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qualcomm
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YOLOv8-Segmentation

Image Segmentation
PyTorch
TF Lite
real_time
android
Model card Files Community
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@@ -29,13 +29,14 @@ More details on model performance across various devices, can be found
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  - Input resolution: 640x640
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  - Number of parameters: 3.43M
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  - Model size: 13.2 MB
 
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  | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
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  | ---|---|---|---|---|---|---|---|
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- | Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 6.377 ms | 4 - 7 MB | FP16 | NPU | [YOLOv8-Segmentation.tflite](https://huggingface.co/qualcomm/YOLOv8-Segmentation/blob/main/YOLOv8-Segmentation.tflite)
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  - Input resolution: 640x640
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  - Number of parameters: 3.43M
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  - Model size: 13.2 MB
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+ - Number of output classes: 80
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  | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
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  | ---|---|---|---|---|---|---|---|
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+ | Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 6.526 ms | 4 - 14 MB | FP16 | NPU | [YOLOv8-Segmentation.tflite](https://huggingface.co/qualcomm/YOLOv8-Segmentation/blob/main/YOLOv8-Segmentation.tflite)
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