Roboflow: RF-DETR

RF-DETR Segmentation is a real-time instance segmentation model developed by Roboflow, with a preview base model released in October 2025 under the Apache 2.0 license and the full variant family — Nano through 2XL — released in January 2026. It extends the RF-DETR object detection architecture with a segmentation head inspired by MaskDINO, enabling pixel-level object delineation while maintaining the real-time performance characteristics of the base model. It is deployable through Roboflow Inference and the open-source rfdetr Python package.RF-DETR Segmentation supports fine-tuning on custom COCO- or YOLO-format instance segmentation datasets and is benchmarked on Microsoft COCO. It is suited for applications requiring both precise object masks and real-time inference, such as robotic manipulation, quality control, and augmented reality overlays.

RT-DETR (Real-Time Detection Transformer) is an object detection model developed by Baidu, released in April 2023 under the Apache 2.0 license. It is the first transformer-based real-time object detector, addressing the inference speed limitations of earlier DETR models through an efficient hybrid encoder that decouples intra-scale interaction and cross-scale fusion, enabling the model to process multi-scale features without the high computational overhead of standard transformer encoders.RT-DETR achieves 53.1% AP on COCO at 108 FPS on an NVIDIA T4 GPU for the RT-DETR-L variant, outperforming comparably sized YOLO detectors at similar speeds. It maintains end-to-end inference without non-maximum suppression, simplifying deployment pipelines. RT-DETR established the baseline for real-time transformer detection and has been extended by subsequent works including RF-DETR and RT-DETRv2.

YOLO11 is an object detection and multi-task vision model developed by Ultralytics, released in September 2024 under the AGPL-3.0 license. It is the latest generation in the Ultralytics YOLO series and supports object detection, instance segmentation, image classification, pose estimation, and oriented bounding box detection within a single unified framework. YOLO11 introduces architectural refinements that improve accuracy while reducing parameter count compared to YOLOv8 at equivalent model sizes.YOLO11 is available in five model sizes from Nano to Extra Large and is deployable through the Ultralytics Python package, Roboflow Inference, and export formats including ONNX, TensorRT, and CoreML. It supports fine-tuning on custom datasets through the standard Ultralytics training API.

YOLOv8 is an object detection and multi-task vision model developed by Ultralytics, released in January 2023 under the AGPL-3.0 license. It succeeds YOLOv5 and introduces an anchor-free detection head, a new C2f module for improved gradient flow, and a decoupled head that separates classification and regression tasks. These changes improve both accuracy and training efficiency compared to earlier Ultralytics models.YOLOv8 supports object detection, instance segmentation, image classification, pose estimation, and oriented bounding box detection within a unified codebase. It is available in five sizes from Nano to Extra Large and exports to ONNX, TensorRT, CoreML, and other formats. YOLOv8 is one of the most widely adopted detection models in production and is directly supported by Roboflow Inference for custom model training and deployment.

D-FINE is a real-time object detection model introduced in October 2024 by researchers at the University of Science and Technology of China. It builds on the DETR family of transformer-based detectors by reformulating bounding box regression as a Fine-grained Distribution Refinement task. Rather than predicting box coordinates directly, D-FINE iteratively refines probability distributions over coordinate offsets across decoder layers, which provides finer localization granularity without adding inference cost. The architecture also replaces the encoder's CSP blocks with GELAN modules and inserts a Target Gating Layer after the decoder's cross-attention to reduce representational entanglement across queries. A second contribution, Global Optimal Localization Self-Distillation, transfers localization knowledge from refined deeper-layer predictions back to earlier decoder layers through internal self-distillation.D-FINE is released in five model sizes (Nano, Small, Medium, Large, and X), with D-FINE-L achieving 54.0% AP on the Microsoft COCO benchmark at 124 FPS on an NVIDIA T4 GPU, and D-FINE-X reaching 55.8% AP at 78 FPS. Pretraining on the Objects365 dataset further improves accuracy to 57.1% AP for the L variant and 59.3% AP for the X variant. The paper was accepted at ICLR 2025 as a Spotlight. Code and pretrained weights are released under the Apache 2.0 license, making the model suitable for commercial use.

DEIM is a training framework for DETR-based object detection models released in December 2024 by researchers at Intellindust AI Lab, City University of Hong Kong, Great Bay University, and Hefei Normal University. It enhances existing real-time DETR architectures by improving the matcher used during training, enabling faster convergence and higher accuracy without modifying the inference architecture or adding computational overhead at deployment time. DEIM introduces two core techniques: Dense One-to-One (O2O) matching, which increases the number of positive matches per target, and Matchability-Aware Loss (MAL), which down-weights low-quality matches generated by the dense strategy. The paper was accepted at CVPR 2025.When integrated with RT-DETR and D-FINE, DEIM consistently improves performance while reducing training time by up to 50%. Applied to RT-DETRv2, it achieves 53.2% AP with a single day of training on an NVIDIA 4090 GPU. DEIM-enhanced models including DEIM-D-FINE-L and DEIM-D-FINE-X achieve 54.7% and 56.5% AP at 124 and 78 FPS respectively on an NVIDIA T4 GPU. DEIM is released under the Apache 2.0 license. A successor, DEIMv2, was released in September 2025, adding DINOv3-based backbones and introducing ultra-lightweight variants (Pico, Femto, and Atto) for edge deployment.