When I run the following inference code:
images = [cv2.imread("./example/img1.png")] model.extract_molecule_bboxes_from_figures(images)
The prediction results contain a score of 2011, which seems very unusual. Here’s an example of the output:
[[{'category': '[Mol]', 'bbox': (0.03601800900450225, 0.07630863546207435, 0.28064032016008006, 0.33645171090096415), 'category_id': 1, 'score': 2011}, {'category': '[Mol]', 'bbox': (0.03751875937968985, 0.6538262629364098, 0.28114057028514255, 0.9157036255448923), 'category_id': 1, 'score': 2011}, {'category': '[Mol]', 'bbox': (0.37868934467233617, 0.08671435847962994, 0.5467733866933466, 0.19510730657916736), 'category_id': 1, 'score': 2011}, {'category': '[Mol]', 'bbox': (0.37868934467233617, 0.6642319859539654, 0.5467733866933466, 0.7726249340535027), 'category_id': 1, 'score': 2011},
Is there any specific meaning or explanation for why the prediction score is 2011?
When I run the following inference code:
images = [cv2.imread("./example/img1.png")] model.extract_molecule_bboxes_from_figures(images)The prediction results contain a score of 2011, which seems very unusual. Here’s an example of the output:
[[{'category': '[Mol]', 'bbox': (0.03601800900450225, 0.07630863546207435, 0.28064032016008006, 0.33645171090096415), 'category_id': 1, 'score': 2011}, {'category': '[Mol]', 'bbox': (0.03751875937968985, 0.6538262629364098, 0.28114057028514255, 0.9157036255448923), 'category_id': 1, 'score': 2011}, {'category': '[Mol]', 'bbox': (0.37868934467233617, 0.08671435847962994, 0.5467733866933466, 0.19510730657916736), 'category_id': 1, 'score': 2011}, {'category': '[Mol]', 'bbox': (0.37868934467233617, 0.6642319859539654, 0.5467733866933466, 0.7726249340535027), 'category_id': 1, 'score': 2011},Is there any specific meaning or explanation for why the prediction score is 2011?