---
library_name: pytorch
license: gpl-3.0
pipeline_tag: object-detection
tags:
- real_time
- quantized
- android

---

![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/yolov7_quantized/web-assets/model_demo.png)

# Yolo-v7-Quantized: Optimized for Mobile Deployment
## Quantized real-time object detection optimized for mobile and edge

YoloV7 is a machine learning model that predicts bounding boxes and classes of objects in an image. This model is post-training quantized to int8 using samples from the COCO dataset.

This model is an implementation of Yolo-v7-Quantized found [here](https://github.com/WongKinYiu/yolov7/).
This repository provides scripts to run Yolo-v7-Quantized on Qualcomm® devices.
More details on model performance across various devices, can be found
[here](https://aihub.qualcomm.com/models/yolov7_quantized).


### Model Details

- **Model Type:** Object detection
- **Model Stats:**
  - Model checkpoint: YoloV7 Tiny
  - Input resolution: 720p (720x1280)
  - Number of parameters: 6.24M
  - Model size: 6.23 MB




| Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
| ---|---|---|---|---|---|---|---|
| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 4.596 ms | 0 - 2 MB | INT8 | NPU |  [Yolo-v7-Quantized.tflite](https://huggingface.co/qualcomm/Yolo-v7-Quantized/blob/main/Yolo-v7-Quantized.tflite) 



## Installation

This model can be installed as a Python package via pip.

```bash
pip install "qai-hub-models[yolov7_quantized]"
```



## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device

Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your
Qualcomm® ID. Once signed in navigate to `Account -> Settings -> API Token`.

With this API token, you can configure your client to run models on the cloud
hosted devices.
```bash
qai-hub configure --api_token API_TOKEN
```
Navigate to [docs](https://app.aihub.qualcomm.com/docs/) for more information.



## Demo off target

The package contains a simple end-to-end demo that downloads pre-trained
weights and runs this model on a sample input.

```bash
python -m qai_hub_models.models.yolov7_quantized.demo
```

The above demo runs a reference implementation of pre-processing, model
inference, and post processing.

**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
environment, please add the following to your cell (instead of the above).
```
%run -m qai_hub_models.models.yolov7_quantized.demo
```


### Run model on a cloud-hosted device

In addition to the demo, you can also run the model on a cloud-hosted Qualcomm®
device. This script does the following:
* Performance check on-device on a cloud-hosted device
* Downloads compiled assets that can be deployed on-device for Android.
* Accuracy check between PyTorch and on-device outputs.

```bash
python -m qai_hub_models.models.yolov7_quantized.export
```

```
Profile Job summary of Yolo-v7-Quantized
--------------------------------------------------
Device: RB5 (Proxy) (12)
Estimated Inference Time: 93.32 ms
Estimated Peak Memory Range: 8.36-44.24 MB
Compute Units: NPU (32),GPU (126),CPU (68) | Total (226)


```




## Run demo on a cloud-hosted device

You can also run the demo on-device.

```bash
python -m qai_hub_models.models.yolov7_quantized.demo --on-device
```

**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
environment, please add the following to your cell (instead of the above).
```
%run -m qai_hub_models.models.yolov7_quantized.demo -- --on-device
```


## Deploying compiled model to Android


The models can be deployed using multiple runtimes:
- TensorFlow Lite (`.tflite` export): [This
  tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a
  guide to deploy the .tflite model in an Android application.


- QNN (`.so` export ): This [sample
  app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html)
provides instructions on how to use the `.so` shared library  in an Android application.


## View on Qualcomm® AI Hub
Get more details on Yolo-v7-Quantized's performance across various devices [here](https://aihub.qualcomm.com/models/yolov7_quantized).
Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)

## License
- The license for the original implementation of Yolo-v7-Quantized can be found
  [here](https://github.com/WongKinYiu/yolov7/blob/main/LICENSE.md).
- The license for the compiled assets for on-device deployment can be found [here](https://github.com/WongKinYiu/yolov7/blob/main/LICENSE.md)

## References
* [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://arxiv.org/abs/2207.02696)
* [Source Model Implementation](https://github.com/WongKinYiu/yolov7/)

## Community
* Join [our AI Hub Slack community](https://qualcomm-ai-hub.slack.com/join/shared_invite/zt-2d5zsmas3-Sj0Q9TzslueCjS31eXG2UA#/shared-invite/email) 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).