The net:cal calibration framework is a Python 3 library for measuring and mitigating miscalibration of uncertainty estimates, e.g., by a neural network.
Hi,
I am passing the below input to the LogisticCalibration, but it is giving the runtime error.
`confidence_scores = np.array([0.70745564, 0.71694]
matched = np.array([1, 1] # as both are boxes are matched with the ground truth's
relative_x_position = np.array([0.7543349742889405, 0.24766819924116135])
input = np.stack((confidences_scores, relative_x_position), axis=1)
lr = LogisticCalibration(detection=True, use_cuda=False) # flag 'detection=True' is mandatory for this method
lr.fit(input, matched)
calibrated = lr.transform(input)
`
Hi, I am passing the below input to the LogisticCalibration, but it is giving the runtime error.
`confidence_scores = np.array([0.70745564, 0.71694] matched = np.array([1, 1] # as both are boxes are matched with the ground truth's relative_x_position = np.array([0.7543349742889405, 0.24766819924116135]) input = np.stack((confidences_scores, relative_x_position), axis=1)
lr = LogisticCalibration(detection=True, use_cuda=False) # flag 'detection=True' is mandatory for this method lr.fit(input, matched) calibrated = lr.transform(input) `
Thanks