  Based on the transfer learning, we design a bird species identification model
that uses the VGG-16 model (pretrained on ImageNet) for feature extraction,
then a classifier consisting of two fully-connected hidden layers and a Softmax
layer is attached. We compare the performance of the proposed model with the
original VGG16 model. The results show that the former has higher train
efficiency, but lower mean average precisions(MAP). To improve the MAP of the
proposed model, we investigate the result fusion mode to form multi-channel
identification model, the best MAP reaches 0.9998. The number of model
parameters is 13110, which is only 0.0082% of the VGG16 model. Also, the size
demand of sample is decreased.
