## 1 Introduction  
Infrared camera-traps are a non-intrusive monitoring method that can monitor wildlife in nature reserves and collect monitoring images without causing disturbance. Fundamental studies, such as rare species detection, species' distribution evaluation, animal behavior monitoring, and other vital studies [1], are supported by this method. However, traditional analytics of the monitoring images can take a lot of manual labor and time. The automatic wildlife identification method based on deep learning can save time and labor costs, reduce the monitoring cycle of wildlife, and enable timely study of the distribution and living conditions, which can promote effective protection. The method of automatic learning of represented features from data based on deep learning has effectively improved the performance of target detection [2]. Therefore, deep learning has been used by many researchers for wildlife identification. [PERSON] et al. [3] studied the identification methods of principal component analysis, linear discriminant analysis, and local binary mode histogram of wild animal images. The experiment result shows that Local Binary Patterns Histograms (LBPH) are better than Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for small training data sets. [PERSON] et al. [4] proposed a wildlife identification method based on deep learning and visual vocabulary, which used grayscale color information and different spatial aggregation methods to complete the training process, the accuracy of which reached 99.93%. By incorporating knowledge about object similarities from visual and semantic domains during the transfer process, [PERSON] et al. [5] proposed the object similarity-based knowledge transfer method, which achieved state-of-the-art detection performance using the semi-supervised learning method. [PERSON] et al. [6] used the convolutional neural network (CNN) and recurrent neural network (RNN) to recognize the collective activities of a herd of sheep. Despite a deviation in the distribution of herd activity, the collective activities of the herd were accurately identified. [PERSON] et al. [7] proposed an effective system for animal recognition and classification based on texture features, which obtained the required features from the local appearance and texture of the animals. Furthermore, they classified them by using K nearest neighbor (KNN) and support vector machine. The final accuracy of the KNN algorithm was 66.7%, which was better than Support Vector Machine (SVM). [PERSON] et al. [8] proposed a method for selecting filters in deep networks using a weak detector and a spatially weighted Fisher vector, and this method achieved superior results in fine-grained identification of birds. However, this method does not adopt an end-to-end structure and it needs to be finely adjusted for the difference of data distribution in the middle part. [PERSON] et al. [9] first used the \"you only look once\" (YOLO) model to detect wildlife areas, and proposed a two-channel convolutional neural network to identify wildlife, achieving an accuracy of more than 90%. This method needs accurate markings of the positions of the targets, which reduces the efficiency of wildlife recognition. The above studies all detect and identify wildlife under certain conditions, but there are still problems, such as unsatisfactory accuracy, high calibration cost, and high algorithm complexity. The Deep Residual Network (ResNet) was proposed by [PERSON] et al. [10]. The 152-layer neural network was successfully trained and won the championship in ILSVRC2015, the error rate of which on Top-5 (when the correct class is within the five most probable classes) was 3.57%, which was an outstanding effect. To simplify the calculation process and reduce the structure parameters, [PERSON] et al. [11] improved the structure of ResNet and proposed a new structure, ResNeXt. Thanks to its excellent performance, it is widely used in target detection, image recognition [12], and other applications [13]. [PERSON] et al.