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| 2014-01-20 06:38:27 -0600 | commented question | opencv_traincascade (neg)image reader parallelization Thanks, for contact. I'll probably dig deeper into the problem. |
| 2014-01-20 05:40:46 -0600 | commented question | opencv_traincascade (neg)image reader parallelization Steven, thanks for quick answer. If there will be 'unique' images in groups, we should have the same behavior as current implementation, I mean samples should be unique. Maybe more instances of negative image reader would do the job (each one with different images)... I posted this question, because I think this is the biggest bottleneck in training. |
| 2014-01-20 04:31:14 -0600 | received badge | ● Student (source) |
| 2014-01-20 04:23:48 -0600 | asked a question | opencv_traincascade (neg)image reader parallelization Hello, using traincascade, one soon realizes that the most time consuming part is getting (later stages) negative samples. Though, the question is: can be getting negative samples parellelized? Maybe on some basic level. What issues can arise from simply dividing negative samples to N groups (N cores I wanna use) and use TBB? (I know this is not very nice solution, but it can help a lot) I think, that exact number of negative samples is not that important, if this is considered an issue (it really doesn't matter, if we have 9582 or 10239 samples instead of 10000). Thanks, Igor |