README.md

---
library_name: pytorch
license: other
tags:
- real_time
- android
pipeline_tag: object-detection

---

![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/yolov6/web-assets/model_demo.png)

# Yolo-v6: Optimized for Qualcomm Devices

YoloV6 is a machine learning model that predicts bounding boxes and classes of objects in an image.

This is based on the implementation of Yolo-v6 found [here](https://github.com/meituan/YOLOv6/).
This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/yolov6) 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/quic/ai-hub-models/blob/main/qai_hub_models/models/yolov6) 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-v6 on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/yolov6) for usage instructions.


## Model Details

**Model Type:** Model_use_case.object_detection

**Model Stats:**
- Model checkpoint: YoloV6-N
- Input resolution: 640x640
- Number of parameters: 4.68M
- Model size (float): 17.9 MB
- Model size (w8a8): 4.68 MB
- Model size (w8a16): 5.03 MB

## Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
|---|---|---|---|---|---|---
| Yolo-v6 | ONNX | float | Snapdragon® X Elite | 9.207 ms | 14 - 14 MB | NPU
| Yolo-v6 | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 7.253 ms | 5 - 155 MB | NPU
| Yolo-v6 | ONNX | float | Qualcomm® QCS8550 (Proxy) | 9.931 ms | 0 - 42 MB | NPU
| Yolo-v6 | ONNX | float | Qualcomm® QCS9075 | 9.881 ms | 5 - 7 MB | NPU
| Yolo-v6 | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 5.326 ms | 0 - 122 MB | NPU
| Yolo-v6 | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 4.321 ms | 0 - 122 MB | NPU
| Yolo-v6 | QNN_DLC | float | Snapdragon® X Elite | 6.189 ms | 5 - 5 MB | NPU
| Yolo-v6 | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 4.398 ms | 5 - 187 MB | NPU
| Yolo-v6 | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 16.035 ms | 1 - 154 MB | NPU
| Yolo-v6 | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 6.009 ms | 5 - 9 MB | NPU
| Yolo-v6 | QNN_DLC | float | Qualcomm® SA8775P | 7.582 ms | 0 - 158 MB | NPU
| Yolo-v6 | QNN_DLC | float | Qualcomm® QCS9075 | 7.695 ms | 5 - 11 MB | NPU
| Yolo-v6 | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 8.966 ms | 5 - 185 MB | NPU
| Yolo-v6 | QNN_DLC | float | Qualcomm® SA7255P | 16.035 ms | 1 - 154 MB | NPU
| Yolo-v6 | QNN_DLC | float | Qualcomm® SA8295P | 9.042 ms | 0 - 152 MB | NPU
| Yolo-v6 | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 3.334 ms | 0 - 155 MB | NPU
| Yolo-v6 | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 3.002 ms | 5 - 161 MB | NPU
| Yolo-v6 | QNN_DLC | w8a16 | Snapdragon® X Elite | 2.547 ms | 2 - 2 MB | NPU
| Yolo-v6 | QNN_DLC | w8a16 | Snapdragon® 8 Gen 3 Mobile | 1.514 ms | 2 - 61 MB | NPU
| Yolo-v6 | QNN_DLC | w8a16 | Qualcomm® QCS6490 | 6.678 ms | 2 - 6 MB | NPU
| Yolo-v6 | QNN_DLC | w8a16 | Qualcomm® QCS8275 (Proxy) | 5.356 ms | 0 - 41 MB | NPU
| Yolo-v6 | QNN_DLC | w8a16 | Qualcomm® QCS8550 (Proxy) | 2.247 ms | 2 - 4 MB | NPU
| Yolo-v6 | QNN_DLC | w8a16 | Qualcomm® SA8775P | 11.103 ms | 0 - 39 MB | NPU
| Yolo-v6 | QNN_DLC | w8a16 | Qualcomm® QCS9075 | 2.545 ms | 2 - 6 MB | NPU
| Yolo-v6 | QNN_DLC | w8a16 | Qualcomm® QCM6690 | 18.582 ms | 2 - 153 MB | NPU
| Yolo-v6 | QNN_DLC | w8a16 | Qualcomm® QCS8450 (Proxy) | 2.806 ms | 2 - 58 MB | NPU
| Yolo-v6 | QNN_DLC | w8a16 | Qualcomm® SA7255P | 5.356 ms | 0 - 41 MB | NPU
| Yolo-v6 | QNN_DLC | w8a16 | Qualcomm® SA8295P | 3.493 ms | 0 - 38 MB | NPU
| Yolo-v6 | QNN_DLC | w8a16 | Snapdragon® 8 Elite For Galaxy Mobile | 1.138 ms | 2 - 43 MB | NPU
| Yolo-v6 | QNN_DLC | w8a16 | Snapdragon® 7 Gen 4 Mobile | 2.73 ms | 2 - 153 MB | NPU
| Yolo-v6 | QNN_DLC | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 0.944 ms | 2 - 42 MB | NPU
| Yolo-v6 | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 11.892 ms | 0 - 70 MB | GPU
| Yolo-v6 | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 59.006 ms | 0 - 52 MB | GPU
| Yolo-v6 | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 15.686 ms | 0 - 38 MB | GPU
| Yolo-v6 | TFLITE | float | Qualcomm® SA8775P | 26.538 ms | 0 - 56 MB | GPU
| Yolo-v6 | TFLITE | float | Qualcomm® QCS9075 | 7.869 ms | 0 - 18 MB | NPU
| Yolo-v6 | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 23.049 ms | 0 - 78 MB | GPU
| Yolo-v6 | TFLITE | float | Qualcomm® SA7255P | 59.006 ms | 0 - 52 MB | GPU
| Yolo-v6 | TFLITE | float | Qualcomm® SA8295P | 20.129 ms | 0 - 57 MB | GPU
| Yolo-v6 | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 3.807 ms | 0 - 163 MB | NPU
| Yolo-v6 | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 3.381 ms | 0 - 166 MB | NPU

## License
* The license for the original implementation of Yolo-v6 can be found
  [here](https://github.com/meituan/YOLOv6/blob/47625514e7480706a46ff3c0cd0252907ac12f22/LICENSE).

## References
* [YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications](https://arxiv.org/abs/2209.02976)
* [Source Model Implementation](https://github.com/meituan/YOLOv6/)

## 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).