README.md

---
library_name: pytorch
license: agpl-3.0
tags:
- real_time
- android
pipeline_tag: image-segmentation

---

![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/yolov8_seg/web-assets/model_demo.png)

# 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](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/yolo/segment).


 More details on model performance across various devices, can be found [here](https://aihub.qualcomm.com/models/yolov8_seg).

### 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 | TFLITE | 6.339 ms | 4 - 31 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 6.374 ms | 5 - 7 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 7.4 ms | 15 - 47 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 4.641 ms | 4 - 62 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 4.417 ms | 5 - 25 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 5.023 ms | 17 - 82 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 3.766 ms | 0 - 51 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 4.392 ms | 5 - 60 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 4.813 ms | 3 - 58 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | SA7255P ADP | SA7255P | TFLITE | 93.022 ms | 4 - 49 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | SA7255P ADP | SA7255P | QNN | 92.171 ms | 1 - 8 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 6.341 ms | 4 - 22 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | SA8255 (Proxy) | SA8255P Proxy | QNN | 6.332 ms | 5 - 8 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | SA8295P ADP | SA8295P | TFLITE | 11.343 ms | 4 - 37 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | SA8295P ADP | SA8295P | QNN | 10.824 ms | 0 - 10 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 6.373 ms | 4 - 23 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | SA8650 (Proxy) | SA8650P Proxy | QNN | 6.346 ms | 5 - 7 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | SA8775P ADP | SA8775P | TFLITE | 9.949 ms | 4 - 49 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | SA8775P ADP | SA8775P | QNN | 9.903 ms | 0 - 6 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | QCS8275 (Proxy) | QCS8275 Proxy | TFLITE | 93.022 ms | 4 - 49 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | QCS8275 (Proxy) | QCS8275 Proxy | QNN | 92.171 ms | 1 - 8 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 6.424 ms | 4 - 27 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 6.319 ms | 5 - 8 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | QCS9075 (Proxy) | QCS9075 Proxy | TFLITE | 9.949 ms | 4 - 49 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | QCS9075 (Proxy) | QCS9075 Proxy | QNN | 9.903 ms | 0 - 6 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 9.95 ms | 4 - 46 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 9.311 ms | 5 - 47 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 7.066 ms | 5 - 5 MB | FP16 | NPU | -- |
| YOLOv8-Segmentation | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 7.708 ms | 17 - 17 MB | FP16 | NPU | -- |




## License
* The license for the original implementation of YOLOv8-Segmentation can be found
  [here](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).
* The license for the compiled assets for on-device deployment can be found [here](https://github.com/ultralytics/ultralytics/blob/main/LICENSE)



## References
* [Ultralytics YOLOv8 Docs: Instance Segmentation](https://docs.ultralytics.com/tasks/segment/)
* [Source Model Implementation](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/yolo/segment)



## Community
* Join [our AI Hub Slack community](https://qualcomm-ai-hub.slack.com/join/shared_invite/zt-2d5zsmas3-Sj0Q9TzslueCjS31eXG2UA#/shared-invite/email) to collaborate, post questions and learn more about on-device AI.
* For questions or feedback please [reach out to us](mailto:[email protected]).

## 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