{ "model_version": "3.3.0", "feature_schema_version": 2, "feature_names": [ "shoulder_hip_ratio", "waist_hip_ratio", "waist_shoulder_ratio", "bust_hip_ratio", "bust_waist_ratio", "shoulder_waist_diff", "hip_shoulder_diff", "hip_waist_diff" ], "feature_units": { "shoulder_hip_ratio": "dimensionless (ratio of two widths)", "waist_hip_ratio": "dimensionless (ratio of two widths)", "waist_shoulder_ratio": "dimensionless (ratio of two widths)", "bust_hip_ratio": "dimensionless (ratio of two widths)", "bust_waist_ratio": "dimensionless (ratio of two widths)", "shoulder_waist_diff": "dimensionless (width diff / hip width)", "hip_shoulder_diff": "dimensionless (width diff / hip width)", "hip_waist_diff": "dimensionless (width diff / hip width)" }, "hip_cm_reference": 1.0, "mean": [ 0.37780097126960754, 0.8161380887031555, 2.200714111328125, 0.9687443375587463, 1.1990960836410522, -0.43833690881729126, 0.6221989393234253, 0.18386207520961761 ], "std": [ 0.05251329392194748, 0.0854557454586029, 0.3728339374065399, 0.09726245701313019, 0.17114204168319702, 0.0974234789609909, 0.05251329392194748, 0.08545612543821335 ], "raw_min": [ 0.2409999966621399, 0.611299991607666, 1.417099952697754, 0.6786999702453613, 0.8282999992370605, -0.7000498175621033, 0.45520004630088806, -0.04578861966729164 ], "raw_max": [ 0.5447999835014343, 1.045799970626831, 3.4370999336242676, 1.2482999563217163, 1.700600028038025, -0.20338687300682068, 0.7590000033378601, 0.38873669505119324 ], "normalized_min": [ -2.6050732135772705, -2.397007942199707, -2.101778030395508, -2.982079267501831, -2.1665985584259033, -2.686343193054199, -3.1801259517669678, -2.687351942062378 ], "normalized_max": [ 3.180128335952759, 2.687494993209839, 3.3161838054656982, 2.874239683151245, 2.930337429046631, 2.4116365909576416, 2.605074882507324, 2.3974246978759766 ], "class_names": [ "Hourglass", "Pear", "Apple", "Rectangle", "Inverted Triangle" ], "input": { "shape": [ 1, 8 ], "dtype": "float32", "note": "standardize raw values as (x - mean) / std BEFORE inference" }, "output": { "shape": [ 1, 5 ], "dtype": "float32", "type": "softmax" }, "note_app_side": "All 8 features are DIMENSIONLESS. Compute them from pose keypoint distances in pixels: ratios as the width ratio directly, and diffs as (widthA - widthB) / hipWidth. Then standardize with mean/std. hip_cm_reference should be ignored (it is 1.0)." }