Diabetic retinopathy (DR) is a leading cause of preventable blindness among working-age adults, with global cases expected to rise from 103 million in 2020 to 161 million by 2045. Early detection and treatment are essential for preventing vision loss. While color fundus photography (CFP) is the gold standard for DR diagnosis, ultra-widefield (UWF) fundus imaging, which provides up to a 200-degree view of the retina, is gaining traction as an alternative. However, classifying UWF images remains labor-intensive, and research on automated analysis is limited. In this study, we trained several deep-learning models for DR detection and assessment using UWF imaging. For Task 1, Image Quality Assessment, we utilized the DINOv2 models due to their ability to efficiently manage complex features while maintaining computational efficiency. For Task 2, Identification of Referable DR, we employed MobileNetV3, which stood out for its lightweight architecture and advanced features like Squeeze-and-Excitation modules and H-Swish activation. MobileNetV3 achieved high accuracy with minimal computational requirements, making it well-suited for clinical deployment in resource-limited environments. To further enhance the model performance, we applied test-time augmentation (TTA) during inference, improving robustness and accuracy in challenging cases. This approach offers a powerful and efficient solution for DR classification and detection in UWF fundus images. Our proposed model ranked second in identifying referable DR on the final test dataset. Souce code is available at https://github.com/RespectKnowledge/UWF4DR_Challenge_2nd_place .