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ZFTurbo / Weighted-Boxes-Fusion

Set of methods to ensemble boxes from different object detection models, including implementation of "Weighted boxes fusion (WBF)" method.
MIT License
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Bbox is 'Nan' when weights have 0 #44

Open Yangr116 opened 2 years ago

Yangr116 commented 2 years ago

Hello, thanks for your nice work!

Recently, I used your method in my own project, and the mAP is improved indeed. However, When I check the JSON format result, most of the bounding boxes are Nan.

The below are the parameters: iou_thr=0.5, skip_box_thr=0.01, weights=[1, 0]

when weights have 0, there will be warnings: RuntimeWarning: invalid value encountered in true_divide box[4:] /= conf But there are also 0 in the benchmark you have given.

Could you give me some suggestions? Looking forward to your reply, Thanks!

ZFTurbo commented 2 years ago

The easiest solution is to remove predictions from ensemble you don't need to use (e. g. weight = 0).

Yangr116 commented 2 years ago

Yeah, that's right. Weights [1, 0] made the ensembled model improve 0.03 mAP than the single model, 0.51 to 0.54 mAP ,but weights [1, 1] only obtained 0.002 mAP. Maybe more models show the ability of the ensemble method better?

ZFTurbo commented 2 years ago

I meant you can remove second set of boxes and set weights to weights=[1], then WBF will be applied to first set of boxes only. It's the equiualent to weights=[1, 0]. Looks like you have some of boxes intersected in the predictions of same model, so they fuse in process.

Yangr116 commented 2 years ago

Oh, thanks for your reply!   I will try it. 

---Original--- From: @.> Date: Mon, Oct 11, 2021 21:51 PM To: @.>; Cc: @.**@.>; Subject: Re: [ZFTurbo/Weighted-Boxes-Fusion] Bbox is 'Nan' when weights have 0 (#44)

I meant you can remove second set of boxes and set weights to weights=[1], then WBF will be applied to first set of boxes only. It's the equiualent to weights=[1, 0]. Looks like you have some of boxes intersected in the predictions of same model, so they fuse in process.

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