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Could you please explain what is happening in the below lines.
The final fc layer of 1470 values are split into 3 branches for class prob--scales--and boxes
But how it is used to predict the exact boxes and the dimensions are confusing me.
Could you please explain what is happening in the below lines.
The final fc layer of 1470 values are split into 3 branches for class prob--scales--and boxes
But how it is used to predict the exact boxes and the dimensions are confusing me.
class_probs = np.reshape(output[0:980],(7,7,20))
scales = np.reshape(output[980:1078],(7,7,2))
boxes = np.reshape(output[1078:],(7,7,2,4))
offset = np.transpose(np.reshape(np.array([np.arange(7)]*14),(2,7,7)),(1,2,0))
boxes[:,:,:,0] += offset
boxes[:,:,:,1] += np.transpose(offset,(1,0,2))
boxes[:,:,:,0:2] = boxes[:,:,:,0:2] / 7.0
boxes[:,:,:,2] = np.multiply(boxes[:,:,:,2],boxes[:,:,:,2])
boxes[:,:,:,3] = np.multiply(boxes[:,:,:,3],boxes[:,:,:,3])
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