Detectron2 vs YOLOv8
Compare Detectron2 and YOLOv8 side-by-side.
Compare Detectron2 vs YOLOv8 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
Detectron2 vs YOLOv8 Comparison Table
Evals updated October 13, 2026Pricing updated October 23, 2026
| Property | Detectron2 | YOLOv8 |
|---|---|---|
| Organization | Meta | Ultralytics |
| Category | open | open |
| Modality | vision | vision |
| Release Date | Sep 2019 | Jan 2023 |
| Context Window | — | — |
| Parameters | Unknown | 3.2M-68.2M |
| 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 | 52.9XL · 68.2M params |
| Smallest size | Not benchmarked | 36.5Nano · 3.2M params |
| mAP range | Not benchmarked | 36.5–52.9across 5 sizes |
| Model Sizes COCO mAP 50:95, tested resolution and params per size | ||
| Nano | Not available | 36.5640×640 · 3.2M params |
| Small | Not available | 44.1640×640 · 11.2M params |
| Medium | Not available | 49.2640×640 · 25.9M params |
| Large | Not available | 51.8640×640 · 43.7M params |
| XL | Not available | 52.9640×640 · 68.2M 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 | Supported | Not listed |
| Keypoint Detection | Supported | Not listed |
| Semantic Segmentation | Supported | Not listed |
| Model Features | ||
| Foundation Vision | Supported | Not listed |
| Real-Time Vision | Not listed | Supported |
Detectron2 vs YOLOv8: Overview
Detectron2 is a computer vision model library developed by Facebook AI Research (Meta), released in September 2019. It serves as a comprehensive platform for object detection, instance segmentation, panoptic segmentation, keypoint detection, and DensePose, implemented in PyTorch. It is the successor to the original Detectron framework, which was written in Caffe2, and offers a more modular and extensible codebase designed for both research and production use.
Detectron2 includes implementations of Faster R-CNN, Mask R-CNN, RetinaNet, Cascade R-CNN, Panoptic FPN, and several other architectures. Its modular design allows components such as backbones, necks, and heads to be swapped independently, making it widely used as a baseline framework in academic research. It supports training on COCO-format datasets and integrates with standard distributed training setups.
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.
