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YOLOv8-Segmentation

Image Segmentation
PyTorch
real_time
android
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metadata 
library_name: pytorch
license: other
tags:
  - real_time
  - android
pipeline_tag: image-segmentation

YOLOv8-Segmentation: Optimized for Qualcomm Devices

Ultralytics YOLOv8 is a machine learning model that predicts bounding boxes, segmentation masks and classes of objects in an image.

This is based on the implementation of YOLOv8-Segmentation found here. This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the Qualcomm® AI Hub Models library to export with custom configurations. More details on model performance across various devices, can be found here.

Qualcomm AI Hub Models uses Qualcomm AI Hub Workbench to compile, profile, and evaluate this model. Sign up to run these models on a hosted Qualcomm® device.

Getting Started

Due to licensing restrictions, we cannot distribute pre-exported model assets for this model. Use the Qualcomm® AI Hub Models Python library to compile and export the model with your own:

  • Custom weights (e.g., fine-tuned checkpoints)
  • Custom input shapes
  • Target device and runtime configurations

See our repository for YOLOv8-Segmentation on GitHub for usage instructions.

Model Details

Model Type: Model_use_case.semantic_segmentation

Model Stats:

  • Model checkpoint: YOLOv8N-Seg
  • Input resolution: 640x640
  • Number of output classes: 80
  • Number of parameters: 3.43M
  • Model size (float): 13.2 MB
  • Model size (w8a16): 3.91 MB

Performance Summary

Model Runtime Precision Chipset Inference Time (ms) Peak Memory Range (MB) Primary Compute Unit
YOLOv8-Segmentation ONNX float Snapdragon® 8 Elite Gen 5 Mobile 2.957 ms 1 - 233 MB NPU
YOLOv8-Segmentation ONNX float Snapdragon® X2 Elite 3.462 ms 16 - 16 MB NPU
YOLOv8-Segmentation ONNX float Snapdragon® X Elite 6.843 ms 17 - 17 MB NPU
YOLOv8-Segmentation ONNX float Snapdragon® 8 Gen 3 Mobile 4.054 ms 17 - 299 MB NPU
YOLOv8-Segmentation ONNX float Qualcomm® QCS8550 (Proxy) 6.388 ms 1 - 12 MB NPU
YOLOv8-Segmentation ONNX float Qualcomm® QCS9075 7.781 ms 12 - 15 MB NPU
YOLOv8-Segmentation ONNX float Snapdragon® 8 Elite For Galaxy Mobile 3.308 ms 1 - 222 MB NPU
YOLOv8-Segmentation QNN_DLC float Snapdragon® 8 Elite Gen 5 Mobile 1.992 ms 1 - 196 MB NPU
YOLOv8-Segmentation QNN_DLC float Snapdragon® X2 Elite 2.813 ms 5 - 5 MB NPU
YOLOv8-Segmentation QNN_DLC float Snapdragon® X Elite 4.99 ms 5 - 5 MB NPU
YOLOv8-Segmentation QNN_DLC float Snapdragon® 8 Gen 3 Mobile 3.444 ms 0 - 206 MB NPU
YOLOv8-Segmentation QNN_DLC float Qualcomm® QCS8275 (Proxy) 17.109 ms 0 - 179 MB NPU
YOLOv8-Segmentation QNN_DLC float Qualcomm® QCS8550 (Proxy) 4.58 ms 5 - 6 MB NPU
YOLOv8-Segmentation QNN_DLC float Qualcomm® SA8775P 6.505 ms 1 - 182 MB NPU
YOLOv8-Segmentation QNN_DLC float Qualcomm® QCS9075 6.05 ms 5 - 15 MB NPU
YOLOv8-Segmentation QNN_DLC float Qualcomm® QCS8450 (Proxy) 9.281 ms 5 - 196 MB NPU
YOLOv8-Segmentation QNN_DLC float Qualcomm® SA7255P 17.109 ms 0 - 179 MB NPU
YOLOv8-Segmentation QNN_DLC float Qualcomm® SA8295P 9.408 ms 0 - 166 MB NPU
YOLOv8-Segmentation QNN_DLC float Snapdragon® 8 Elite For Galaxy Mobile 2.783 ms 0 - 181 MB NPU
YOLOv8-Segmentation TFLITE float Snapdragon® 8 Elite Gen 5 Mobile 1.835 ms 0 - 99 MB NPU
YOLOv8-Segmentation TFLITE float Snapdragon® 8 Gen 3 Mobile 3.01 ms 0 - 108 MB NPU
YOLOv8-Segmentation TFLITE float Qualcomm® QCS8275 (Proxy) 16.304 ms 4 - 83 MB NPU
YOLOv8-Segmentation TFLITE float Qualcomm® QCS8550 (Proxy) 4.104 ms 4 - 7 MB NPU
YOLOv8-Segmentation TFLITE float Qualcomm® SA8775P 5.941 ms 4 - 88 MB NPU
YOLOv8-Segmentation TFLITE float Qualcomm® QCS9075 5.868 ms 3 - 22 MB NPU
YOLOv8-Segmentation TFLITE float Qualcomm® QCS8450 (Proxy) 8.567 ms 4 - 86 MB NPU
YOLOv8-Segmentation TFLITE float Qualcomm® SA7255P 16.304 ms 4 - 83 MB NPU
YOLOv8-Segmentation TFLITE float Qualcomm® SA8295P 8.665 ms 4 - 60 MB NPU
YOLOv8-Segmentation TFLITE float Snapdragon® 8 Elite For Galaxy Mobile 2.259 ms 0 - 84 MB NPU

License

  • The license for the original implementation of YOLOv8-Segmentation can be found here.

References

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