Open jingenyan opened 4 years ago
same problem, any updates?
一样的问题,请问你解决了吗? @jingenyan
出现了同样的问题,希望得到解答 @Tianxiaomo
same problem@Tianxiaomo
same problem
same problem
same problem, any updates?
Same problem. Also notice that the final prediction results have extremly large box width. The center x,y and height are pretty much accurate. But you see generally large loss_xy
but low loss_wh
during the training. Opposite with the inference. I tried with yolov4.pth
checkpoint, no problem at all. I've checked the visualization, dataset/dataloader and forwarding part. Seems fine. So I assume that the loss term might be the cause.
I was confused by the code in yolo_loss
. Hope someone can help. Great appreciate. @Tianxiaomo
https://github.com/AlexeyAB/darknet/blob/9db0ed96621bcc8bd4aba27b0e9662c6dc33f011/src/yolo_layer.c#L344-L675
This is the original loss term. Would definately check these code and see if there's anything we can do.
It seems that total_loss = iou_loss + classification_loss
. I also see label smoothness and some "normalizer"-things. This project uses MSELoss
but base on my personal usage of the original darknet project, the default region loss should be a CIoU
not GIoU
or MSE
. So I think the loss term in this project might have problem. Also, label smoothness, drop-block, multi-scale training are not yet available.
同样的问题,请问有解决吗 Train step_320: loss : 64961.03125,loss xy : 19.951534271240234,loss wh : 22.7925968170166,loss obj : 64887.9765625,loss cls : 30.31162452697754,loss l2 : 23576.07421875,lr : 1.6e-11
I met this problem too, have you solved it?
感觉是obj_mask的问题
learning_rate is so small
Message: 'Train step_240: loss : 35656.95703125,loss xy : 118.43473815917969,loss wh : 51.72434616088867,loss obj : 33237.66015625,loss cls : 2249.13525390625,loss l2 : 13105.2314453125,lr : 2.0735999999999997e-07'