Papers › OneNet: A Channel-Wise 1D Convolutional U-Net
OneNet: A Channel-Wise 1D Convolutional U-Net
Sanghyun Byun, Kayvan Shah, Ayushi Gang, Christopher Apton, Jacob Song, Woo Seong Chung
Many state-of-the-art computer vision architectures leverage U-Net for its adaptability and efficient feature extraction. However, the multi-resolution convolutional design often leads to significant computational demands, limiting deployment on edge devices. We present a streamlined alternative: a 1D convolutional encoder that retains accuracy while enhancing its suitability for edge applications. Our novel encoder architecture achieves semantic segmentation through channel-wise 1D convolutions combined with pixel-unshuffle operations. By incorporating PixelShuffle, known for improving accuracy in super-resolution tasks while reducing computational load, OneNet captures spatial relationships without requiring 2D convolutions, reducing parameters by up to 47%. Additionally, we explore a fully 1D encoder-decoder that achieves a 71% reduction in size, albeit with some accuracy loss. We benchmark our approach against U-Net variants across diverse mask-generation tasks, demonstrating that it preserves accuracy effectively. Although focused on image segmentation, this architecture is adaptable to other convolutional applications. Code for the project is available at https://github.com/shbyun080/OneNet .
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Segmentation | MSD Heart | OneNete,4 | mIoU | 6.6 | #1 of 1 | Archive leaderboard | report |
| Image Segmentation | OxfordPets | OneNete,4-C | Dice Score | 0.967 | #1 of 1 | Archive leaderboard | report |
| Image Segmentation | PASCAL VOC | OneNete,4-C | mIoU | 63.6 | #1 of 3 | Archive leaderboard | report |
| Image Segmentation | PASCAL VOC | OneNeted,4 | mIoU | 14.9 | #2 of 3 | Archive leaderboard | report |
| Image Segmentation | PASCAL VOC | OneNete,4-S | mAP0.5 | 52.75 | #3 of 3 | 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
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