## 4 Experiments  
### Classification  
For the classification task, the Omni-MNIST [20] dataset is used in this paper. This dataset is an equirectangular projection generated by MNIST through back projection with a resolution of 60 \(\times\) 60. As for the MNIST dataset, the Omni-MNIST data includes 100,000 training and 60,000 test samples with 10 classes. We use the convolution neural network to directly train the equirectangular images of the Omni-MNIST dataset to obtain EquirectCNN, which has four convolutional layers and one fully connected layer. The acquired features after the first layer of convolution are not processed by the max pooling and activation function and do not change the shape of the input, while the other convolution layers are processed using the max pooling and activation function. SphereNet [20] can also solve the problem of edge discontinuity and distortion by resampling. For comparison with the proposed ECDAB, the first convolutional layer of EquirectCNN is replaced by SphereNet to build SphereNetCNN. The question arises of why all the convolutional layers of the network are not constructed using the SphereNet. The reason is that [PERSON] et al. [36] also noted that the interpolation assumption of SphereNet affects the performance of the network as the depth of the network deepens. Similarly, we trained ECDANet, which replaces the first convolutional layer of EquirectCNN with our proposed ECDAB. We trained the CRCANet based on the CRCAB, which removed the first layer of EquirectCNN and added a CRCAB in front of the last fully connected layer. Similarly, we replaced CRCAB with ICRCAB and constructed ICRCANet. In addition, we also trained ECDA-CRCANet and ECDA-ICRCANet, which combine ECDAB, CRCAB, and ICRCAB. ECDA-CRCANet adds a CRCAB in front of the last fully connected layer of ECDANet and the DAB also divides the image into two blocks. ECDA-ICRCANet replaces the CRCAB in ECDA-CRCANet with the ICRCAB. To achieve better performance from the network, we add the output features of EDCAB, CRCAB and ICRCAB to the input features to construct the residual structure. During training the optimizer is Adam [44], with a base learning rate of 0.001 and batches of size 128 for 100 epochs. In the experiment, we found that increasing the number of segmentation blocks in DAB had a negative impact on the final classification accuracy. In general, we suggest that zero padding is performed when group convolution occurs after segmentation and that the higher the number of segmentation blocks, the more zero padding is performed. For the classification experiments, the shallow depth of the network model did not accommodate zero padding well, so the gain in distortion mitigation was not high. Finally, the number of segmentation blocks was set to two. For ICRCAB, we added it to the last layer of the network, which had an output resolution of 6 \(\times\) 6. After considering the final accuracy, the input features were divided into three vectors of rows and columns. Figure 7: The feature row vectors in CRCAB (**left**) and ICRCAB (**right**). To achieve greater rigor in the experiment, each network was trained five times, with 100 epochs each time and, finally, the average of the precision was taken. The experimental results are shown in Table 2. The change in test accuracy in the training process is shown in Figure 8; the test accuracy was obtained by taking the average of five experimental results. The experimental results showed that most of our proposed models achieved lower classification error rates than the baseline model, especially for the error rates obtained by ICRCANet and ECDA-ICRCANet, which were 2.8% and 4.01%, respectively, both lower than EquirectCNN. To verify the effectiveness of our proposed CRCAB, we only performed rigorous experiments on CIFAR-10. Although the focus of this paper is on 360\({}^{\circ}\) omnidirectional images, CRCAB is equally valid for conventional images. The experiment undertaken was similar to that for the Omni-MNIST dataset, with the same network structure.