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README.md
__init__.py
builder.py
input.py

README.md

NAS-FPN

This repository implements NAS-FPN in the SimpleDet framework.

Qucik Start

# train baseline retinanet following the setting of NAS-FPN
python3 detection_train.py --config config/NASFPN/retina_r50v1b_fpn_640_1@256_25epoch.py

# train NAS-FPN
python3 detection_train.py --config config/NASFPN/retina_r50v1b_nasfpn_640_7@256_25epoch.py
python3 detection_train.py --config config/NASFPN/retina_r50v1b_nasfpn_1024_7@256_25epoch.py
python3 detection_train.py --config config/NASFPN/retina_r50v1b_nasfpn_1280_7@384_25epoch.py

# train hand-crafted neck
python3 detection_train.py --config config/NASFPN/retina_r50v1b_tdbu_1280_3@384_25epoch.py

Results and Models

All AP results are reported on test-dev of the COCO dataset.

Model InputSize Backbone Neck Train Schedule GPU Image/GPU FP16 Train MEM Train Speed Box AP(Mask AP) Link
RetinaNet 640 R50v1b-FPN 1@256 25 epoch 8X 1080Ti 8 yes 6.6G 85 img/s 37.4 model
NAS-FPN 640 R50v1b-FPN 7@256 25 epoch 8X 1080Ti 8 yes 7.8G 66 img/s 40.1 model
NAS-FPN 1024 R50v1b-FPN 7@256 25 epoch 8X 1080Ti 4 yes 9.1G 17 img/s 44.2 model
NAS-FPN 1280 R50v1b-FPN 7@384 25 epoch 8X 1080Ti 2 yes 8.9G 10 img/s 45.3 model
TD-BU* 1280 R50v1b-FPN 3@384 25 epoch 8X 1080Ti 3 yes 10.5G 12 img/s 44.7 model

* Short for TopDown-BottomUp neck which is highly symmetric proposed by Zehao.

Reference

@inproceedings{ghiasi2019fpn,
  title={NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection},
  author={Ghiasi, Golnaz and Lin, Tsung-Yi and Pang, Ruoming and Le, Quoc V},
  booktitle={CVPR},
  year={2019}
}
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