Papers › ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
Adam Paszke, Abhishek Chaurasia, Sangpil Kim, Eugenio Culurciello
The ability to perform pixel-wise semantic segmentation in real-time is of paramount importance in mobile applications. Recent deep neural networks aimed at this task have the disadvantage of requiring a large number of floating point operations and have long run-times that hinder their usability. In this paper, we propose a novel deep neural network architecture named ENet (efficient neural network), created specifically for tasks requiring low latency operation. ENet is up to 18× faster, requires 75× less FLOPs, has 79× less parameters, and provides similar or better accuracy to existing models. We have tested it on CamVid, Cityscapes and SUN datasets and report on comparisons with existing state-of-the-art methods, and the trade-offs between accuracy and processing time of a network. We present performance measurements of the proposed architecture on embedded systems and suggest possible software improvements that could make ENet even faster.
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Code
Syntology Ran 5 of 30 code samples harvested from 8 repositories linked to this paper; 25 have no recorded run. Of those that ran: 4 ran · our draft was wrong; 1 ran with no contract checked.
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49 repositories listed; official and paper-mentioned ones first.
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Code Syntology ran Syntology
30 samples harvested; 5 ran; 0 honoured the contract we drafted; 25 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Real-Time Semantic Segmentation | Cityscapes test | ENet | Frame (fps) | 76.9 | #39 of 39 | Archive leaderboard | report |
| Real-Time Semantic Segmentation | Cityscapes test | ENet | Time (ms) | 13 | #39 of 39 | Archive leaderboard | report |
| Real-Time Semantic Segmentation | Cityscapes test | ENet | mIoU | 58.3% | #39 of 39 | Archive leaderboard | report |
| Semantic Segmentation | Cityscapes test | ENet | Mean IoU (class) | 58.3% | #101 of 105 | Archive leaderboard | report |
| Semantic Segmentation | ScanNetV2 | ENet | Mean IoU | 37.6% | #10 of 12 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: ENet, ENet Bottleneck, ENet Dilated Bottleneck, ENet Initial Block
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