metadata
library_name: pytorch
license: agpl-3.0
tags:
- real_time
- android
pipeline_tag: image-segmentation
YOLOv8-Segmentation: Optimized for Mobile Deployment
Real-time object segmentation optimized for mobile and edge by Ultralytics
Ultralytics YOLOv8 is a machine learning model that predicts bounding boxes, segmentation masks and classes of objects in an image.
This model is an implementation of YOLOv8-Segmentation found here.
More details on model performance across various devices, can be found here.
Model Details
- Model Type: Semantic segmentation
- Model Stats:
- Model checkpoint: YOLOv8N-Seg
- Input resolution: 640x640
- Number of parameters: 3.43M
- Model size: 13.2 MB
- Number of output classes: 80
| Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model |
|---|---|---|---|---|---|---|---|---|
| YOLOv8-Segmentation | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 4.951 ms | 5 - 7 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 7.858 ms | 14 - 39 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 3.571 ms | 5 - 24 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 5.469 ms | 7 - 68 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 4.159 ms | 5 - 52 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 4.665 ms | 15 - 64 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | SA7255P ADP | SA7255P | QNN | 90.376 ms | 1 - 10 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | SA8255 (Proxy) | SA8255P Proxy | QNN | 4.999 ms | 5 - 8 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | SA8295P ADP | SA8295P | QNN | 9.161 ms | 0 - 14 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | SA8650 (Proxy) | SA8650P Proxy | QNN | 4.947 ms | 5 - 7 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | SA8775P ADP | SA8775P | QNN | 8.042 ms | 0 - 10 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | QCS8275 (Proxy) | QCS8275 Proxy | QNN | 90.376 ms | 1 - 10 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 4.967 ms | 5 - 7 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | QCS9075 (Proxy) | QCS9075 Proxy | QNN | 8.042 ms | 0 - 10 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 9.35 ms | 5 - 38 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 5.405 ms | 5 - 5 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 7.902 ms | 17 - 17 MB | FP16 | NPU | -- |
License
- The license for the original implementation of YOLOv8-Segmentation can be found here.
- The license for the compiled assets for on-device deployment can be found here
References
Community
- Join our AI Hub Slack community to collaborate, post questions and learn more about on-device AI.
- For questions or feedback please reach out to us.
Usage and Limitations
Model may not be used for or in connection with any of the following applications:
- Accessing essential private and public services and benefits;
- Administration of justice and democratic processes;
- Assessing or recognizing the emotional state of a person;
- Biometric and biometrics-based systems, including categorization of persons based on sensitive characteristics;
- Education and vocational training;
- Employment and workers management;
- Exploitation of the vulnerabilities of persons resulting in harmful behavior;
- General purpose social scoring;
- Law enforcement;
- Management and operation of critical infrastructure;
- Migration, asylum and border control management;
- Predictive policing;
- Real-time remote biometric identification in public spaces;
- Recommender systems of social media platforms;
- Scraping of facial images (from the internet or otherwise); and/or
- Subliminal manipulation