---
title: Yolo-R
canonical_url: "https://www.modelscope.cn/models/Qualcomm-AI-Hub/Yolo-R"
md_url: "https://www.modelscope.cn/models/Qualcomm-AI-Hub/Yolo-R.md"
repository: Qualcomm-AI-Hub/Yolo-R
last_updated: 2026-10-16
license: gpl-3.0
downloads: 0
stars: 0
tags:
  - bu_auto
  - real_time
  - android
---

# Yolo-R

> Yolo-R - Qualcomm-AI-Hub 在 ModelScope 开源的模型。Yolo-R: Optimized for Qualcomm Devices

Qualcomm-AI-Hub/Yolo-R 是 ModelScope 魔搭社区上的机器学习模型，采用 gpl-3.0 许可。

- **Repository**: Qualcomm-AI-Hub/Yolo-R
- **License**: gpl-3.0
- **Tags**: bu_auto, real_time, android
- **Downloads**: 0
- **Stars**: 0
- **Last updated**: 2026-10-16

Source: https://www.modelscope.cn/models/Qualcomm-AI-Hub/Yolo-R

---

![](https://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/qai-hub-models/models/yolor/web-assets/model_demo.png)

# 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-cn/blob/main/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.cn) 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-cn/blob/main/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-cn/blob/main/src/qai_hub_models/models/yolor) for usage instructions.


## Model Details

**Model Type:** Model_use_case.object_detection

**Model Stats:**
- Input resolution: 640x640
- Model checkpoint: yolor_p6
- Model size (float): 17.9 MB
- Number of parameters: 4.68M

## Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
|---|---|---|---|---|---|---
| Yolo-R | ONNX | float | Snapdragon® X2 Elite | 27.222 ms | 5 - 5 MB | NPU
| Yolo-R | ONNX | float | Snapdragon® X Elite | 45.796 ms | 72 - 72 MB | NPU
| Yolo-R | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 35.091 ms | 1 - 249 MB | NPU
| Yolo-R | ONNX | float | Snapdragon® 8 Gen 1 Mobile | 72.384 ms | 1 - 311 MB | NPU
| Yolo-R | ONNX | float | Qualcomm® Dragonwing™ IQ-8275 | 54.017 ms | 5 - 13 MB | NPU
| Yolo-R | ONNX | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 45.68 ms | 5 - 444 MB | NPU
| Yolo-R | ONNX | float | Qualcomm® QCS8450 | 72.384 ms | 1 - 311 MB | NPU
| Yolo-R | ONNX | float | Qualcomm® Dragonwing™ IQ-9075 | 51.648 ms | 5 - 12 MB | NPU
| Yolo-R | ONNX | float | Qualcomm® Dragonwing™ IQ-X7181 | 45.796 ms | 72 - 72 MB | NPU
| Yolo-R | ONNX | float | Qualcomm® Dragonwing™ Q-8750 | 30.266 ms | 2 - 196 MB | NPU
| Yolo-R | ONNX | float | Snapdragon® 8 Elite Mobile | 30.266 ms | 2 - 196 MB | NPU
| Yolo-R | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 27.816 ms | 3 - 202 MB | NPU
| Yolo-R | ONNX | w8a16 | Snapdragon® X2 Elite | 15.382 ms | 2 - 2 MB | NPU
| Yolo-R | ONNX | w8a16 | Snapdragon® X Elite | 23.73 ms | 37 - 37 MB | NPU
| Yolo-R | ONNX | w8a16 | Snapdragon® 8 Gen 3 Mobile | 15.901 ms | 0 - 398 MB | NPU
| Yolo-R | ONNX | w8a16 | Snapdragon® 8 Gen 1 Mobile | 32.195 ms | 3 - 405 MB | NPU
| Yolo-R | ONNX | w8a16 | Qualcomm® Dragonwing™ IQ-8275 | 21.601 ms | 1 - 7 MB | NPU
| Yolo-R | ONNX | w8a16 | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 22.789 ms | 0 - 47 MB | NPU
| Yolo-R | ONNX | w8a16 | Qualcomm® QCS8450 | 32.195 ms | 3 - 405 MB | NPU
| Yolo-R | ONNX | w8a16 | Qualcomm® Dragonwing™ IQ-9075 | 23.321 ms | 1 - 6 MB | NPU
| Yolo-R | ONNX | w8a16 | Qualcomm® Dragonwing™ IQ-X7181 | 23.73 ms | 37 - 37 MB | NPU
| Yolo-R | ONNX | w8a16 | Qualcomm® Dragonwing™ Q-7790 | 47.307 ms | 3 - 320 MB | NPU
| Yolo-R | ONNX | w8a16 | Qualcomm® Dragonwing™ Q-8750 | 13.573 ms | 1 - 334 MB | NPU
| Yolo-R | ONNX | w8a16 | Snapdragon® 8 Elite Mobile | 13.573 ms | 1 - 334 MB | NPU
| Yolo-R | ONNX | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 14.619 ms | 1 - 342 MB | NPU
| Yolo-R | ONNX | w8a16 | Snapdragon® 7 Gen 4 Mobile | 47.307 ms | 3 - 320 MB | NPU
| Yolo-R | QNN_DLC | float | Snapdragon® X2 Elite | 18.205 ms | 5 - 5 MB | NPU
| Yolo-R | QNN_DLC | float | Snapdragon® X Elite | 51.659 ms | 5 - 5 MB | NPU
| Yolo-R | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 40.865 ms | 0 - 253 MB | NPU
| Yolo-R | QNN_DLC | float | Snapdragon® 8 Gen 1 Mobile | 65.516 ms | 5 - 312 MB | NPU
| Yolo-R | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-8275 | 58.207 ms | 5 - 12 MB | NPU
| Yolo-R | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 55.198 ms | 5 - 406 MB | NPU
| Yolo-R | QNN_DLC | float | Qualcomm® SA8775P | 61.374 ms | 1 - 197 MB | NPU
| Yolo-R | QNN_DLC | float | Qualcomm® SA8650P | 61.374 ms | 1 - 197 MB | NPU
| Yolo-R | QNN_DLC | float | Qualcomm® SA8255P | 61.374 ms | 1 - 197 MB | NPU
| Yolo-R | QNN_DLC | float | Qualcomm® QCS8450 | 65.516 ms | 5 - 312 MB | NPU
| Yolo-R | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 61.449 ms | 7 - 13 MB | NPU
| Yolo-R | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 51.659 ms | 5 - 5 MB | NPU
| Yolo-R | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 22.853 ms | 5 - 199 MB | NPU
| Yolo-R | QNN_DLC | float | Qualcomm® SA7255P | 124.404 ms | 1 - 198 MB | NPU
| Yolo-R | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 22.853 ms | 5 - 199 MB | NPU
| Yolo-R | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 19.092 ms | 5 - 211 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Snapdragon® X2 Elite | 7.053 ms | 2 - 2 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Snapdragon® X Elite | 16.333 ms | 2 - 2 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Snapdragon® 8 Gen 3 Mobile | 10.015 ms | 2 - 382 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Snapdragon® 8 Gen 1 Mobile | 25.198 ms | 2 - 382 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ QCS6490 | 65.097 ms | 2 - 7 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ IQ-8275 | 13.954 ms | 2 - 7 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 15.338 ms | 2 - 4 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Qualcomm® SA8775P | 15.488 ms | 1 - 310 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Qualcomm® SA8650P | 15.488 ms | 1 - 310 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Qualcomm® SA8255P | 15.488 ms | 1 - 310 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Qualcomm® QCS8450 | 25.198 ms | 2 - 382 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ IQ-9075 | 15.889 ms | 4 - 8 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ IQ-X7181 | 16.333 ms | 2 - 2 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ Q-6690 | 165.426 ms | 2 - 350 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ Q-7790 | 21.262 ms | 2 - 326 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ Q-8750 | 7.358 ms | 2 - 320 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Qualcomm® SA7255P | 32.554 ms | 1 - 311 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Qualcomm® SA8295P | 20.795 ms | 0 - 312 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Snapdragon® 8 Elite Mobile | 7.358 ms | 2 - 320 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 5.854 ms | 2 - 326 MB | NPU
| Yolo-R | QNN_DLC | w8a16 | Snapdragon® 7 Gen 4 Mobile | 21.262 ms | 2 - 326 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
* For questions or feedback, chat with our engineers in [Qualcomm® AI Hub Workbench](https://workbench.aihub.qualcomm.cn).