label for negative!

[feixiangdekaka]

计算每一个先验框应该有的权重

    alpha_factor_for_back = keras.backend.ones_like(labels_for_back) * alpha
    alpha_factor_for_back = backend.where(keras.backend.equal(labels_for_back, 1), alpha_factor_for_back, 1 - alpha_factor_for_back)
    focal_weight_for_back = backend.where(keras.backend.equal(labels_for_back, 1), 1 - classification_for_back, classification_for_back)
    focal_weight_for_back = alpha_factor_for_back * focal_weight_for_back ** gamma

这个labels_for_back 中哪个元素是1， 负样本如何在制作标签中指定1，还是都设为0

[bubbliiiing]

负样本不是没有存在目标的先验框么，那他的labels_for_back就是0吧

[bubbliiiing]

就全都是0

[feixiangdekaka]

负样本不是没有存在目标的先验框么，那他的labels_for_back就是0吧

keras.backend.equal(labels_for_back, 1) 那这个输出全是False，然后取 alpha_factor_for_back = 1 - alpha_factor_for_back focal_weight_for_back=classification_for_back作为系数 可以这样理解吗

[bubbliiiing]

是的