Detecting multiple classes about epnet HOT 7 CLOSED

@KevinTang29 Maybe Focal loss is an available method to solve the imbalance between the background class and other classes in training. Please have a try, and use "SigmoidFocalLoss" instead of "CrossEntropy" for multi-classes in the LI_Fusion_with_attention_use_ce_loss.yaml. Good luck to you!

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@happinesslz Thank you very much for your advice. It seems that the original "SigmoidFocalLoss" can only be applied in binary classification, and I tried to change it to focal loss under multiple classification and trained with the focal loss. However, the problem still exists. Maybe the parameter alpha and gamma need to be tuned under multiple classification? In addition, I tried to increase the cross entropy weights of the object classes

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Did you make it work for multiclass-Epnet?

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Did you make it work for multiclass-Epnet?

I just trained the model with CE loss with class weights. I set the model to detect four classes ['Car', 'Pedestrian', 'Cyclist', 'Truck'] and set the corresponding weights to be [1.0, 3.0, 6.0, 6.0, 12.0] (the first weight is of background class). The evaluation results seemed acceptable, though not good enough. Maybe the weights and other parameters need to be tuned further.

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I have finished , can you show your mail to me ?and we can talk deeper ------------------ 原始邮件 ------------------ 发件人: "happinesslz/EPNet" <[email protected]>; 发送时间: 2021年1月28日(星期四) 下午4:03 收件人: "happinesslz/EPNet"<[email protected]>; 抄送: "何必歇斯底里"<[email protected]>;"Comment"<[email protected]>; 主题: Re: [happinesslz/EPNet] Detecting multiple classes (#8) Did you make it work for multiclass-Epnet? I just trained the model with CE loss with class weights. I set the model to detect four classes ['Car', 'Pedestrian', 'Cyclist', 'Truck'] and set the corresponding weights to be [1.0, 3.0, 6.0, 6.0, 12.0] (the first weight is of background class). The evaluation results seemed acceptable, though not good enough. Maybe the weights and other parameters need to be tuned further. — You are receiving this because you commented. Reply to this email directly, view it on GitHub, or unsubscribe.

I would like to know how you did. My Email is [email protected]

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I have finished , can you show your mail to me ?and we can talk deeper ------------------ 原始邮件 ------------------ 发件人: "happinesslz/EPNet" <[email protected]>; 发送时间: 2021年1月28日(星期四) 下午4:03 收件人: "happinesslz/EPNet"<[email protected]>; 抄送: "何必歇斯底里"<[email protected]>;"Comment"<[email protected]>; 主题: Re: [happinesslz/EPNet] Detecting multiple classes (#8) Did you make it work for multiclass-Epnet? I just trained the model with CE loss with class weights. I set the model to detect four classes ['Car', 'Pedestrian', 'Cyclist', 'Truck'] and set the corresponding weights to be [1.0, 3.0, 6.0, 6.0, 12.0] (the first weight is of background class). The evaluation results seemed acceptable, though not good enough. Maybe the weights and other parameters need to be tuned further. — You are receiving this because you commented. Reply to this email directly, view it on GitHub, or unsubscribe.

Hello @Benedict0819 , I also want to achevice the multi-clas on EPNet, could you please share your code about multi-class to me?
My email address is [email protected]
Thank you very much!

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