Comments (5)
我在voc上训练的,loss一开始就是很大,1万多。后来我试着在yolov3上训练,也是这样的。但是在wins上,直接用darknet训练,就没有这种情况,loss一开始就几十。原因不明。
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after 4 epoch, it reaches
1838/2587 [====================>.........] - ETA: 2:37:20 - loss: 65.4863
1839/2587 [====================>.........] - ETA: 2:37:08 - loss: 65.4886
1840/2587 [====================>.........] - ETA: 2:36:56 - loss: 65.4875
1841/2587 [====================>.........] - ETA: 2:36:43 - loss: 65.4870
1842/2587 [====================>.........] - ETA: 2:36:30 - loss: 65.4876
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差不多,我一个epoch后loss能达到160多,但是这是有问题.loss正常从几十开始.可能标注数据的问题,但是我用了下面这2种 ,一样的loss巨大.
第一种:
/VOCdevkit/VOC2007/JPEGImages/000012.jpg 156,97,351,270,6
第二种:
/VOCdevkit/VOC2007/JPEGImages/000016.jpg 0.4638835 0.5425 0.927767 0.9137500000000001 6
所以很奇怪,我看别人都没有遇到这种情况.
from keras-yolo4.
差不多,我一个epoch后loss能达到160多,但是这是有问题.loss正常从几十开始.可能标注数据的问题,但是我用了下面这2种 ,一样的loss巨大.
第一种:
/VOCdevkit/VOC2007/JPEGImages/000012.jpg 156,97,351,270,6第二种:
/VOCdevkit/VOC2007/JPEGImages/000016.jpg 0.4638835 0.5425 0.927767 0.9137500000000001 6
所以很奇怪,我看别人都没有遇到这种情况.
I think yolo format bbox should be absolute pixel rather than relative scale ?
from keras-yolo4.
是的,绝对像素值.你试过keras-yolov3吗?
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