基于小样本学习的牛肉大理石花纹智能分级方法研究

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中图分类号：S24；TP391.4 文献标识码：A 文章编号：2095-5553（2025）12-0171-08

Abstract：The beef quality clasification model basedon dep learning requires a large number ofsamples to update the weights，and theacquisitionof accurately classified beef samples requires manual clasificationor morecomplex process methods，which involvesa huge workload.To solve these problems，a bef marbling grading model basedon small sample learning was proposed.According tothe national standard，a bef marble pattm grading data set was established byartificialclasificationmethod，andalightweightCNNnetworkwasdesignedforimagefeatureextraction.The pre-training of CNN was completedon the mini—ImageNetdata set.Fine-tuning technique is used to update the cross entropy loss function with support set samplesand the entropyregularization functionwith query set samples to further optimize the parameter weights in softmax clasifier. Cosine similarityis used to compare image feature vectors，and softmax is usedasclassifiertocomplete theclasification task of beef images.Theresultsshow thatthe classification effect of this proposedmodel is the best，and the highest accuracyrateof beef marble pattern recognition is 96.66% ： Under the premise of the same number of training samples，it is obviously better than other models.

Keywords：beef quality；smallsample learning；marblepattern；cosine similarity；fine-tuning technique

0 引言

牛肉的肌内脂肪是产生良好口感的重要因素[1]。（剩余14426字）