AlibabaResearch / efficientteacher

A Supervised and Semi-Supervised Object Detection Library for YOLO Series
GNU General Public License v3.0
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教师模型精度还可以,学生模型精度一直很低,问题出在哪里 #73

Open M-Chase opened 1 year ago

M-Chase commented 1 year ago

如题,在自己数据集上训练的,这种情况是需要修改教师Loss的占比权重吗?

halalo513 commented 1 year ago

try teacher_loss_weights=1.0?

Meoooww commented 1 year ago

大佬跑通了吗,可以加一下联系方式吗?想有偿咨询一下我为啥没跑通

jaideep11061982 commented 1 year ago

@Meoooww Under the custom project , we have optional Supervised training step, is it mandatory , how much data is needed in that step ? Last step is Directly starting of SSOD training, is it the 4th step in series of step ?what is the problem i idnt understand exactly. Thanks in advance

Meoooww commented 12 months ago

@Meoooww Under the custom project , we have optional Supervised training step, is it mandatory , how much data is needed in that step ? Last step is Directly starting of SSOD training, is it the 4th step in series of step ?what is the problem i idnt understand exactly. Thanks in advance

Sorry for the late reply. my question is how to adjust the parameters in yolov5l_custon_ssod.yaml: I used this model on my own dataset, and the AP of fully supervised Yolov5L is around 80, with slight overfitting. After semi-supervised on datasets of the same distribution, there is a serious overfitting problem. The rendered pseudo-label is not quite as expected (a large number of wrong labels and missing labels)

Here are a few parameters I considered, if there are errors or omissions, please point them out: nms_conf_thres, nms_iou_thres: to remove redundant prediction box ignore_thres_low, ignore_thres_high: Corresponds to τ1, τ2 in the paper, which is used to determine whether the pseudo label is reliable How do you regulate ignore_thres_low here, ignore_thres_high? My experiments have not yielded the desired results, it troubles me for a long time

AlexandreDiPiazza commented 10 months ago

Hi @Meoooww ,

I am having similar issues on some custom datasets - did you fix your problem by changing/tuning the parameters your mentioned:

Thank you :)

Meoooww commented 10 months ago

Hi @Meoooww ,

I am having similar issues on some custom datasets - did you fix your problem by changing/tuning the parameters your mentioned:

  • nms_conf_thres, nms_iou_thres
  • ignore_thres_low, ignore_thres_high

Thank you :)

Hi The paper did not mention how to adjust the four parameters, so when I failed to reproduce, I guessed that these four parameters needed to be adjusted. But, I didn't succeed, I don't know what principles to follow to adjust the four parameters. If you find a suitable tuning idea, please let me know as well

(Blindly adjusting parameters has taken me too much time, and I have temporarily abandoned this project)