DeepRule
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what is "lighting" transformation
How were the hard coded values below calculated :
db> coco.py> Line> init()
self._eig_val = np.array([0.2141788, 0.01817699, 0.00341571], dtype=np.float32)
self._eig_vec = np.array([
[-0.58752847, -0.69563484, 0.41340352],
[-0.5832747, 0.00994535, -0.81221408],
[-0.56089297, 0.71832671, 0.41158938]], dtype=np.float32)
These are then used in this function : utils>image.py> lighting_
def lighting_(data_rng, image, alphastd, eigval, eigvec):
alpha = data_rng.normal(scale=alphastd, size=(3, ))
image += np.dot(eigvec, eigval * alpha)
Please explain.
Digging deeper these are values from original coco dataset. And redundant for this paper.