Quenstion about total detection procedure about ctw-baseline

[yuantailing]

Q1: Yes.

2: Whether to use 1000 most frequent categories is up to you. Maybe using all 3850 categories will perform better? 😄

3: Whether to use thresh > 0.005 and whether to divide into 12 splits are up to you.

Q2: Yes. (If you are wondering center-x center-y ---- because darknet YOLOv2 did this.)

4: You don't have ground truth of testset. If you test on testset, you cannot run python3 detection_perf.py, but you can upload the results to evaluation server.

[wjp0408]

Thanks for your reply ! ：）
I just use val set as test set, train+val set as train set.
And when I run cd ../judge && python3 detection_perf.py, I got this ...

...... and finally an ERROR:

This really confused me ......

[yuantailing]

fixed in f9c70fc .

[wjp0408]

Thanks for your code again. ：）
And Can you tell me how long( or how many max_batches) and the number of gpus did you train yolov2 with CTW in origin paper ? If that's okay with you...
Thanks.

[yuantailing]

NVIDIA GTX TITAN X (PASCAL) * 1, 3.0 sec/step, 38 hours in total.

[wjp0408]

@yuantailing Hi,
I'm confused by this two passages in Appendix of tutorial Part 3:

Q1: How to choose c0 ? Why sometimes nums(TPs) + nums(FNs) > nums(GTs) ? （Why nums(GT matched with detected box) + nums(GT unmatched with detected box) != nums(GTs)? )

Q2: How to compute AP? Does it means when c0 is given, all boxes with score < c0 will be filtered out, then many recall 0, recall 1, ..., recall n are given, and the AP is the mean value of max precisions under each recall ?
Like this :

[yuantailing]

Q1. Sorry, I made a mistake. It should be ''we take a minimum confidence score $c_0$ which leads to $num(TPs) + num(FPs) \leq num(GTs)$''. The paper is correct (in section 4.2):

To compute the recall rates, for each image in the testing set, denoting the number of annotated character instances as n, we select n recognized character instances with the highest confidences as output of YOLOv2.

The mistake is fixed in ff97954.

Q2. Yes, and I think it's the equivalent to the AP in PASCAL VOC. For every real number c0, we can compute a recall (The `recall' is not recall metric mentioned in the paper) and a precision. So, there are (M + 1) kinds of c0 levels to compute (M + 1) recalls and (M + 1) precisions.

We use max precisions where (r' > r) to compute AP, it's also the same.

ctw-baseline/cppapi/eval_tools.hpp

Lines 145 to 146 in ff97954

	for (int i = (int)acc.size() - 1; i > 0; i--)
	acc[i - 1] = std::max(acc[i - 1], acc[i]);

[wjp0408]

Thanks for your patience and quick reply. ：）