def od_detection_mask_learner(od_detection_mask_dataset):
""" returns a mask detection learner that has been trained for one epoch. """
model = get_pretrained_maskrcnn(
num_classes=len(od_detection_mask_dataset.labels) + 1,
min_size=100,
max_size=200,
rpn_pre_nms_top_n_train=500,
rpn_pre_nms_top_n_test=250,
rpn_post_nms_top_n_train=500,
rpn_post_nms_top_n_test=250,
)
learner = DetectionLearner(od_detection_mask_dataset, model=model)
learner.fit(1)
return learner
def od_detection_keypoint_learner(tiny_od_detection_keypoint_dataset):
""" returns a keypoint detection learner that has been trained for one epoch. """
model = get_pretrained_keypointrcnn(
num_classes=len(tiny_od_detection_keypoint_dataset.labels) + 1,
num_keypoints=len(
tiny_od_detection_keypoint_dataset.keypoint_meta["labels"]),
min_size=100,
max_size=200,
rpn_pre_nms_top_n_train=500,
rpn_pre_nms_top_n_test=250,
rpn_post_nms_top_n_train=500,
rpn_post_nms_top_n_test=250,
)
learner = DetectionLearner(tiny_od_detection_keypoint_dataset, model=model)
learner.fit(1, skip_evaluation=True)
return learner