NICUface: Robust Neonatal Face Detection in Complex NICU Scenes
| Model | Backbone | Weights |
|---|---|---|
| NICUface-RF | RetinaFace | Link: |
| NICUface-Y5F | YOLO5Face (YOLOV5l) | Link: https://drive.google.com/file/d/16-OEMYuAuaNyd3vUcmlw1aCWsQHtDAYt/view?usp=sharing |
| FaceOrientation | YOLO5Face (YOLOV5l) | Link: https://drive.google.com/file/d/1IvPdph3ghr6bVJp4HEbAMJZ1ZiID4sAM/view?usp=sharing |
Two models are presented here for neonatal face orientation: NICUface-RF based on RetinaFace, and NICUface-Y5F based on YOLO5Face. We strongly recommend users to try both models for their neonatal application given that they are highly complementary in detecting diverse complex NICU scenes.
- Data preprocessing: Face orientation estimation to standardize images.
python3 face_orientation_estimation.py --image_directory
This will create a north_oriented_faces directory with the supplied images from image_directory with faces oriented North.
- Prepare your data into a new
neonatedirectory as:
data/neonate/
train/
images/
label.txt
val/
images/
label.txt
- Download YOLO5face repo
https://github.com/deepcam-cn/yolov5-face.git
- Download NICUface repo
https://github.com/GreenCUBIC/NICUface.git
-
Download NICUface-Y5F weights (presented in table above)
-
Train model
python3 train_nicuface_y5f.py --data data/neonate.yaml --cfg models/model_nicuface_y5f.yaml --weights weights/nicuface_y5f.pt --hyp data/hyp.nicuface_y5f.yaml
After training, the resulting weights are saved under runs/train/exp1/last.pt and can be used in inference as:
test_nicuface_y5f.py --runs/train/exp1/last.pt
Souley Dosso Y, Kyrollos D, Greenwood K, Harrold J, Green JR, 2022, "NICUface: Robust Neonatal Face Detection in Complex NICU Scenes," IEEE Access, 10:62893-62909, doi:10.1109/ACCESS.2022.3181167.
Link: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9791241
- Yasmina Souley Dosso yasminasouleydosso@cmail.carleton.ca
- Daniel Kyrollos DanielKyrollos@cmail.carleton.ca
- James Green jrgreen@sce.carleton.ca

