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EfficientNet

195 papers tagged archive 2025-07-28

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

EfficientNet is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a compound coefficient. Unlike conventional practice that arbitrary scales these factors, the EfficientNet scaling method uniformly scales network width, depth, and resolution with a set of fixed scaling coefficients. For example, if we want to use 2ᴺ times more computational resources, then we can simply increase the network depth by αᴺ, width by βᴺ, and image size by γᴺ, where α, β, γ are constant coefficients determined by a small grid search on the original small model. EfficientNet uses a compound coefficient ϕ to uniformly scales network width, depth, and resolution in a principled way.

The compound scaling method is justified by the intuition that if the input image is bigger, then the network needs more layers to increase the receptive field and more channels to capture more fine-grained patterns on the bigger image.

The base EfficientNet-B0 network is based on the inverted bottleneck residual blocks of MobileNetV2, in addition to squeeze-and-excitation blocks.

EfficientNets also transfer well and achieve state-of-the-art accuracy on CIFAR-100 (91.7%), Flowers (98.8%), and 3 other transfer learning datasets, with an order of magnitude fewer parameters.

Source: EfficientNet: Rethinking Model Scaling for Convolutional...See Code · lukemelas/EfficientNet-PyTorch

Papers archive 2025-07-28

30 shown of 195, 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 224 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
Image Classification42
Transfer Learning32
image-classification28
General Classification20
Object Detection17
Semantic Segmentation17
Data Augmentation16
Classification15
Deep Learning13
Neural Architecture Search12
Diagnostic10
object-detection10
Segmentation9
Decoder6
Self-Supervised Learning6
Specificity6
model6
Anomaly Detection5
Instance Segmentation5
Multi-Task Learning5

Usage over time archive 2025-07-28

Papers per year tagged with EfficientNet: 2019 to 2025, peak 62 62 0 2019: 14 papers 2019 2020: 57 papers 2020 2021: 30 papers 2021 2022: 8 papers 2022 2023: 4 papers 2023 2024: 62 papers 2024 2025: 20 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (195 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

Image Models

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