2021年7月3日 · bifpn即“双向特征金字塔网络”，是一种常用于计算机视觉任务，特别是目标检测和实例分割的神经网络架构。它扩展了特征金字塔网络（fpn），通过在金字塔级别之间引入双向连接，使信息能够在网络中同时进行自底向上和自顶向下的流动。

2019年11月20日 · First, we propose a weighted bi-directional feature pyramid network (BiFPN), which allows easy and fast multiscale feature fusion; Second, we propose a compound scaling method that uniformly scales the resolution, depth, and width for all backbone, feature network, and box/class prediction networks at the same time.

-ally contribute to the fused output feature unequally. To address this issue, we propose a simple yet highly effective weighted bi-directional feature pyramid network (BiFPN), which introduces learnable weights to learn the importance of different input features, while repeatedly app.

A BiFPN, or Weighted Bi-directional Feature Pyramid Network, is a type of feature pyramid network which allows easy and fast multi-scale feature fusion. It incorporates the multi-level feature fusion idea from FPN, PANet and NAS-FPN that enables information to flow in both the top-down and bottom-up directions, while using regular and efficient ...

In this paper, BiFPN-YOLO is proposed, featuring clear improvements over the existing range of YOLOv5 object detection models; these include replacing the traditional Path-Aggregation Network (PANet) with a higher performing Bi-Directional Feature Pyramid Network (BiFPN), requiring complex adaptation from its original implementation to function ...

First, we propose a weighted bi-directional feature pyramid network (BiFPN), which allows easy and fast multi-scale feature fusion; Second, we propose a compound scaling method that uniformly scales the resolution, depth, and width for all backbone, feature network, and box/class prediction networks at the same time.

Bidirectional Feature Pyramid Network (BiFPN): Enhances the performance of target detection by introducing a BiFPN structure into the detection head of YOLOv8. BiFPN is integrated during the process of connecting three layers of the network to form the detection head.

2023年4月13日 · 本文受EfficientNet的启发，提出了一种用于目标检测 模型 的复合尺度变换方法，不仅可以同时对分辨率、网络深度、网络宽度三个维度进行统一缩放，而且可以对目标检测模型中的骨干网络、特征网络、分类/回归预测网络中的上述三个维度进行统一缩放。 此外，本文还提出了一种新的加权双向特征金字塔网络（bi-directional feature pyramid network，BiFPN）， …

本文系统地研究了目标检测地神经网络架构设计选择，并提出了几项关键优化以提高效率。 首先，本文提出了一种加权双向特征金字塔网络（BIFPN），它允许轻松且快速地进行多尺度特征融合；其次，本文提出了一种复合缩放方法，它可以同时均匀地缩放所有背景网络/特征网络和框/类别预测网络地分辨率，深度和宽度。 基于这些优化和更好地背景网络，本文开发了一个新的目 …

2021年7月29日 · In this story, EfficientDet: Scalable and Efficient Object Detection, (EfficientDet), by Google Research, Brain Team, is reviewed. In this paper: First, a weighted bi-directional feature...