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library_name: pytorch
license: other
tags:
- bu_auto
- real_time
- android
pipeline_tag: object-detection
---
# Yolo-R: Optimized for Qualcomm Devices
YoloR is a machine learning model that predicts bounding boxes and classes of objects in an image.
This is based on the implementation of Yolo-R found [here](https://github.com/WongKinYiu/yolor).
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/v0.59.0/src/qai_hub_models/models/yolor) 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/v0.59.0/src/qai_hub_models/models/yolor) 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 [Yolo-R on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.59.0/src/qai_hub_models/models/yolor) for usage instructions.
## Model Details
**Model Type:** Model_use_case.object_detection
**Model Stats:**
- Model checkpoint: yolor_p6
- Input resolution: 640x640
- Number of parameters: 4.68M
- Model size (float): 17.9 MB
## Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
|---|---|---|---|---|---|---
| Yolo-R | ONNX | float | Snapdragon® X2 Elite | 26.79 ms | 5 - 5 MB | NPU
| Yolo-R | ONNX | float | Snapdragon® X Elite | 40.751 ms | 75 - 75 MB | NPU
| Yolo-R | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 31.447 ms | 0 - 331 MB | NPU
| Yolo-R | ONNX | float | Snapdragon® 8 Gen 1 Mobile | 92.147 ms | 6 - 524 MB | NPU
| Yolo-R | ONNX | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 40.026 ms | 5 - 8 MB | NPU
| Yolo-R | ONNX | float | Qualcomm® QCS8450 | 92.147 ms | 6 - 524 MB | NPU
| Yolo-R | ONNX | float | Qualcomm® Dragonwing™ IQ-9075 | 47.694 ms | 5 - 12 MB | NPU
| Yolo-R | ONNX | float | Qualcomm® Dragonwing™ IQ-X7181 | 40.751 ms | 75 - 75 MB | NPU
| Yolo-R | ONNX | float | Qualcomm® Dragonwing™ Q-8750 | 28.044 ms | 3 - 233 MB | NPU
| Yolo-R | ONNX | float | Snapdragon® 8 Elite Mobile | 28.044 ms | 3 - 233 MB | NPU
| Yolo-R | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 27.585 ms | 3 - 295 MB | NPU
| Yolo-R | ONNX | w8a16 | Snapdragon® X2 Elite | 19.175 ms | 2 - 2 MB | NPU
| Yolo-R | ONNX | w8a16 | Snapdragon® X Elite | 30.831 ms | 41 - 41 MB | NPU
| Yolo-R | ONNX | w8a16 | Snapdragon® 8 Gen 3 Mobile | 20.922 ms | 1 - 464 MB | NPU
| Yolo-R | ONNX | w8a16 | Snapdragon® 8 Gen 1 Mobile | 42.424 ms | 3 - 461 MB | NPU
| Yolo-R | ONNX | w8a16 | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 29.41 ms | 2 - 6 MB | NPU
| Yolo-R | ONNX | w8a16 | Qualcomm® QCS8450 | 42.424 ms | 3 - 461 MB | NPU
| Yolo-R | ONNX | w8a16 | Qualcomm® Dragonwing™ IQ-9075 | 30.957 ms | 1 - 6 MB | NPU
| Yolo-R | ONNX | w8a16 | Qualcomm® Dragonwing™ IQ-X7181 | 30.831 ms | 41 - 41 MB | NPU
| Yolo-R | ONNX | w8a16 | Qualcomm® Dragonwing™ Q-8750 | 17.867 ms | 2 - 365 MB | NPU
| Yolo-R | ONNX | w8a16 | Snapdragon® 8 Elite Mobile | 17.867 ms | 2 - 365 MB | NPU
| Yolo-R | ONNX | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 18.762 ms | 1 - 425 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Snapdragon® X2 Elite | 8.641 ms | 2 - 2 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Snapdragon® X Elite | 19.509 ms | 2 - 2 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Snapdragon® 8 Gen 3 Mobile | 12.447 ms | 2 - 356 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Snapdragon® 8 Gen 1 Mobile | 28.046 ms | 2 - 358 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ QCS6490 | 91.189 ms | 2 - 7 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ QCS8275 | 37.802 ms | 2 - 291 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 18.513 ms | 2 - 4 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Qualcomm® SA8775P | 18.744 ms | 1 - 291 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Qualcomm® SA8650P | 18.744 ms | 1 - 291 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Qualcomm® SA8255P | 18.744 ms | 1 - 291 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Qualcomm® QCS8450 | 28.046 ms | 2 - 358 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ IQ-9075 | 18.861 ms | 0 - 4 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ IQ-X7181 | 19.509 ms | 2 - 2 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ Q-6690 | 219.672 ms | 2 - 399 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ Q-7790 | 25.965 ms | 2 - 309 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ Q-8750 | 9.391 ms | 2 - 304 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Qualcomm® SA7255P | 37.802 ms | 2 - 291 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Qualcomm® SA8295P | 24.277 ms | 0 - 292 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Snapdragon® 8 Elite Mobile | 9.391 ms | 2 - 304 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 7.859 ms | 2 - 310 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Snapdragon® 7 Gen 4 Mobile | 25.965 ms | 2 - 309 MB | NPU
## License
* The license for the original implementation of Yolo-R can be found
[here](https://github.com/WongKinYiu/yolor/blob/main/LICENSE).
## References
* [You Only Learn One Representation: Unified Network for Multiple Tasks](https://arxiv.org/abs/2105.04206)
* [Source Model Implementation](https://github.com/WongKinYiu/yolor)
## 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).
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