Hi,
I've developed an app which is using 4 data sets with 100 targets each ( JPG files 512 x 512 ). Each data set's file size is about 7MB. But when loaded into mobile's memory one data set is taking up to 70 - 80MB. So , when DS's are loaded at start together they use about 300MB of memory. With such scenario I'm getting 'memory limit exceed' info and app is being closed by the system. Unfortunately I cannot use Cloud Reco. App must run in offline mode. Morover I've tried to load DS's in a different way , like 1. Load 1st data set , 2. Scan 1st data set .3 When target not recognized - unload 1st data set. 4. Load another datset. But the problem is that loading datasets during one scan attempt is taking too much time. So my question is : Is there any way to decrease memory usage ? Maybe scaling down (to 256 x 256) image targets will help ? Do you have any ideas / clues ?
Thanks for attention !
Ok, thanks for the clarification... then, it is actually about the Dataset Loading time in this case (once the loading is completed, detection should occur very fast, but indeed, loading a full dataset with 100 target can take a bit long).
So, in this case, there is not much else that can be done, at least to my knowledge, except switching to Cloud Reco (which would be really well suited for cases like these, with hundreds of targets)... although, I understand that this is not an option for you, since you do not want / cannot use network connection.