CornerNet
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princeton-vl
when i train my data, the category count is not 80 as coco, which code should i modify?
Dear all:
when i train my data, the category count is not 80 as coco, which code should i modify?
i find three part of code using "80" :
-
models/CornerNet.py: line 72 class model(kp): def init(self, db): n = 5 dims = [256, 256, 384, 384, 384, 512] modules = [2, 2, 2, 2, 2, 4] out_dim = 80 <---------------------- here need change to my category count???
-
db/detection.py: line 8
self._configs["categories"] = 80 <---------------------- here need change to my category count??? -
config/CornerNet.json: line 45
"categories": 80 <---------------------- here need change to my category count???
@heilaw Does all of these 3 place need to be change? and any other place need change?
Did you change db/coco.py line 48
self._cat_ids = [
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13,
14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
24, 25, 27, 28, 31, 32, 33, 34, 35, 36,
37, 38, 39, 40, 41, 42, 43, 44, 46, 47,
48, 49, 50, 51, 52, 53, 54, 55, 56, 57,
58, 59, 60, 61, 62, 63, 64, 65, 67, 70,
72, 73, 74, 75, 76, 77, 78, 79, 80, 81,
82, 84, 85, 86, 87, 88, 89, 90
]
self._classes = {
ind + 1: cat_id for ind, cat_id in enumerate(self._cat_ids)
}
to your number of categories @xiaoxiongli
Did you change db/coco.py line 48 self._cat_ids = [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90 ] self._classes = { ind + 1: cat_id for ind, cat_id in enumerate(self._cat_ids) } to your number of categories @xiaoxiongli
Did you have any luck? I am having trouble during evaluation with this line:
category = self._coco_to_class_map[cat_id]
in the db/coco.py I have only 1 class, and its giving me a "key error 0"
@xiaoxiongli @tianzhi0549
when the category count is not 80 as coco，the loss Fall quickly. At last， the loss is very very low , like this
training loss at iteration 27230: 7.612511581100989e-07
training loss at iteration 27235: 1.9973826965724584e-06
training loss at iteration 27240: 6.293149453995284e-06
training loss at iteration 27245: 9.290890261581808e-07
training loss at iteration 27250: 7.603926519550441e-07
training loss at iteration 27255: 8.633248853584519e-07
training loss at iteration 27260: 7.514188951063261e-07
training loss at iteration 27265: 8.675733624841087e-07
training loss at iteration 27270: 1.0097636504724505e-06
training loss at iteration 27275: 8.036624308260798e-07
training loss at iteration 27280: 8.953446695159073e-07
training loss at iteration 27285: 7.899022875790251e-07
training loss at iteration 27290: 4.14156420447398e-06
training loss at iteration 27295: 1.6851539612616762e-06
training loss at iteration 27300: 7.977693030625232e-07
validation loss at iteration 27300: 1.594285890860192e-06
training loss at iteration 27305: 2.5046172140719136e-06
training loss at iteration 27310: 7.887338142609224e-07
training loss at iteration 27315: 9.830607723415596e-07
training loss at iteration 27320: 7.96585936768679e-07
training loss at iteration 27325: 7.648009727745375e-07
training loss at iteration 27330: 1.6255791024377686e-06
training loss at iteration 27335: 7.160156201280188e-07
training loss at iteration 27340: 1.0358436384194647e-06
training loss at iteration 27345: 7.546686333625985e-07
training loss at iteration 27350: 9.059414196599391e-07
training loss at iteration 27355: 7.671519597352017e-07
5%|#5 | 27357/500000 [17:14:46<348:08:50, 2.65s/it]shuffling indices...
training loss at iteration 27360: 2.279482941958122e-06
5%|#5 | 27363/500000 [17:14:59<296:37:52, 2.26s/it]shuffling indices...
training loss at iteration 27365: 2.0431405118870316e-06
training loss at iteration 27370: 1.0739236131485086e-06
training loss at iteration 27375: 9.094511597140809e-07
training loss at iteration 27380: 8.221845178013609e-07
training loss at iteration 27385: 8.983377028926043e-07
training loss at iteration 27390: 2.3142054033087334e-06
training loss at iteration 27395: 2.802274821078754e-06
training loss at iteration 27400: 9.592042715667048e-07
validation loss at iteration 27400: 7.173352400968724e-07
5%|#5 | 27403/500000 [17:16:38<306:36:56, 2.34s/it]shuffling indices...
training loss at iteration 27405: 1.3337682958081132e-06
training loss at iteration 27410: 1.046495299306116e-06
training loss at iteration 27415: 9.256709745386615e-07
training loss at iteration 27420: 9.490519232713268e-07
training loss at iteration 27425: 8.912809335015481e-07
training loss at iteration 27430: 1.081516074918909e-06
training loss at iteration 27435: 7.116623237379827e-07
5%|#5 | 27437/500000 [17:18:04<349:13:25, 2.66s/it]shuffling indices...
training loss at iteration 27440: 7.564152042505157e-07
training loss at iteration 27445: 9.4166585995481e-07
training loss at iteration 27450: 4.15429667555145e-06
training loss at iteration 27455: 1.51546009874437e-06
training loss at iteration 27460: 8.638742201583227e-07
training loss at iteration 27465: 1.4471661415882409e-06
do you Encounter this problem？
yes, this happens to me, also the accuracy is less than 1 percent. I don't know what is the problem yet.
Make sure your labels for your dataset is appropriately labeled. This error basically means you dont have any labeled instances with "0" in your dataset!
Could you tell me which codes need to be changed when I have only one class? Thank you.
