# Instance Segmentation of Shrimp Based on Contrastive Learning
## 5 Conclusions  
In this paper, we propose a novel approach to integrate state-of-the-art unsupervised learning with shrimp farming to reduce the reliance on labeled data in the instance segmentation of shrimp. We first build a shrimp dataset to facilitate the application of computer vision techniques in shrimp farming. This dataset contains two sub-datasets, where the first sub-dataset has about 10 k shrimp images without any label information, and can be used for training unsupervised learning models. The other one has 1 k shrimp images with high-quality label information. We then train five state-of-the-art contrastive learning models, including MoCov1, MoCov2, MoCov3, SimCLR, and Byol, and take them as a pre-trained backbone for fine-tuning the Mask R-CNN model. The experimental results show that compared with directly fine-tuning the full Mask R-CNN model, using a contrastive learning model as the backbone and then fine-tuning the rest of the modules in the Mask R-CNN model results in better performance, even though the contrastive learning model is not pre-trained on the shrimp dataset in advance. Furthermore, with the increase in data scale, the performance in terms of fine-tuning the Mask R-CNN model with a contrastive learning model as the backbone surpasses the common fine-tuning method with an increasing margin. In the comparisons of five contrastive learning models, MoCov2 outperforms other models in the instance segmentation task. These experimental results show the effectiveness of incorporating unsupervised learning with instance segmentation, which can lead to significant cost savings in shrimp farming. The approach proposed in this paper can also be extended to other fields of application with minimal modifications. Conceptualization, H.Z. and S.C.K. ; methodology, H.Z. ; data collection, S.H.K. and S.W.K. ; supervision, S.C.K. and H.K. ; writing, H.Z. and S.C.K. ; visualization, H.Z. ; funding acquisition, S.C.K., C.W.K. and H.K. All authors have read and agreed to the published version of the manuscript. This research was supported by Korea Institute of Marine Science & Technology Promotion (KIMST) funded by the Ministry of Oceans and Fisheries (20210460). Code: [[https://github.com/heng94/ShrimpInstanceSegmentation.git](https://github.com/heng94/ShrimpInstanceSegmentation.git)]([https://github.com/heng94/ShrimpInstanceSegmentation.git](https://github.com/heng94/ShrimpInstanceSegmentation.git)). We express our greatest appreciation to [PERSON] and [PERSON] for their help in labeling the data. The authors declare no conflict of interest. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **20\%** & **40\%** & **60\%** & **80\%** & **100\%** \\ \hline \multirow{2}{*}{Super.(pre-trained)} & **AP\({}^{\text{hb}}\)** & 0.019 & 0.237 & 0.333 & 0.308 & 0.377 \\ \cline{2-7}  & **AP\({}^{\text{mk}}\)** & 0.008 & 0.131 & 0.213 & 0.188 & 0.209 \\ \hline \multirow{2}{*}{MoCov1 [8]} & **AP\({}^{\text{hb}}\)** & 0.067 & 0.255 & 0.415 & 0.453 & 0.564 \\ \cline{2-7}  & **AP\({}^{\text{mk}}\)** & 0.025 & 0.099 & 0.183 & 0.234 & 0.285 \\ \hline \multirow{2}{*}{MoCov2 [9]} & **AP\({}^{\text{hb}}\)** & 0.054 & **0.379** & **0.514** & **0.576** & **0.662** \\ \cline{2-7}  & **AP\({}^{\text{mk}}\)** & **0.027** & **0.144** & **0.252** & **0.302** & **0.376** \\ \hline \multirow{2}{*}{MoCov3 [10]} & **AP\({}^{\text{hb}}\)** & 0.051 & 0.299 & 0.459 & 0.535 & 0.619 \\ \cline{2-7}  & **AP\({}^{\text{mk}}\)** & 0.015 & 0.08 & 0.191 & 0.243 & 0.308 \\ \hline \multirow{2}{*}{SimCLR [11]} & **AP\({}^{\text{hb}}\)** & **0.068** & 0.31 & 0.46 & 0.532 & 0.613 \\ \cline{2-7}  & **AP\({}^{\text{mk}}\)** & 0.018 & 0.095 & 0.191 & 0.251 & 0.331 \\ \hline \multirow{2}{*}{Byol [12]} & **AP\({}^{\text{hb}}\)** & 0.018 & 0.289 & 0.45 & 0.529 & 0.615 \\ \cline{2-7}  & **AP\({}^{\text{mk}}\)** & 0.023 & 0.087 & 0.178 & 0.256 & 0.312 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Instance segmentation results of different training data scales.