# Instance Segmentation of Shrimp Based on Contrastive Learning
## 4 Experimental Results
### Instance Segmentation Results  
As previously mentioned, our approach employs the Mask R-CNN [14] architecture, but with different backbones. The experimental results of instance segmentation are summarized in Table 2. We use \"Super. random\" and \"Super. pre-trained\" to indicate the use of random initialization and the pre-trained model on the COCO dataset to initialize the backbone module at the beginning of fine-tuning. The other entries represent the use of the five contrastive learning models as backbones, with the parameters frozen and the rest of the Mask R-CNN model fine-tuned. In particular, MoCov2* is a contrastive learning model trained on the ImageNet [31] dataset, not on our shrimp dataset. The results in Table 2 demonstrate that all the instance segmentation performances of the contrastive learning models exceed those of the supervised learning model. Specifically, MoCov2 and MoCov2* models outperform the other models in all metrics, with the second-best model, MoCov3, achieving approximately 6.5% higher \(\mathbf{AP}^{bb}\) and SimCLR about 4.5% higher \(\mathbf{AP}^{mk}\). Here, we found surprising results. Even though the MoCov2* model is trained on ImageNet, where this dataset does not have any shrimp images, it still has the best performance in terms of bounding box regression. We think this can be attributed to its powerful ability of feature extraction. The training batch size has harmful effects on performance when trained on our shrimp dataset, where in the original paper the training batch size is 4096 and ours is 64. For the semantic segmentation task, MoCov2* is only slightly worse than MoCov2. We think this is because the semantic segmentation task is harder than bounding box regression and MoCov2 has seen many shrimp images. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Models**} & \multicolumn{2}{c}{**Data Augmentations**} \\ \hline MoCov1 [8] & RandomResizedCrop & RandomGrayscale & RandomFlip \\ \hline MoCov2 [9] & RandomResizedCrop & RandomGrayscale & RandomFlip \\  & RandomApply & RandomGaussianBlur & \\ \hline MoCov3 [10] & RandomResizedCrop & RandomGrayscale & RandomFlip \\  & RandomApply & RandomGaussianBlur & RandomSolarize \\ \hline SimCLR [11] & RandomResizedCrop & RandomGrayscale & RandomFlip \\  & RandomApply & RandomGaussianBlur & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The data augmentations used by each contrastive learning model. On the other hand, MoCov3, SimCLR, and Byol exhibit similar performances in detection and segmentation, but their performance is significantly better than that of the supervised method. The MoCov1 model has the worst performance among the five contrastive learning models, but it still outperforms the supervised method, with an 18.7% increase in \(\mathbf{AP}^{bb}\) and 7.6% increase in \(\mathbf{AP}^{mk}\). These experimental results strongly support our argument that combining the unsupervised learning method of contrastive learning for fine-tuning can achieve better experimental results than directly fine-tuning, even though the contrastive learning model is not pre-trained on the specific dataset. When training the contrastive learning model, it can learn potential feature representations in the dataset through contrastive loss, which can better segment the target. Several instance segmentation results are visualized in Figure 4, which clearly demonstrate that MoCov2 exhibits relatively better segmentation quality compared to the other methods.