## 1 Introduction  
The zebrafish and its larvae are becoming prominent vertebrate models in biological and medical research [1; 2; 3; 4; 5; 6]. The transparency of zebrafish larvae facilitates convenient observation of the experimental process. Compared to the observations made by naked eyes in early days, the development of modern microscopy and imaging technology has facilitated the implementation of more complicated experiments by obtaining and storing massive zebrafish phenotype imaging data digitally [7].  
Early methods of phenotype image analysis mostly rely on manual work which is labour intensive and error prone. Due to the increasing amount of image data, efficient image-processing methods extracting meaningful information from a massive amount of phenotype images are increasingly important [8]. So far, automatic approaches have been proposed for classifying the stages of zebrafish embryo development [3; 9; 10] and recognition of adult fish behavior [11; 12; 13], following classical image-processing techniques like wavelet analysis and fractal geometry [14; 15; 16]. These studies openeda new era of computerized zebrafish microscopic image analysis, liberating biologists from tedious manual work and increasing the objectivity of the task.  
Comparing to the development of image-analysis methods for zebrafish adults or eggs, the studies on zebrafish larvae are relatively rare. The analysis of the larvae image remains a challenging task due to the complexity and diversity of larvae phenotypes [17; 18; 19]. The distinctions among different phenotypes are difficult to identify even for human experts. Among the limited number of existing zebrafish larvae image-analysis studies, [PERSON] et al. proposed an automatic approach for larvae defects classification from microscopic images using a supervised machine-learning approach [20]. They used dense random sub-windows to extract hand-crafted image features for phenotype defects classification. For nine out of 11 classification categories, their method reached 90% to 100% agreement with consensus voting of biologists. This method gives new insights into automatic recognition of zebrafish larvae phenotypes by replacing human labour with the computerized method.  
Even though the machine-learning method using hand-crafted features achieved comparable performance to human experts, there is still significant scope to improve the accuracy and automation of the method. The accuracy of these methods mostly depends on the quality of the extracted features. The process for feature extraction involves subjective factors and human interventions. In contrast to the classical machine-learning methods using hand-crafted features, the newly developed deep-learning (DL) methods automatically extract image features and achieved a dramatic improvement of the classification accuracy. In the last decade, DL methods, especially convolutional neural networks (CNNs), have revolutionised the field of image analysis [21; 22; 23]. Despite the success of deep learning in image processing, there are some unique difficulties of zebrafish larvae microscopic images hampering the application of DL approaches:  
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this dataset and developed a DL-based zebrafish larvae classification model that is more robust and accurate. In Section 2, the dataset annotated elaborately by the [PERSON] et al. is described. Section 3 introduces the detailed structure and method. Experimental results are reported in Section 4, with further discussions presented in Section 5.