NAS-FCOS consists of two sub networks, an FPN $f$ and a set of prediction heads $h$ which have shared structures. One notable difference with other FPN-based one-stage detectors is that our heads have partially shared weights. Only the last several layers of the predictions heads (marked as yellow) are tied by their weights. The number of layers to share is decided automatically by the search algorithm. Note that both FPN and head are in our actual search space; and have more layers than shown in this figure.
Source: NAS-FCOS: Fast Neural Architecture Search for Object DetectionPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Object Detection | 2 | 50.00% |
Instance Segmentation | 1 | 25.00% |
Semantic Segmentation | 1 | 25.00% |