5
Erklärung der Yolo-Loss-Funktion
Ich versuche die Yolo v2-Verlustfunktion zu verstehen: λc o o r d∑i = 0S2∑j = 0B1o b jich j[ ( xich- x^ich)2+ ( yich−y^i)2]+λcoord∑i=0S2∑j=0B1objij[(wi−−√−w^i−−√)2+(hi−−√−h^i−−√)2]+∑i=0S2∑j=0B1objij(Ci−C^i)2+λnoobj∑i=0S2∑j=0B1noobjij(Ci−C^i)2+∑i=0S21obji∑c∈classes(pi(c)−p^i(c))2λcoord∑i=0S2∑j=0B1ijobj[(xi−x^i)2+(yi−y^i)2]+λcoord∑i=0S2∑j=0B1ijobj[(wi−w^i)2+(hi−h^i)2]+∑i=0S2∑j=0B1ijobj(Ci−C^i)2+λnoobj∑i=0S2∑j=0B1ijnoobj(Ci−C^i)2+∑i=0S21iobj∑c∈classes(pi(c)−p^i(c))2\begin{align} &\lambda_{coord} \sum_{i=0}^{S^2}\sum_{j=0}^B \mathbb{1}_{ij}^{obj}[(x_i-\hat{x}_i)^2 + (y_i-\hat{y}_i)^2 ] \\&+ \lambda_{coord} \sum_{i=0}^{S^2}\sum_{j=0}^B \mathbb{1}_{ij}^{obj}[(\sqrt{w_i}-\sqrt{\hat{w}_i})^2 +(\sqrt{h_i}-\sqrt{\hat{h}_i})^2 ]\\ &+ \sum_{i=0}^{S^2}\sum_{j=0}^B \mathbb{1}_{ij}^{obj}(C_i - \hat{C}_i)^2 + \lambda_{noobj}\sum_{i=0}^{S^2}\sum_{j=0}^B \mathbb{1}_{ij}^{noobj}(C_i - \hat{C}_i)^2 \\ &+ \sum_{i=0}^{S^2} \mathbb{1}_{i}^{obj}\sum_{c …