Macaque detection

Datasets

http://www.pri.kyoto-u.ac.jp/datasets/macaquepose/index.html

Prefix	Description	Count
PRI	Primate Research Institute, Kyoto University (Species: Japanese Macaque)	1641
ZooA	Toyama Municipal Family Park Zoo (Species: Japanese Macaque)	3784
ZooB	Itozu no Mori Zoological Park (Species: Japanese Macaque)	1312
ZooC	Inokashira Park Zoo (Species: Rhesus Macaque)	2747
ZooD	Tobu Zoo (Species: Rhesus Macaque)	2461
Other	Google Open Images Dataset (Species: Various)	1138
Total	Total	13083

To extract bounding boxes from segmentation annotations and export them to COCO format (YOLO is also supported):

python scripts/macaquepose_v1_bboxes.py --data_dir PATH/TO/macaquepose_v1 --output train.json --split train --format coco
python scripts/macaquepose_v1_bboxes.py --data_dir PATH/TO/macaquepose_v1 --output val.json --split val --format coco
python scripts/macaquepose_v1_bboxes.py --data_dir PATH/TO/macaquepose_v1 --output all.json --split all --format coco

Notes:

• --data_dir should contain annotations.csv file and images folder. pandas and PIL are required.
• If YOLO format is used, --output should be a subdirectory name.
• Train split: PRI, ZooA, ZooB, ZooC, ZooD. Val split: Other (Open Images).

Training

Training script is adapted from torchvision. Some default values are changed. We use RetinaNet with ResNet-50 FPN backbone.

python detection/train.py --data-path datasets/macaquepose_v1 -b 16 --epochs 5 --pretrained --amp