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@shinjayne
Thanks a lot for your implementation and contribution!
First of all, I like the way you write program. I spent couple days to read your programs. However, there are some questions that I could not understand after deliberation.
Here is the question
line 55-56 in default_box_control.py, I print all information of scale, w, and h. however, what's the really meaning of default_w and default_h, according to line 57,58, where c_x, and c_y is normalized between 0 and 1. Is it possible that w and h will be normalized with respect to width and height of the original image?
default_w = scale * np.sqrt(rs[i])
default_h = scale / np.sqrt(rs[i])
line 35-38 in svt_data_loader.py, why h is divided by 300 instead of the height and width of the original image?
h = float(rectangle.get('height')) / 300.0
w = float(rectangle.get('width')) / 300.0
x = float(rectangle.get('x')) / 300.0
y = float(rectangle.get('y')) / 300.0
BTW, how many epochs will lead to converge based on the given training data?
Looking forward to Your reply. Thanks a lot.
The text was updated successfully, but these errors were encountered:
@shinjayne
Thanks a lot for your implementation and contribution!
First of all, I like the way you write program. I spent couple days to read your programs. However, there are some questions that I could not understand after deliberation.
Here is the question
default_w = scale * np.sqrt(rs[i])
default_h = scale / np.sqrt(rs[i])
scale: 0.10, box_ratio: 1.00, w: 0.10 h: 0.10
scale: 0.10, box_ratio: 2.00, w: 0.14 h: 0.07
scale: 0.10, box_ratio: 3.00, w: 0.17 h: 0.06
scale: 0.10, box_ratio: 5.00, w: 0.22 h: 0.04
scale: 0.10, box_ratio: 7.00, w: 0.26 h: 0.04
scale: 0.10, box_ratio: 10.00, w: 0.32 h: 0.03
scale: 0.27, box_ratio: 1.00, w: 0.27 h: 0.27
scale: 0.27, box_ratio: 2.00, w: 0.38 h: 0.19
scale: 0.27, box_ratio: 3.00, w: 0.47 h: 0.16
scale: 0.27, box_ratio: 5.00, w: 0.60 h: 0.12
scale: 0.27, box_ratio: 7.00, w: 0.71 h: 0.10
scale: 0.27, box_ratio: 10.00, w: 0.85 h: 0.09
scale: 0.44, box_ratio: 1.00, w: 0.44 h: 0.44
scale: 0.44, box_ratio: 2.00, w: 0.62 h: 0.31
scale: 0.44, box_ratio: 3.00, w: 0.76 h: 0.25
scale: 0.44, box_ratio: 5.00, w: 0.98 h: 0.20
scale: 0.44, box_ratio: 7.00, w: 1.16 h: 0.17
scale: 0.44, box_ratio: 10.00, w: 1.39 h: 0.14
scale: 0.61, box_ratio: 1.00, w: 0.61 h: 0.61
scale: 0.61, box_ratio: 2.00, w: 0.86 h: 0.43
scale: 0.61, box_ratio: 3.00, w: 1.06 h: 0.35
scale: 0.61, box_ratio: 5.00, w: 1.36 h: 0.27
scale: 0.61, box_ratio: 7.00, w: 1.61 h: 0.23
scale: 0.61, box_ratio: 10.00, w: 1.93 h: 0.19
scale: 0.78, box_ratio: 1.00, w: 0.78 h: 0.78
scale: 0.78, box_ratio: 2.00, w: 1.10 h: 0.55
scale: 0.78, box_ratio: 3.00, w: 1.35 h: 0.45
scale: 0.78, box_ratio: 5.00, w: 1.74 h: 0.35
scale: 0.78, box_ratio: 7.00, w: 2.06 h: 0.29
scale: 0.78, box_ratio: 10.00, w: 2.47 h: 0.25
scale: 0.95, box_ratio: 1.00, w: 0.95 h: 0.95
scale: 0.95, box_ratio: 2.00, w: 1.34 h: 0.67
scale: 0.95, box_ratio: 3.00, w: 1.65 h: 0.55
scale: 0.95, box_ratio: 5.00, w: 2.12 h: 0.42
scale: 0.95, box_ratio: 7.00, w: 2.51 h: 0.36
scale: 0.95, box_ratio: 10.00, w: 3.00 h: 0.30
h = float(rectangle.get('height')) / 300.0
w = float(rectangle.get('width')) / 300.0
x = float(rectangle.get('x')) / 300.0
y = float(rectangle.get('y')) / 300.0
BTW, how many epochs will lead to converge based on the given training data?
Looking forward to Your reply. Thanks a lot.
The text was updated successfully, but these errors were encountered: