#EfficientSAM

This project presents an unofficial implementation of YOLO-World and EfficientSAM models tailored for ComfyUI, emphasizing practical object detection and segmentation. Version 2.0 enhances functionality with mask separation and extraction, compatible with images and videos. The project facilitates model loading for YOLO-World and EfficientSAM on both CUDA and CPU platforms. It offers features like confidence and IoU threshold setting, and customizable mask outputs. Contributions such as the Yoloworld ESAM Detector Provider add value, with user-friendly installation and thorough workflows, supporting detailed detection tasks.

EfficientSAM employs advanced masked image pretraining to boost segmentation performance across diverse applications. Integrated with popular tools like Labelme, and available on platforms such as Hugging Face Space in ONNX format, it showcases versatile instance segmentation through point, box, and saliency prompts. With accessible checkpoints and comprehensive model examples, EfficientSAM streamlines the implementation of segmentation tasks. Recent enhancements include torchscript and grounded demos, expanding usability and reach. Discover its full capabilities via online demos and Jupyter Notebook illustrations.