之前，也有人提到得出一张既具有高分辨率又具有较强语义信息的特征图进行预测，但FPN的独特之处在于，它是在以特征金字塔为基础结构上，对每一层级的特征图分别进行预测。 3.FPN网络详解. 论文中网络结构图： (1). Bottom-up pathway

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2021年7月7日 · 文章浏览阅读2.2w次，点赞19次，收藏107次。本文介绍了Feature Pyramid Networks (FPN)在目标检测中的关键作用，它通过构建特征金字塔结构，有效应对小目标检测，提升性能并减少计算成本。FPN结合了图像金字塔概念与深度学习网络，利用top-down和lateral连接，显著改善了Faster R-CNN的性能。

LLM notes, including model inference, transformer model structure, and llm framework code analysis notes. - harleyszhang/llm_note

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Segmentation models with pretrained backbones. Keras and TensorFlow Keras. - qubvel/segmentation_models

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