Comments (3)
@twmht 这个公式是手动计算cross_entropy为后续ohem作准备的,交叉熵公式-pi*log(qi), 其中pi=1,qi为Softmax的结果,因此公式简化为log( exp(x0-xmax) + exp(x1-xmax) ) - log( exp(xi-xmax) ) =
log( exp(x0-xmax) + exp(x1-xmax) ) + xmax - xi = log_sum_exp(xi) - xi, xi为网络的输出值,x0代表背景,x1代表人脸
from pytorch_retinaface.
我看 retinaface 的 paper (https://arxiv.org/abs/1905.00641) 也沒有解釋它們怎麼算的...
from pytorch_retinaface.
對於整個算法我的理解
batch_conf:
a1_c1, a1_c2
a2_c1, a2_c2
...
an_cn, an_cn
log_sum_exp(batch_conf) 是把 batch_conf 弄成
a1_c1, + a1_c2
a2_c1 + a2_c2
...
an_cn + an_cn
這邊為了方便省略 log 的計算了...
然後去扣這個 anchor 對到的 bounding box (可能是背景),如果是背景的話,就盡可能讓網路學習把前景的 conf 學成 0
我這樣的理解對嗎?
from pytorch_retinaface.
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from pytorch_retinaface.