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BiFPN

48 papers tagged archive 2025-07-28

Introduced by Mingxing Tan et al. in EfficientDet: Scalable and Efficient Object Detection

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

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 connections. It also utilizes a fast normalized fusion technique. Traditional approaches usually treat all features input to the FPN equally, even those with different resolutions. However, input features at different resolutions often have unequal contributions to the output features. Thus, the BiFPN adds an additional weight for each input feature allowing the network to learn the importance of each. All regular convolutions are also replaced with less expensive depthwise separable convolutions.

Comparing with PANet, PANet added an extra bottom-up path for information flow at the expense of more computational cost. Whereas BiFPN optimizes these cross-scale connections by removing nodes with a single input edge, adding an extra edge from the original input to output node if they are on the same level, and treating each bidirectional path as one feature network layer (repeating it several times for more high-level future fusion).

PaperSourceSee Code · zylo117/Yet-Another-EfficientDet-Pytorch

Papers archive 2025-07-28

30 shown of 48, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 71 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Object Detection31
object-detection25
Object10
Deep Learning4
Autonomous Driving3
Data Augmentation3
Instance Segmentation3
Segmentation3
Diagnostic2
Ensemble Learning2
Image Classification2
Real-Time Object Detection2
Semantic Segmentation2
Transfer Learning2
image-classification2
2D Cyclist Detection1
3D Object Detection1
Action Recognition1
Anomaly Detection1
AutoML1

Usage over time archive 2025-07-28

Papers per year tagged with BiFPN: 2019 to 2025, peak 12 12 0 2019: 1 paper 2019 2020: 6 papers 2020 2021: 8 papers 2021 2022: 11 papers 2022 2023: 8 papers 2023 2024: 12 papers 2024 2025: 2 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (48 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Feature Pyramid BlocksFeature Extractors

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