D-FINE vs YOLO11
Compare D-FINE and YOLO11 side-by-side.
Compare D-FINE 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
D-FINE vs YOLO11 Comparison Table
Evals updated October 13, 2026Pricing updated October 23, 2026
| Property | D-FINE | YOLO11 |
|---|---|---|
| Organization | USTC | Ultralytics |
| Category | open | open |
| Modality | vision | vision |
| Release Date | Oct 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 |
D-FINE vs YOLO11: Overview
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.
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.
