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---
datasets:
- COCO
library_name: pytorch
license: apache-2.0
pipeline_tag: object-detection
tags:
- real_time
- quantized
- android
---
# Yolo-NAS-Quantized: Optimized for Mobile Deployment
## Quantized real-time object detection optimized for mobile and edge
YoloNAS 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-NAS-Quantized found [here](https://github.com/Deci-AI/super-gradients).
This repository provides scripts to run Yolo-NAS-Quantized on Qualcomm® devices.
More details on model performance across various devices, can be found
[here](https://aihub.qualcomm.com/models/yolonas_quantized).
### Model Details
- **Model Type:** Object detection
- **Model Stats:**
- Model checkpoint: YoloNAS Small
- Input resolution: 640x640
- Number of parameters: 12.2M
- Model size: 12.1 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 | 4.77 ms | 0 - 193 MB | INT8 | NPU | [Yolo-NAS-Quantized.tflite](https://huggingface.co/qualcomm/Yolo-NAS-Quantized/blob/main/Yolo-NAS-Quantized.tflite)
## Installation
This model can be installed as a Python package via pip.
```bash
pip install "qai-hub-models[yolonas_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.yolonas_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.yolonas_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.yolonas_quantized.export
```
```
Profile Job summary of Yolo-NAS-Quantized
--------------------------------------------------
Device: RB3 Gen 2 (Proxy) (12)
Estimated Inference Time: 13.74 ms
Estimated Peak Memory Range: 0.09-66.41 MB
Compute Units: NPU (203),CPU (1) | Total (204)
```
## Run demo on a cloud-hosted device
You can also run the demo on-device.
```bash
python -m qai_hub_models.models.yolonas_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.yolonas_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-NAS-Quantized's performance across various devices [here](https://aihub.qualcomm.com/models/yolonas_quantized).
Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
## License
- The license for the original implementation of Yolo-NAS-Quantized can be found
[here](https://github.com/Deci-AI/super-gradients/blob/master/LICENSE.md).
- The license for the compiled assets for on-device deployment can be found [here](https://github.com/Deci-AI/super-gradients/blob/master/LICENSE.md)
## References
* [YOLO-NAS by Deci Achieves SOTA Performance on Object Detection Using Neural Architecture Search](https://deci.ai/blog/yolo-nas-object-detection-foundation-model/)
* [Source Model Implementation](https://github.com/Deci-AI/super-gradients)
## 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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