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yolo v3 于2.1. Bounding Box Prediction中对xy和wh使用的loss进行了说明:
During training we use sum of squared error loss.
但在作者的实现中,对wh使用的确实是squared loss,但xy实现使用交叉熵,代码如下:
xy_loss = object_mask * box_loss_scale * K.binary_crossentropy(raw_true_xy, raw_pred[..., 0:2], from_logits=True)
所以请教作者为何这样实现? 谢谢
The text was updated successfully, but these errors were encountered:
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yolo v3 于2.1. Bounding Box Prediction中对xy和wh使用的loss进行了说明:
但在作者的实现中,对wh使用的确实是squared loss,但xy实现使用交叉熵,代码如下:
所以请教作者为何这样实现?
谢谢
The text was updated successfully, but these errors were encountered: