#Mask DINO

DINO, featuring improved de-noising anchors, enhances Detection Transformers for superior object detection capabilities. It excels in both universal and open-set detection and segmentation tasks, showcasing significant performance on COCO benchmarks with a compact model. Utilizing ResNet and Swin Transformer backbones, DINO promises quick convergence and precision. Innovative variants like Mask DINO and Stable-DINO offer straightforward training and adaptability across diverse detection scenarios. The model zoo provides access to the latest checkpoints, supporting extensive multi-scale training and inference.

This open-source project presents a unified transformer-based architecture that enhances object detection and segmentation. It supports panoptic, instance, and semantic segmentation, and demonstrates data cooperation across tasks. The project is compatible with COCO, ADE20K, and Cityscapes datasets, offering a range of pre-trained models and tools for various AI applications.