Methods › General › Neural Architecture Search › DetNAS

DetNAS

3 papers tagged archive 2025-07-28

Introduced by Yukang Chen et al. in DetNAS: Backbone Search for Object Detection

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

DetNAS is a neural architecture search algorithm for the design of better backbones for object detection. It is based on the technique of one-shot supernet, which contains all possible networks in the search space. The supernet is trained under the typical detector training schedule: ImageNet pre-training and detection fine-tuning. Then, the architecture search is performed on the trained supernet, using the detection task as the guidance. DetNAS uses evolutionary search as opposed to RL-based methods or gradient-based methods.

PaperSourceSee Code · megvii-model/DetNAS

Papers archive 2025-07-28

3 shown of 3, 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

9 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
Neural Architecture Search3
Object Detection3
object-detection3
General Classification2
Image Classification2
image-classification2
Object1
Segmentation1
Semantic Segmentation1

Usage over time archive 2025-07-28

Papers per year tagged with DetNAS: 2019 to 2020, peak 2 2 0 2019: 1 paper 2019 2020: 2 papers 2020
Papers per year the archive tags with this method, by the paper's archive date (3 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

Neural Architecture Search

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