Most of the existing target detection algorithms rely on large?scale annotation datasets to ensure the accuracy of detection， however， it is difficult for some scenes to obtain a large number of annotation data and it consums a lot of human and material resources. In order to resolve this problem， a Few?Shot Target Detection method based on Negative Margin loss （NM?FSTD） was proposed. The negative margin loss method belonging to metric learning in Few?Shot Learning （FSL） was introduced into target detection， which could avoid mistakenly mapping the samples of the same novel classes to multiple peaks or clusters and helping to the classification of novel classes in few?shot target detection. Firstly， a large number of training samples and the target detection framework based on negative margin loss were used to train the model with good generalization performance. Then， the model was finetuned through a small number of labeled target category samples. Finally， the finetuned model was used to detect the new sample of target category. To verify the detection effect of NM?FSTD， MS COCO was used for training and evaluation. Experimental results show that the AP₅₀ of NM?FSTD reaches 22.8%； compared with Meta R?CNN （Meta Regions with CNN features） and MPSR （Multi?Scale Positive Sample Refinement）， the accuracies are improved by 3.7 and 4.9 percentage points， respectively. NM?FSTD can effectively improve the detection performance of target categories in the case of few?shot， and solve the problem of insufficient data in the field of target detection.