v0.50.2

See https://github.com/qualcomm/ai-hub-models/releases/v0.50.2 for changelog.

LICENSE

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+The license of the original trained model can be found at https://github.com/ultralytics/ultralytics/blob/main/LICENSE.

README.md

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+---
+library_name: pytorch
+license: other
+tags:
+- real_time
+- android
+pipeline_tag: image-segmentation
+
+---
+
+![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/yolo26_seg/web-assets/model_demo.png)
+
+# YOLO26-Segmentation: Optimized for Qualcomm Devices
+
+Ultralytics YOLO26 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 YOLO26-Segmentation found [here](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/yolo/segment).
+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/src/qai_hub_models/models/yolo26_seg) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
+
+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.
+
+## Getting Started
+Due to licensing restrictions, we cannot distribute pre-exported model assets for this model.
+Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/main/src/qai_hub_models/models/yolo26_seg) 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 [YOLO26-Segmentation on GitHub](https://github.com/qualcomm/ai-hub-models/blob/main/src/qai_hub_models/models/yolo26_seg) for usage instructions.
+
+
+## Model Details
+
+**Model Type:** Model_use_case.semantic_segmentation
+
+**Model Stats:**
+- Model checkpoint: YOLO26N-Seg
+- Input resolution: 640x640
+- Number of output classes: 80
+- Number of parameters: 2.7M
+- Model size (float): 9.29 MB
+- Model size (w8a16): 5.25 MB
+
+## Performance Summary
+| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
+|---|---|---|---|---|---|---
+| YOLO26-Segmentation | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 4.074 ms | 15 - 189 MB | NPU
+| YOLO26-Segmentation | ONNX | float | Snapdragon® X2 Elite | 3.916 ms | 15 - 15 MB | NPU
+| YOLO26-Segmentation | ONNX | float | Snapdragon® X Elite | 7.312 ms | 17 - 17 MB | NPU
+| YOLO26-Segmentation | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 4.75 ms | 16 - 228 MB | NPU
+| YOLO26-Segmentation | ONNX | float | Qualcomm® QCS8550 (Proxy) | 6.886 ms | 11 - 20 MB | NPU
+| YOLO26-Segmentation | ONNX | float | Qualcomm® QCS9075 | 9.957 ms | 12 - 15 MB | NPU
+| YOLO26-Segmentation | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 4.552 ms | 11 - 185 MB | NPU
+| YOLO26-Segmentation | ONNX | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 2.642 ms | 0 - 94 MB | NPU
+| YOLO26-Segmentation | ONNX | w8a16 | Snapdragon® X2 Elite | 2.874 ms | 6 - 6 MB | NPU
+| YOLO26-Segmentation | ONNX | w8a16 | Snapdragon® X Elite | 6.903 ms | 7 - 7 MB | NPU
+| YOLO26-Segmentation | ONNX | w8a16 | Snapdragon® 8 Gen 3 Mobile | 3.883 ms | 9 - 247 MB | NPU
+| YOLO26-Segmentation | ONNX | w8a16 | Qualcomm® QCS6490 | 444.357 ms | 171 - 176 MB | CPU
+| YOLO26-Segmentation | ONNX | w8a16 | Qualcomm® QCS8550 (Proxy) | 6.282 ms | 8 - 13 MB | NPU
+| YOLO26-Segmentation | ONNX | w8a16 | Qualcomm® QCS9075 | 7.143 ms | 8 - 11 MB | NPU
+| YOLO26-Segmentation | ONNX | w8a16 | Qualcomm® QCM6690 | 215.887 ms | 170 - 181 MB | CPU
+| YOLO26-Segmentation | ONNX | w8a16 | Snapdragon® 8 Elite For Galaxy Mobile | 2.984 ms | 0 - 93 MB | NPU
+| YOLO26-Segmentation | ONNX | w8a16 | Snapdragon® 7 Gen 4 Mobile | 200.226 ms | 105 - 115 MB | CPU
+| YOLO26-Segmentation | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 2.603 ms | 0 - 169 MB | NPU
+| YOLO26-Segmentation | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 3.859 ms | 3 - 196 MB | NPU
+| YOLO26-Segmentation | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 16.925 ms | 4 - 167 MB | NPU
+| YOLO26-Segmentation | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 5.174 ms | 4 - 7 MB | NPU
+| YOLO26-Segmentation | TFLITE | float | Qualcomm® SA8775P | 6.776 ms | 4 - 170 MB | NPU
+| YOLO26-Segmentation | TFLITE | float | Qualcomm® QCS9075 | 7.871 ms | 4 - 23 MB | NPU
+| YOLO26-Segmentation | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 12.002 ms | 4 - 213 MB | NPU
+| YOLO26-Segmentation | TFLITE | float | Qualcomm® SA7255P | 16.925 ms | 4 - 167 MB | NPU
+| YOLO26-Segmentation | TFLITE | float | Qualcomm® SA8295P | 11.058 ms | 5 - 184 MB | NPU
+| YOLO26-Segmentation | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 3.224 ms | 0 - 171 MB | NPU
+
+## License
+* The license for the original implementation of YOLO26-Segmentation can be found
+  [here](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).
+
+## References
+* [Ultralytics YOLO26 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://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
+* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).