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YOLOv11-Segmentation

Image Segmentation
PyTorch
real_time
android
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v0.49.1

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See https://github.com/qualcomm/ai-hub-models/releases/v0.49.1 for changelog.

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  1. README.md +30 -30
README.md CHANGED
@@ -15,18 +15,18 @@ pipeline_tag: image-segmentation
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  Ultralytics YOLOv11 is a machine learning model that predicts bounding boxes, segmentation masks and classes of objects in an image.
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  This is based on the implementation of YOLOv11-Segmentation found [here](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/yolo/segment).
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- This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/main/qai_hub_models/models/yolov11_seg) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
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  Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.com) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) to run these models on a hosted Qualcomm® device.
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  ## Getting Started
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  Due to licensing restrictions, we cannot distribute pre-exported model assets for this model.
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- Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/main/qai_hub_models/models/yolov11_seg) Python library to compile and export the model with your own:
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  - Custom weights (e.g., fine-tuned checkpoints)
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  - Custom input shapes
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  - Target device and runtime configurations
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- See our repository for [YOLOv11-Segmentation on GitHub](https://github.com/qualcomm/ai-hub-models/blob/main/qai_hub_models/models/yolov11_seg) for usage instructions.
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  ## Model Details
@@ -44,33 +44,33 @@ See our repository for [YOLOv11-Segmentation on GitHub](https://github.com/qualc
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  ## Performance Summary
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  | Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
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  |---|---|---|---|---|---|---
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- | YOLOv11-Segmentation | ONNX | float | Snapdragon® X2 Elite | 3.407 ms | 16 - 16 MB | NPU
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- | YOLOv11-Segmentation | ONNX | float | Snapdragon® X Elite | 7.146 ms | 17 - 17 MB | NPU
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- | YOLOv11-Segmentation | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 4.196 ms | 0 - 269 MB | NPU
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- | YOLOv11-Segmentation | ONNX | float | Qualcomm® QCS8550 (Proxy) | 6.698 ms | 11 - 15 MB | NPU
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- | YOLOv11-Segmentation | ONNX | float | Qualcomm® QCS9075 | 7.837 ms | 11 - 14 MB | NPU
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- | YOLOv11-Segmentation | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 3.456 ms | 1 - 225 MB | NPU
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- | YOLOv11-Segmentation | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 2.917 ms | 0 - 235 MB | NPU
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- | YOLOv11-Segmentation | ONNX | w8a16 | Snapdragon® X2 Elite | 2.66 ms | 6 - 6 MB | NPU
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- | YOLOv11-Segmentation | ONNX | w8a16 | Snapdragon® X Elite | 6.411 ms | 8 - 8 MB | NPU
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- | YOLOv11-Segmentation | ONNX | w8a16 | Snapdragon® 8 Gen 3 Mobile | 3.645 ms | 8 - 236 MB | NPU
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- | YOLOv11-Segmentation | ONNX | w8a16 | Qualcomm® QCS6490 | 423.428 ms | 164 - 169 MB | CPU
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- | YOLOv11-Segmentation | ONNX | w8a16 | Qualcomm® QCS8550 (Proxy) | 5.887 ms | 5 - 11 MB | NPU
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- | YOLOv11-Segmentation | ONNX | w8a16 | Qualcomm® QCS9075 | 7.101 ms | 7 - 10 MB | NPU
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- | YOLOv11-Segmentation | ONNX | w8a16 | Qualcomm® QCM6690 | 217.803 ms | 156 - 166 MB | CPU
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- | YOLOv11-Segmentation | ONNX | w8a16 | Snapdragon® 8 Elite For Galaxy Mobile | 2.743 ms | 2 - 102 MB | NPU
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- | YOLOv11-Segmentation | ONNX | w8a16 | Snapdragon® 7 Gen 4 Mobile | 196.767 ms | 100 - 110 MB | CPU
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- | YOLOv11-Segmentation | ONNX | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 2.425 ms | 0 - 89 MB | NPU
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- | YOLOv11-Segmentation | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 3.155 ms | 0 - 117 MB | NPU
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- | YOLOv11-Segmentation | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 15.378 ms | 4 - 85 MB | NPU
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- | YOLOv11-Segmentation | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 4.314 ms | 4 - 6 MB | NPU
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- | YOLOv11-Segmentation | TFLITE | float | Qualcomm® SA8775P | 6.006 ms | 4 - 89 MB | NPU
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- | YOLOv11-Segmentation | TFLITE | float | Qualcomm® QCS9075 | 5.867 ms | 4 - 22 MB | NPU
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- | YOLOv11-Segmentation | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 10.077 ms | 4 - 208 MB | NPU
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- | YOLOv11-Segmentation | TFLITE | float | Qualcomm® SA7255P | 15.378 ms | 4 - 85 MB | NPU
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- | YOLOv11-Segmentation | TFLITE | float | Qualcomm® SA8295P | 9.348 ms | 4 - 177 MB | NPU
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- | YOLOv11-Segmentation | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 2.367 ms | 0 - 95 MB | NPU
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- | YOLOv11-Segmentation | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 1.947 ms | 1 - 106 MB | NPU
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  ## License
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  * The license for the original implementation of YOLOv11-Segmentation can be found
 
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  Ultralytics YOLOv11 is a machine learning model that predicts bounding boxes, segmentation masks and classes of objects in an image.
16
 
17
  This is based on the implementation of YOLOv11-Segmentation found [here](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/yolo/segment).
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+ This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/tree/v0.49.1/qai_hub_models/models/yolov11_seg) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
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  Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.com) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) to run these models on a hosted Qualcomm® device.
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  ## Getting Started
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  Due to licensing restrictions, we cannot distribute pre-exported model assets for this model.
24
+ Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/tree/v0.49.1/qai_hub_models/models/yolov11_seg) Python library to compile and export the model with your own:
25
  - Custom weights (e.g., fine-tuned checkpoints)
26
  - Custom input shapes
27
  - Target device and runtime configurations
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+ See our repository for [YOLOv11-Segmentation on GitHub](https://github.com/qualcomm/ai-hub-models/tree/v0.49.1/qai_hub_models/models/yolov11_seg) for usage instructions.
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  ## Model Details
 
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  ## Performance Summary
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  | Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
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  |---|---|---|---|---|---|---
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+ | YOLOv11-Segmentation | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 2.914 ms | 0 - 234 MB | NPU
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+ | YOLOv11-Segmentation | ONNX | float | Snapdragon® X2 Elite | 3.393 ms | 16 - 16 MB | NPU
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+ | YOLOv11-Segmentation | ONNX | float | Snapdragon® X Elite | 7.168 ms | 17 - 17 MB | NPU
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+ | YOLOv11-Segmentation | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 4.207 ms | 16 - 284 MB | NPU
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+ | YOLOv11-Segmentation | ONNX | float | Qualcomm® QCS8550 (Proxy) | 6.654 ms | 11 - 16 MB | NPU
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+ | YOLOv11-Segmentation | ONNX | float | Qualcomm® QCS9075 | 7.833 ms | 13 - 16 MB | NPU
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+ | YOLOv11-Segmentation | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 3.461 ms | 0 - 228 MB | NPU
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+ | YOLOv11-Segmentation | ONNX | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 2.427 ms | 0 - 90 MB | NPU
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+ | YOLOv11-Segmentation | ONNX | w8a16 | Snapdragon® X2 Elite | 2.665 ms | 6 - 6 MB | NPU
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+ | YOLOv11-Segmentation | ONNX | w8a16 | Snapdragon® X Elite | 6.477 ms | 8 - 8 MB | NPU
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+ | YOLOv11-Segmentation | ONNX | w8a16 | Snapdragon® 8 Gen 3 Mobile | 3.661 ms | 1 - 233 MB | NPU
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+ | YOLOv11-Segmentation | ONNX | w8a16 | Qualcomm® QCS6490 | 416.127 ms | 164 - 169 MB | CPU
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+ | YOLOv11-Segmentation | ONNX | w8a16 | Qualcomm® QCS8550 (Proxy) | 5.88 ms | 5 - 10 MB | NPU
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+ | YOLOv11-Segmentation | ONNX | w8a16 | Qualcomm® QCS9075 | 7.095 ms | 6 - 9 MB | NPU
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+ | YOLOv11-Segmentation | ONNX | w8a16 | Qualcomm® QCM6690 | 219.193 ms | 167 - 176 MB | CPU
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+ | YOLOv11-Segmentation | ONNX | w8a16 | Snapdragon® 8 Elite For Galaxy Mobile | 2.742 ms | 1 - 101 MB | NPU
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+ | YOLOv11-Segmentation | ONNX | w8a16 | Snapdragon® 7 Gen 4 Mobile | 196.905 ms | 99 - 109 MB | CPU
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+ | YOLOv11-Segmentation | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 1.933 ms | 0 - 104 MB | NPU
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+ | YOLOv11-Segmentation | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 3.15 ms | 0 - 114 MB | NPU
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+ | YOLOv11-Segmentation | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 15.371 ms | 4 - 86 MB | NPU
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+ | YOLOv11-Segmentation | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 4.335 ms | 4 - 8 MB | NPU
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+ | YOLOv11-Segmentation | TFLITE | float | Qualcomm® SA8775P | 6.071 ms | 4 - 90 MB | NPU
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+ | YOLOv11-Segmentation | TFLITE | float | Qualcomm® QCS9075 | 5.861 ms | 4 - 22 MB | NPU
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+ | YOLOv11-Segmentation | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 10.077 ms | 4 - 211 MB | NPU
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+ | YOLOv11-Segmentation | TFLITE | float | Qualcomm® SA7255P | 15.371 ms | 4 - 86 MB | NPU
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+ | YOLOv11-Segmentation | TFLITE | float | Qualcomm® SA8295P | 9.371 ms | 4 - 177 MB | NPU
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+ | YOLOv11-Segmentation | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 2.381 ms | 0 - 96 MB | NPU
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  ## License
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  * The license for the original implementation of YOLOv11-Segmentation can be found