Methods › Computer Vision › Semantic Segmentation Models › ENet

ENet

24 papers tagged archive 2025-07-28

Introduced by Adam Paszke et al. in ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation

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

ENet is a semantic segmentation architecture which utilises a compact encoder-decoder architecture. Some design choices include:

  1. Using the SegNet approach to downsampling y saving indices of elements chosen in max pooling layers, and using them to produce sparse upsampled maps in the decoder.
  2. Early downsampling to optimize the early stages of the network and reduce the cost of processing large input frames. The first two blocks of ENet heavily reduce the input size, and use only a small set of feature maps.
  3. Using PReLUs as an activation function
  4. Using dilated convolutions
  5. Using Spatial Dropout
PaperSourceSee Code · iArunava/ENet-Real-Time-Semantic-Segmentation

Papers archive 2025-07-28

24 shown of 24, 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 38 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
Semantic Segmentation13
Segmentation10
Autonomous Driving5
Autonomous Vehicles2
Decoder2
Deep Learning2
Efficient Neural Network2
Image Segmentation2
Instance Segmentation2
Medical Image Analysis2
Quantization2
Real-Time Semantic Segmentation2
Scene Understanding2
regression2
Cardiac Segmentation1
Data Augmentation1
Decision Making1
Depth Estimation1
Diagnostic1
Disaster Response1

Usage over time archive 2025-07-28

Papers per year tagged with ENet: 2016 to 2024, peak 5 5 0 2016: 1 paper 2016 2017: 3 papers 2017 2018: 5 papers 2018 2019: 4 papers 2019 2020: 4 papers 2020 2021: 2 papers 2021 2022: 2 papers 2022 2023: 2 papers 2023 2024: 1 paper 2024
Papers per year the archive tags with this method, by the paper's archive date (24 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

Semantic Segmentation Models

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