The invention provides a method and a device for training a multi-
label classification model, which can dynamically learn image features, enable a
feature extraction network to better adapt to task requirements, and are good in multi-
label classification effect. The method comprises the steps of determning n samples and a
label matrix Yc * n corresponding to the n samples in a training
data set, wherein an element yi * j in the label matrix Yc * n represents whether an ith sample contains an object indicated by a jth label or not, and c represents the number of labels related to the samples; extracting a
feature matrix Xd * n of the n samples by using a
feature extraction network;utilizing a
feature mapping network to obtain a prediction tag matrix of the
feature matrix Xd * n, wherein theprediction tag matrix is obtained by using the
feature mapping network; and obtaining a prediction tag matrix of the
feature matrix Xd * n. Elements in the DA00033902000001. TIF are shown in the specification. The invention also discloses a preparation method of the
optical glass. The
optical glass is characterized in that the
optical glass is prepared from the optical glass, the optical glass and the like; a confidence representing that the i-th sample comprises an object indicated by the j-th tag; According to the label matrix Yc * n and the prediction label matrix, obtaining a predictionlabel matrix Yc * n according to the prediction label matrix Yc * n and the prediction label matrix Yc * n, and updating the weight parameter Z and the
feature mapping matrix Mc * d, and training themulti-tag classification model.