Comments (8)
Hi, our model can output the confidence map. Please refer to the Figure. 2 for a check.
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@taohan10200 I see
From the readme.md: "The sub images are the input image, GT, prediction map,localization result, and pixel-level threshold, respectively: "
Is it the one in the middle bottom?
If i want to extract the array of the confidence levels, is it line 144 in test.py?
pred_map = torch.zeros(b, 1, h, w).cpu()
this pred_map variable?
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Is it the one in the middle bottom?
No, the middle bottom is the binary map, it actually is the last third one in the top row. -
Yes,
pred_map
can represent the confidence level.
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Hey @taohan10200 im back again.
So i have been looking at the pred_map variable, and the points variable and i found something interesting
I noticed that sometimes only 1 point is generated for a few nearby squares.
Also to check with you, can the pred_threshold be made more lenient? So that the count can be increased?
I notice this variable "mask"
pred_map = (pred_map / mask)
pred_threshold = (pred_threshold / mask)
what does the mask actually do?
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-
you can lower the threshold to get more count, but some noise would be miscounted when the threshold is too small.
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The mask is used for the inference of the high-resolution image. When the high-resolution image is cropped to some patches, there are maybe some over region, thus the mask represents the overlap region of those patches.
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If i want to allow for more count, i am ok with miscount. What is a good adjustment i can use?
Should i like multiply the threshold by a small number? like threshold * 0.5 ?
or should i do cv2.dilate on the binary map produced?
Sometimes my image is at night, so the pred_map actually has some valid values, but too small and end up being not counted.
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You can lower the threshold when transform the pred_map to a binary map.
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I tried on a few images, and these are the values i got from the pred_map
25th percentile | Median | 75th percentile | 90th percentile | Max |
---|---|---|---|---|
0.000227584787353408 | 0.00065783877 | 0.00176704095792957 | 0.0109336498193443 | 0.93879163 |
0.000194667365576606 | 0.00053325976 | 0.00165790767641738 | 0.00777001436799764 | 0.8905558 |
0.000234340786846587 | 0.00092632766 | 0.00920665194280446 | 0.0882901914417744 | 0.9853317 |
0.000168121478054672 | 0.0004524978 | 0.00122064480092376 | 0.00313314381055534 | 0.94432545 |
0.000218632791074924 | 0.00065056863 | 0.00357176392572001 | 0.0424249794334173 | 0.97327846 |
0.00041541330574546 | 0.0018048736 | 0.0203839614987373 | 0.147194217145443 | 0.99654627 |
0.000632454815786332 | 0.0024422049 | 0.0156496367417276 | 0.0634994350373745 | 0.9600536 |
0.000240619490796234 | 0.0006701163 | 0.0019299341365695 | 0.00715797664597631 | 0.8866419 |
0.00028129038400948 | 0.00078481884 | 0.00266420183470473 | 0.015467349998653 | 0.9374716 |
0.000315061377477832 | 0.0009813908 | 0.00379698618780822 | 0.0280545573681593 | 0.99406016 |
0.000528485950781032 | 0.0015583441 | 0.00757352309301496 | 0.044178881123662 | 0.9543941 |
0.000236529886024073 | 0.0006938946 | 0.00341518298955634 | 0.0258885353803635 | 0.9731425 |
0.0002100293750118 | 0.00046620745 | 0.00142796660657041 | 0.0261044861748814 | 0.9620243 |
0.000391625668271445 | 0.0014016973 | 0.00722801988013089 | 0.0547232337296009 | 0.9972284 |
0.000338588404702023 | 0.0010491939 | 0.00416168849915266 | 0.0228024385869503 | 0.95380545 |
0.00212409498635679 | 0.013215018 | 0.0577034335583448 | 0.223456771671772 | 0.9940021 |
0.000407069113862235 | 0.0013056945 | 0.00517463218420744 | 0.0229147665202617 | 0.95492464 |
0.00826335977762938 | 0.03685826 | 0.153363801538944 | 0.411073824763298 | 0.97953063 |
0.000324716442264616 | 0.0014018507 | 0.00628324795980006 | 0.0276476550847292 | 0.9497025 |
0.000359946090611629 | 0.0024385485 | 0.041068715043366 | 0.181282731890678 | 0.9545076 |
0.000177024761796929 | 0.00042649882 | 0.00100560131249949 | 0.00255148208234459 | 0.9446191 |
0.000398650074203033 | 0.0013338285 | 0.0114095346070826 | 0.0938105553388597 | 0.9411398 |
0.00054581837321166 | 0.002175008 | 0.0143224969506264 | 0.0861815460026267 | 0.90224934 |
0.000533784361323342 | 0.004247153 | 0.052973534911871 | 0.198961299657822 | 0.9726509 |
0.000430250045610592 | 0.0013449136 | 0.00916040572337806 | 0.0811791822314263 | 0.9484006 |
0.000891148651135154 | 0.0050512687 | 0.0408252645283937 | 0.126598091423512 | 0.92680895 |
0.000466320678242482 | 0.0012214757 | 0.0034151166328229 | 0.0102005018852651 | 0.8576134 |
0.000356850032403599 | 0.0013249489 | 0.00636411120649427 | 0.0430008441209793 | 0.96803534 |
0.000626671375357546 | 0.0016192745 | 0.00488035578746349 | 0.0307323243469 | 0.9404692 |
0.000227514945436269 | 0.00058530585 | 0.00149741262430325 | 0.00430293162353337 | 0.9438938 |
0.000496680644573644 | 0.0016554631 | 0.0111775402911007 | 0.0576809324324132 | 0.91685975 |
0.000175861074239947 | 0.00046023302 | 0.00112474046181887 | 0.00328343338333071 | 0.97611463 |
0.000300693951430731 | 0.0009975006 | 0.00365002348553389 | 0.0157472033053637 | 0.9390901 |
0.000155709774844581 | 0.00042323183 | 0.00113594910362735 | 0.00309471501968801 | 0.9357061 |
0.000198222358449129 | 0.00050764985 | 0.00140502038993873 | 0.00452038552612067 | 0.99700755 |
0.000579676518100314 | 0.0015869758 | 0.00525062158703804 | 0.0221172735095024 | 0.94972193 |
0.000336218829033896 | 0.0015319079 | 0.0175112709403038 | 0.1253966152668 | 0.95290637 |
0.000575630474486388 | 0.0012831714 | 0.00320056668715551 | 0.0115161083638668 | 0.9220723 |
0.000824070593807846 | 0.00418724 | 0.0336605682969093 | 0.113897684216499 | 0.93831986 |
0.00075974794162903 | 0.004304463 | 0.0319418758153915 | 0.103274673223496 | 0.96269846 |
0.000934409719775431 | 0.003661763 | 0.0269821644760668 | 0.140307494997978 | 0.98259616 |
0.000905818204046227 | 0.0036669944 | 0.0232212422415614 | 0.11286867633462 | 0.9553211 |
0.000275519159913529 | 0.0007743646 | 0.00299293280113488 | 0.0401690136641264 | 0.95771384 |
0.000341998886142392 | 0.00097436825 | 0.00302345457021147 | 0.017917924374342 | 0.9285381 |
0.000477230569231324 | 0.0015343723 | 0.00538706476800144 | 0.0265436189249158 | 0.9690722 |
0.000129914584249491 | 0.00048877863 | 0.00279881269671023 | 0.0423766769468784 | 0.8770999 |
0.000146102131111547 | 0.00042334554 | 0.00182684816536494 | 0.0193046942353249 | 0.95974356 |
0.00033833592897281 | 0.0009234102 | 0.00301122071687132 | 0.01575922742486 | 0.9986534 |
what's a good value to adjust the pred_threshold by?
I have tried adding median, multiply by 0.5, by 0.01. None of them feel very sensible.
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