Deformable DETR is an end-to-end object detection model that improves upon the original DETR by addressing its limitations in convergence speed and computational complexity. The key innovation is the introduction of deformable attention modules, which focus on a small set of key sampling points around a reference, significantly reducing the computational burden compared to traditional Transformer attention mechanisms. This approach allows Deformable DETR to achieve better performance, especially for small objects, with 10 times fewer training epochs than DETR. The model uses multi-scale deformable attention modules to efficiently process feature maps at different resolutions, enabling effective object detection without the need for feature pyramid networks. The Deformable DETR model is designed to be efficient and fast-converging, making it suitable for real-time applications. It incorporates an iterative bounding box refinement mechanism and a two-stage approach, where region proposals are generated in the first stage and refined in the second stage. These improvements enhance detection accuracy and convergence speed. Extensive experiments on the COCO benchmark demonstrate that Deformable DETR outperforms DETR and other state-of-the-art methods in terms of detection accuracy and efficiency. The model achieves higher performance with significantly reduced computational costs, making it a promising solution for end-to-end object detection. The proposed method is implemented with a focus on efficiency and effectiveness, and the code is available for further research and development.Deformable DETR is an end-to-end object detection model that improves upon the original DETR by addressing its limitations in convergence speed and computational complexity. The key innovation is the introduction of deformable attention modules, which focus on a small set of key sampling points around a reference, significantly reducing the computational burden compared to traditional Transformer attention mechanisms. This approach allows Deformable DETR to achieve better performance, especially for small objects, with 10 times fewer training epochs than DETR. The model uses multi-scale deformable attention modules to efficiently process feature maps at different resolutions, enabling effective object detection without the need for feature pyramid networks. The Deformable DETR model is designed to be efficient and fast-converging, making it suitable for real-time applications. It incorporates an iterative bounding box refinement mechanism and a two-stage approach, where region proposals are generated in the first stage and refined in the second stage. These improvements enhance detection accuracy and convergence speed. Extensive experiments on the COCO benchmark demonstrate that Deformable DETR outperforms DETR and other state-of-the-art methods in terms of detection accuracy and efficiency. The model achieves higher performance with significantly reduced computational costs, making it a promising solution for end-to-end object detection. The proposed method is implemented with a focus on efficiency and effectiveness, and the code is available for further research and development.