A computer-implemented annotation method, applied to a computer comprising a processor and a memory, the annotation method comprising: determining (S11), by the processor, based on first tag information of a first image set, a first probability value that a first sample image is annotated with an Nth tag when the first sample image is annotated with an Mth tag, wherein M and N are unequal and are positive integers; characterized in that the annotation method further comprises: adding (S12), by the processor, the first probability value to second tag information of a second sample image, annotated with the Mth tag, in a second image set, to annotate the Nth tag in the second sample image by using the first probability value; wherein determining (S11), by the processor, based on the first tag information of the first image set, the first probability value that the first sample image is annotated with the Nth tag when the first sample image is annotated with the Mth tag comprises: acquiring (S51), by the processor, based on the first tag information of the first sample images in the first image set, the first sample images annotated with the Mth tag and the number of the first sample images annotated with the Mth tag as a first number; acquiring (S52), by the processor, from the first sample images annotated with the Mth tag, the number of the first sample images annotated with the Nth tag as a second number; and determining (S53) the first probability value based on a ratio of the second number to the first number, wherein the second image set is used as a training sample to train a face recognition model. adding (S12), by the processor, the first probability value to second tag information of a second sample image, annotated with the Mth tag, in a second image set, to annotate the Nth tag in the second sample image by using the first probability value; wherein determining (S11), by the processor, based on the first tag information of the first image set, the first probability value that the first sample image is annotated with the Nth tag when the first sample image is annotated with the Mth tag comprises: acquiring (S51), by the processor, based on the first tag information of the first sample images in the first image set, the first sample images annotated with the Mth tag and the number of the first sample images annotated with the Mth tag as a first number; acquiring (S52), by the processor, from the first sample images annotated with the Mth tag, the number of the first sample images annotated with the Nth tag as a second number; and determining (S53) the first probability value based on a ratio of the second number to the first number, wherein the second image set is used as a training sample to train a face recognition model. acquiring (S51), by the processor, based on the first tag information of the first sample images in the first image set, the first sample images annotated with the Mth tag and the number of the first sample images annotated with the Mth tag as a first number; acquiring (S52), by the processor, from the first sample images annotated with the Mth tag, the number of the first sample images annotated with the Nth tag as a second number; and determining (S53) the first probability value based on a ratio of the second number to the first number, wherein the second image set is used as a training sample to train a face recognition model. <EOS>