Compare YOLO11 vs YOLOv4 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.
YOLO11 vs YOLOv4 Comparison Table
| Property | YOLO11 | YOLOv4 |
|---|---|---|
| Organization | Ultralytics | Academia Sinica |
| Category | open | open |
| Modality | vision | vision |
| Release Date | Sep 2024 | Apr 2020 |
| Context Window | — | — |
| Parameters | 2.6M-56.9M | Unknown |
| License | AGPL 3.0 | Custom |
| Commercial use | commercial use needs a license (included on Roboflow cloud) | See license terms |
| COCO Benchmark object detection, mAP 50:95 on COCO val2017 | ||
| Best size | 53.6XL · 56.9M params | Not benchmarked |
| Smallest size | 38.6Nano · 2.6M params | Not benchmarked |
| mAP range | 38.6–53.6across 5 sizes | Not benchmarked |
| Model Sizes COCO mAP 50:95, tested resolution and params per size | ||
| Nano | 38.6640×640 · 2.6M params | Not available |
| Small | 45.5640×640 · 9.4M params | Not available |
| Medium | 50.5640×640 · 20.1M params | Not available |
| Large | 52.2640×640 · 25.3M params | Not available |
| XL | 53.6640×640 · 56.9M params | Not available |
| 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 | Demo (COCO) | Supported |
| Instance Segmentation | Demo (COCO) | Not listed |
| Model Features | ||
| Real-Time Vision | Supported | Not listed |
YOLO11 vs YOLOv4: Overview
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.
YOLOv4 is an object detection model developed by Alexey Bochkovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao at Academia Sinica, released in April 2020 via the Darknet framework. It combines a CSPDarknet53 backbone, PANet neck, and YOLOv3 detection head with a large set of training improvements — Bag of Freebies and Bag of Specials — that improve accuracy with minimal inference cost increase.
YOLOv4 achieves 43.5% AP on COCO at 65 FPS on a Tesla V100 GPU. The Darknet implementation is the original version, distinguishing it from subsequent PyTorch-based reimplementations. It remains a widely referenced detection architecture and a supported training target in Roboflow Inference.
