The invention discloses a training method and application of a long-
tail data semi-supervised segmentation model based on double teachers. The training method comprises the following steps: enabling
labeled data input to calculate supervised loss; inputting
label-free data to obtain general identification student prediction features, professional student prediction features, general identification false labels and professional false labels; calculating a first
cross entropy loss between the general recognition student prediction feature and the professional pseudo tag, and a second
cross entropy loss between the professional student prediction feature and the general recognition pseudo tag; calculating the comparative learning loss between the general recognition teacher false
label and the general recognition student prediction features; and iteratively updating the parameters. The method is composed of different learning modules and has different pseudo
label screening strategies, so that model
coupling can be avoided; only some additional parameters are introduced, so that the data occupation is small; and furthermore, a more discriminative feature space is formed through comparative learning, so that the
utilization rate of the pseudo labels is remarkably improved while
overfitting is avoided, and finally, the model training efficiency and accuracy are improved.