37 lines
2.9 KiB
Markdown
37 lines
2.9 KiB
Markdown
# NAS-FPN
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> [NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection](https://arxiv.org/abs/1904.07392)
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<!-- [ALGORITHM] -->
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## Abstract
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Current state-of-the-art convolutional architectures for object detection are manually designed. Here we aim to learn a better architecture of feature pyramid network for object detection. We adopt Neural Architecture Search and discover a new feature pyramid architecture in a novel scalable search space covering all cross-scale connections. The discovered architecture, named NAS-FPN, consists of a combination of top-down and bottom-up connections to fuse features across scales. NAS-FPN, combined with various backbone models in the RetinaNet framework, achieves better accuracy and latency tradeoff compared to state-of-the-art object detection models. NAS-FPN improves mobile detection accuracy by 2 AP compared to state-of-the-art SSDLite with MobileNetV2 model in [32] and achieves 48.3 AP which surpasses Mask R-CNN [10] detection accuracy with less computation time.
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<div align=center>
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<img src="https://user-images.githubusercontent.com/40661020/143968037-cedd76e9-1ae7-4869-bd34-c9d8611d630c.png"/>
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</div>
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## Results and Models
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We benchmark the new training schedule (crop training, large batch, unfrozen BN, 50 epochs) introduced in NAS-FPN. RetinaNet is used in the paper.
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| Backbone | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download |
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|:-----------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:|
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| R-50-FPN | 50e | 12.9 | 22.9 | 37.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_fpn_crop640_50e_coco/retinanet_r50_fpn_crop640_50e_coco-9b953d76.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_fpn_crop640_50e_coco/retinanet_r50_fpn_crop640_50e_coco_20200529_095329.log.json) |
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| R-50-NASFPN | 50e | 13.2 | 23.0 | 40.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco/retinanet_r50_nasfpn_crop640_50e_coco-0ad1f644.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco/retinanet_r50_nasfpn_crop640_50e_coco_20200528_230008.log.json) |
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**Note**: We find that it is unstable to train NAS-FPN and there is a small chance that results can be 3% mAP lower.
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## Citation
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```latex
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@inproceedings{ghiasi2019fpn,
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title={Nas-fpn: Learning scalable feature pyramid architecture for object detection},
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author={Ghiasi, Golnaz and Lin, Tsung-Yi and Le, Quoc V},
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booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
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pages={7036--7045},
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year={2019}
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}
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```
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