Methods › Computer Vision › Light-weight neural networks › FBNet

FBNet

12 papers tagged archive 2025-07-28

Introduced by Bichen Wu et al. in FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search

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

FBNet is a type of convolutional neural architectures discovered through DNAS neural architecture search. It utilises a basic type of image model block inspired by MobileNetv2 that utilises depthwise convolutions and an inverted residual structure (see components).

PaperSourceSee Code · AnnaAraslanova/FBNet

Papers archive 2025-07-28

12 shown of 12, 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 22 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
Neural Architecture Search7
Hardware Aware Neural Architecture Search4
Image Classification3
Script Generation2
Segmentation2
Semantic Segmentation2
image-classification2
Autonomous Driving1
Benchmarking1
Depth Estimation1
GPU1
Image Segmentation1
Instance Segmentation1
Meta-Learning1
Multi-Task Learning1
Object1
Object Detection1
Point Cloud Completion1
Quantization1
Scene Segmentation1

Usage over time archive 2025-07-28

Papers per year tagged with FBNet: 2018 to 2023, peak 5 5 0 2018: 1 paper 2018 2019: 1 paper 2019 2020: 2 papers 2020 2021: 5 papers 2021 2022: 2 papers 2022 2023: 1 paper 2023
Papers per year the archive tags with this method, by the paper's archive date (12 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

Light-weight neural networksConvolutional Neural Networks

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