Detectron2 vs YOLOv10
Compare Detectron2 and YOLOv10 side-by-side.
Compare Detectron2 vs YOLOv10 live
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Models in this comparison
Detectron2 vs YOLOv10 Comparison Table
Evals updated October 13, 2026Pricing updated October 23, 2026
| Property | Detectron2 | YOLOv10 |
|---|---|---|
| Organization | Meta | THU-MIG |
| Category | open | open |
| Modality | vision | vision |
| Release Date | Sep 2019 | May 2024 |
| Context Window | — | — |
| Parameters | Unknown | 2.3M-29.5M |
| 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 · 31.7M params |
| Smallest size | Not benchmarked | 38.0Nano · 2.8M params |
| mAP range | Not benchmarked | 38.0–53.6across 5 sizes |
| Model Sizes COCO mAP 50:95, tested resolution and params per size | ||
| Nano | Not available | 38.0640×640 · 2.8M params |
| Small | Not available | 45.7640×640 · 8.1M params |
| Medium | Not available | 50.3640×640 · 16.5M params |
| Large | Not available | 52.3640×640 · 25.8M params |
| XL | Not available | 53.6640×640 · 31.7M 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. | ||
| 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 |
Detectron2 vs YOLOv10: 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.
YOLOv10 is a real-time end-to-end object detection model developed by THU-MIG at Tsinghua University, released in May 2024 under the AGPL-3.0 license. It introduces consistent dual assignments during training — using both one-to-many and one-to-one label assignment strategies — to eliminate the need for non-maximum suppression at inference time while maintaining competitive accuracy. This end-to-end design reduces inference latency compared to NMS-dependent detectors at similar accuracy levels.
YOLOv10-B achieves 52.7% AP on COCO with 46% lower latency than YOLOv9-C at comparable performance. The model is available in six sizes from Nano to Extra Large, built on the Ultralytics framework, and exportable to ONNX, TensorRT, and CoreML. YOLOv10 is suited for latency-sensitive deployment scenarios where post-processing overhead is a constraint.
