Few-shot Image Classification based on LMRNet

Few-shot image classification aims at recognizing image categories with only a few labeled examples. The metric-based model is commonly used in few-shot learning. But restricted by needing a large amount of memory in training process, existing highly efficient backbone network cannot be used and light weight Residual Network performs not well. So we construct a new light weight network based on the idea of multi-scale analyzation as the feature extractor. We test it on several public datasets and it can run effectively under existing public equipment and provides better efficiency compared with ResNet with the same number of layers.