This paper revisits the ResNet model for visual recognition, exploring whether deeper or wider networks perform better. The authors analyze the behavior of residual networks and propose a new interpretation of their structure, suggesting that they function as an ensemble of shallow networks rather than a single deep network. They derive a new, shallower residual network architecture that outperforms deeper models like ResNet-200 on the ImageNet dataset. This architecture also performs well in semantic segmentation tasks on datasets such as PASCAL VOC, PASCAL Context, and Cityscapes. The new model is more efficient in memory use and sometimes training time, while still achieving state-of-the-art results. The study shows that deeper networks are not always better, and that wider networks can sometimes outperform deeper ones. The authors also demonstrate that their model can be used as a pre-trained feature extractor to improve existing algorithms. The results indicate that the proposed architecture provides better feature extraction performance than current state-of-the-art methods. The paper includes experimental results on image classification and semantic segmentation, showing the effectiveness of the new model. The authors conclude that their architecture achieves fully end-to-end training for large networks and outperforms previous deep residual networks in both classification and segmentation tasks.This paper revisits the ResNet model for visual recognition, exploring whether deeper or wider networks perform better. The authors analyze the behavior of residual networks and propose a new interpretation of their structure, suggesting that they function as an ensemble of shallow networks rather than a single deep network. They derive a new, shallower residual network architecture that outperforms deeper models like ResNet-200 on the ImageNet dataset. This architecture also performs well in semantic segmentation tasks on datasets such as PASCAL VOC, PASCAL Context, and Cityscapes. The new model is more efficient in memory use and sometimes training time, while still achieving state-of-the-art results. The study shows that deeper networks are not always better, and that wider networks can sometimes outperform deeper ones. The authors also demonstrate that their model can be used as a pre-trained feature extractor to improve existing algorithms. The results indicate that the proposed architecture provides better feature extraction performance than current state-of-the-art methods. The paper includes experimental results on image classification and semantic segmentation, showing the effectiveness of the new model. The authors conclude that their architecture achieves fully end-to-end training for large networks and outperforms previous deep residual networks in both classification and segmentation tasks.