The invention discloses a
deep learning model
evaluation system and method oriented to RD time-
frequency data. The process comprises
verification set expansion, multi-IOU threshold value F1 calculation, multi-IOU threshold value mAP calculation,
FLOPs calculation and evaluation index integration calculation. The main body of the evaluation method is a
verification set expansion method and an integration strategy-based model evaluation method, a
verification set is expanded through an
image fusion and inspection mechanism, and the problem of RD-oriented time-
frequency data shortage is solved; a larger-scale verification set can be obtained under the conditions that obvious
noise information is not introduced, target
label information is not leaked and data distribution is not changed, so that the verification set better represents the characteristics of overall data, and the generalization performance of the model is better evaluated; the overall capability of the
deep learning model is represented by the F1
score of the multiple IOU threshold values, the mAP of the multiple IOU threshold values and the
weighted score of the
FLOPs, and the capabilities of the model in the aspects of single-point optimization, global average optimization and time performance can be represented more accurately, so a powerful
technical support is provided for the evaluation of the
deep learning model facing RD time-
frequency data.