Retinal detachment (RD) is a vision-threatening condition requiring prompt intervention to preserve sight. A critical factor in treatment urgency and visual prognosis is macular involvement—whether the macula is intact or detached. Point-of-care ultrasound (POCUS) is a fast, non-invasive, and cost-effective imaging tool commonly used to detect RD across various clinical settings. However, its diagnostic utility is limited by the need for expert interpretation, especially in resource-limited environments. Deep learning has the potential to automate RD detection on ultrasound, but no clinically available models exist, and prior research has not addressed macular status—an essential distinction for surgical prioritization. Additionally, no public dataset currently supports macular-based RD classification using ultrasound video. We introduce Eye Retinal DEtachment ultraSound (ERDES), the first open-access dataset of ocular ultrasound clips labeled for (i) presence of RD and (ii) macula-detached vs. macula-intact status. ERDES enables machine learning development for RD detection. We also provide baseline benchmarks by training 40 models across eight architectures, including 3D convolutional networks and transformer-based models. For code, pre-trained models, and training scripts, see: https://github.com/OSUPCVLab/ERDES.