Papers › Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
Chenxi Liu, Liang-Chieh Chen, Florian Schroff, Hartwig Adam, Wei Hua, Alan Yuille, Li Fei-Fei
Recently, Neural Architecture Search (NAS) has successfully identified neural network architectures that exceed human designed ones on large-scale image classification. In this paper, we study NAS for semantic image segmentation. Existing works often focus on searching the repeatable cell structure, while hand-designing the outer network structure that controls the spatial resolution changes. This choice simplifies the search space, but becomes increasingly problematic for dense image prediction which exhibits a lot more network level architectural variations. Therefore, we propose to search the network level structure in addition to the cell level structure, which forms a hierarchical architecture search space. We present a network level search space that includes many popular designs, and develop a formulation that allows efficient gradient-based architecture search (3 P100 GPU days on Cityscapes images). We demonstrate the effectiveness of the proposed method on the challenging Cityscapes, PASCAL VOC 2012, and ADE20K datasets. Auto-DeepLab, our architecture searched specifically for semantic image segmentation, attains state-of-the-art performance without any ImageNet pretraining.
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Code
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Code Syntology ran Syntology
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Tasks
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semantic Segmentation | ADE20K | Auto-DeepLab-L | Validation mIoU | 43.98 | #205 of 235 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K val | Auto-DeepLab-L | Pixel Accuracy | 81.72 | #87 of 95 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K val | Auto-DeepLab-L | mIoU | 43.98 | #87 of 95 | Archive leaderboard | report |
| Semantic Segmentation | Cityscapes test | Auto-DeepLab-L | Mean IoU (class) | 82.1% | #31 of 105 | Archive leaderboard | report |
| Semantic Segmentation | Cityscapes val | Auto-DeepLab-L | mIoU | 80.33% | #52 of 99 | Archive leaderboard | report |
| Semantic Segmentation | PASCAL VOC 2012 test | Auto-DeepLab-L | Mean IoU | 85.6% | #9 of 51 | Archive leaderboard | report |
| Semantic Segmentation | PASCAL VOC 2012 val | Auto-DeepLab-L | mIoU | 82.04% | #7 of 29 | 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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