Compare YOLOv7 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.
YOLOv7 vs YOLOv8 Comparison Table
| Property | YOLOv7 | YOLOv8 |
|---|---|---|
| Organization | Academia Sinica | Ultralytics |
| Category | open | open |
| Modality | vision | vision |
| Release Date | Jul 2022 | Jan 2023 |
| Context Window | — | — |
| Parameters | 6.2M-151.7M | 3.2M-68.2M |
| License | GPL v3 | AGPL 3.0 |
| Commercial use | commercial use means releasing your source code under GPL-3.0 | 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) |
| Model Features | ||
Both support 1 more feature | ||
YOLOv7 vs YOLOv8: Overview
YOLOv7 is a real-time object detection model developed by Chien-Yao Wang and Hong-Yuan Mark Liao at Academia Sinica, released in July 2022 under the GPL-3.0 license. It introduces Extended Efficient Layer Aggregation Networks (E-ELAN) for improved gradient flow in the backbone, and trainable bag-of-freebies techniques including coarse-to-fine lead guided label assignment and auxiliary heads that improve accuracy without adding inference cost.
YOLOv7 achieves 56.8% AP on COCO at 30 FPS on a V100 GPU at the time of release, establishing a strong accuracy-speed tradeoff among real-time detectors. It supports detection, instance segmentation, and pose estimation variants. YOLOv7 is deployable through Roboflow Inference and the standard training pipeline in the official repository.
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.
