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# BBoxMaskPose Hyperparameters from Experiment J1.
# For details, see the paper: https://arxiv.org/abs/2412.01562, Tab 8. in the supplementary.
# This configuration is good for getting extra AP points when the estimates are already good.
# It is not recommended for the whole loop (as done here -- this is for the demo) but rather for
# the det-pose-sam-pose studied in Tab. 4.
detector:
det_config: 'mmpose/configs/mmdet/rtmdet/rtmdet-ins_l_8xb32-300e_coco.py'
det_checkpoint: 'https://huggingface.co/vrg-prague/BBoxMaskPose/resolve/main/rtmdet-ins-l-mask.pth'
# Detectors D and D' could be different.
det_prime_config: null
det_prime_checkpoint: null
pose_estimator:
pose_config: 'mmpose/configs/MaskPose/ViTb-multi_mask.py'
pose_checkpoint: 'https://huggingface.co/vrg-prague/BBoxMaskPose/resolve/main/MaskPose-b.pth'
sam2:
sam2_config: 'configs/samurai/sam2.1_hiera_b+.yaml' # Use SAMURAI as it has img_size 1024 (SAM-2.1 has 512)
sam2_checkpoint: 'models/SAM/sam2.1_hiera_base_plus.pt'
prompting:
batch: True
use_bbox: False
num_pos_keypoints: 4
num_pos_keypoints_if_crowd: 6
num_neg_keypoints: 0
confidence_thr: 0.5
visibility_thr: 0.5
selection_method: 'distance+confidence'
extend_bbox: False
pose_mask_consistency: False
crowd_by_max_iou: 0.5 # Determine if the instance is in the multi-body scenario. If yes, use different amount of keypoints and NO BBOX. If no, use bbox according to 'use_bbox' argument.
crop: False
exclusive_masks: True
ignore_small_bboxes: False
num_bmp_iters: 2
oks_nms_thr: 0.8 |