MobileNet SSD v2 vs YOLOv8
Compare MobileNet SSD v2 and YOLOv8 side-by-side.
Compare MobileNet SSD v2 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
MobileNet SSD v2 vs YOLOv8 Comparison Table
Evals updated October 13, 2026Pricing updated October 23, 2026
| Property | MobileNet SSD v2 | YOLOv8 |
|---|---|---|
| Organization | Ultralytics | |
| Category | open | open |
| Modality | vision | vision |
| Release Date | Jan 2018 | Jan 2023 |
| Context Window | — | — |
| Parameters | 15.3M | 3.2M-68.2M |
| License | MIT | 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) |
| Model Features | ||
| Real-Time Vision | Supported | Supported |
MobileNet SSD v2 vs YOLOv8: Overview
MobileNet SSD v2 is a lightweight object detection model developed by Google Research, released in January 2018. It combines the MobileNetV2 backbone with the Single Shot MultiBox Detector (SSD) framework to produce a model optimized for inference on mobile and edge devices. MobileNetV2 introduces inverted residuals and linear bottlenecks to reduce computation while maintaining representational capacity compared to its predecessor.
MobileNet SSD v2 is designed for real-time on-device detection, making it suitable for mobile apps, embedded systems, and IoT devices. It performs object detection across a fixed set of categories and can be fine-tuned on custom datasets. It trades peak accuracy for reduced inference cost and model size relative to larger two-stage detectors.
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.
