I have trained YOLOX with custom binary class.
But I don't know how to postprocess with three PAN output layers.
For 640x640, outpus_npshape is with shape [ (1, 80, 80, 4), (1, 80, 80, 1) (1, 80, 80, 80) , (1, 40, 40, 4), (1, 40, 40, 1), (1, 40, 40, 80) , (1, 20, 20, 4), (1, 20, 20, 1), (1, 20, 20, 80) ].
{
"width": 80,
"height": 80,
"channel": 1,
"channels_ordering": "ChannelOrdering.KP_CHANNEL_ORDERING_CHW",
"num_data": 6400,
"ndarray": [
"[[[[-17.03232 -10.219393 -10.838749 ... -18.271034",
" -17.342 -11.767785 ]",
" [-21.677498 -23.84525 -27.251713 ... -25.083963",
" -21.677498 -10.219393 ]",
" [-15.793606 -22.296856 -28.800106 ... -21.677498",
" -21.36782 -9.600035 ]",
" ...",
" [-12.077463 -17.651678 -19.20007 ... -21.677498",
" -20.438786 -11.458107 ]",
" [ -5.2645354 -11.767785 -6.5032496 ... -22.916214",
" -18.890392 -9.600035 ]",
" [ 2.4774284 0.30967855 5.8838925 ... -4.3355",
" -5.574214 -7.4322853 ]]]]"
]
}, {
"width": 40,
"height": 40,
"channel": 1,
"channels_ordering": "ChannelOrdering.KP_CHANNEL_ORDERING_CHW",
"num_data": 1600,
"ndarray": [
"[[[[ 7.8871355 15.774271 26.998272 ... 23.358055 22.14465",
" 18.504435 ]",
" [ 15.167568 15.167568 23.661407 ... 13.347461 -6.6737304",
" -18.201082 ]",
" [ 15.47092 7.2804327 22.751352 ... 18.504435 4.5502706",
" -14.864217 ]",
" ...",
" [ 7.8871355 -2.7301624 4.853622 ... 8.493838 1.5167569",
" 1.8201082]",
" [ 3.3368652 -16.380974 -15.774271 ... -3.0335138 -2.426811",
" -3.9435678]",
" [ 6.977082 -3.6402164 -4.246919 ... 14.257515 14.257515",
" -1.8201082]]]]"
]
}, {
"width": 20,
"height": 20,
"channel": 1,
"channels_ordering": "ChannelOrdering.KP_CHANNEL_ORDERING_CHW",
"num_data": 400,
"ndarray": [
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" -68.95711 -68.95711 -68.95711 -53.786545 -25.51413 ]",
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" -58.61354 -54.476116 -69.64668 -39.99512 -25.51413 ]",
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" -42.753407 -48.269978 -46.890835 -44.13255 -50.338688",
" -50.338688 -40.684692 -40.684692 -39.99512 -47.580406",
" -38.615982 -42.753407 -51.71783 -44.82212 -18.61842 ]",
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" -47.580406 -48.959545 -48.269978 -43.442978 -19.307991 ]",
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]
}
I think that I need to find out with IoU is the best.
Than I can choose the corresponding bounding box, which is the best inference result of my model.
But I have no idea where should I start from.