DEIM vs YOLO11
Compare DEIM and YOLO11 side-by-side.
Compare DEIM vs YOLO11 live
Run the same image across every model that supports a task and compare their outputs side-by-side.
These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.
Models in this comparison
DEIM vs YOLO11 Comparison Table
Evals updated October 13, 2026Pricing updated October 23, 2026
| Property | DEIM | YOLO11 |
|---|---|---|
| Organization | Intellindust AI Lab | Ultralytics |
| Category | open | open |
| Modality | vision | vision |
| Release Date | Dec 2024 | Sep 2024 |
| Context Window | — | — |
| Parameters | 4M-62M | 2.6M-56.9M |
| License | Apache 2.0 | AGPL 3.0 |
| Commercial use | free for commercial use | commercial use needs a license (included on Roboflow cloud) |
| COCO Benchmark object detection, mAP 50:95 on COCO val2017 | ||
| Best size | Not benchmarked | 53.6XL · 56.9M params |
| Smallest size | Not benchmarked | 38.6Nano · 2.6M params |
| mAP range | Not benchmarked | 38.6–53.6across 5 sizes |
| Model Sizes COCO mAP 50:95, tested resolution and params per size | ||
| Nano | Not available | 38.6640×640 · 2.6M params |
| Small | Not available | 45.5640×640 · 9.4M params |
| Medium | Not available | 50.5640×640 · 20.1M params |
| Large | Not available | 52.2640×640 · 25.3M params |
| XL | Not available | 53.6640×640 · 56.9M params |
| COCO val2017 mAP 50:95 and parameter counts from leaderboard.roboflow.com. Size names don't line up across model families, so compare sizes with similar parameter counts. The live demo runs some of these sizes at other resolutions, which were not benchmarked. | ||
| Vision Tasks | ||
| Object Detection | Supported | Demo (COCO) |
| Instance Segmentation | Not listed | Demo (COCO) |
| Model Features | ||
| Real-Time Vision | Supported | Supported |
DEIM vs YOLO11: Overview
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.
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.
