README.md

---
library_name: pytorch
license: bsd-3-clause
pipeline_tag: image-classification
tags:
- quantized
- android

---

![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/convnext_tiny_w8a16_quantized/web-assets/model_demo.png)

# ConvNext-Tiny-w8a16-Quantized: Optimized for Mobile Deployment
## Imagenet classifier and general purpose backbone


ConvNextTiny is a machine learning model that can classify images from the Imagenet dataset. It can also be used as a backbone in building more complex models for specific use cases.

This model is an implementation of ConvNext-Tiny-w8a16-Quantized found [here](https://github.com/pytorch/vision/blob/main/torchvision/models/convnext.py).


This repository provides scripts to run ConvNext-Tiny-w8a16-Quantized on Qualcomm® devices.
More details on model performance across various devices, can be found
[here](https://aihub.qualcomm.com/models/convnext_tiny_w8a16_quantized).


### Model Details

- **Model Type:** Image classification
- **Model Stats:**
  - Model checkpoint: Imagenet
  - Input resolution: 224x224
  - Number of parameters: 28.6M
  - Model size: 28 MB
  - Precision: w8a16 (8-bit weights, 16-bit activations)

| Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
|---|---|---|---|---|---|---|---|---|
| ConvNext-Tiny-w8a16-Quantized | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 3.429 ms | 0 - 136 MB | INT8 | NPU | [ConvNext-Tiny-w8a16-Quantized.so](https://huggingface.co/qualcomm/ConvNext-Tiny-w8a16-Quantized/blob/main/ConvNext-Tiny-w8a16-Quantized.so) |
| ConvNext-Tiny-w8a16-Quantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 2.473 ms | 0 - 37 MB | INT8 | NPU | [ConvNext-Tiny-w8a16-Quantized.so](https://huggingface.co/qualcomm/ConvNext-Tiny-w8a16-Quantized/blob/main/ConvNext-Tiny-w8a16-Quantized.so) |
| ConvNext-Tiny-w8a16-Quantized | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 2.431 ms | 0 - 36 MB | INT8 | NPU | Use Export Script |
| ConvNext-Tiny-w8a16-Quantized | RB3 Gen 2 (Proxy) | QCS6490 Proxy | QNN | 13.088 ms | 0 - 7 MB | INT8 | NPU | Use Export Script |
| ConvNext-Tiny-w8a16-Quantized | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 3.096 ms | 0 - 2 MB | INT8 | NPU | Use Export Script |
| ConvNext-Tiny-w8a16-Quantized | SA7255P ADP | SA7255P | QNN | 26.821 ms | 0 - 10 MB | INT8 | NPU | Use Export Script |
| ConvNext-Tiny-w8a16-Quantized | SA8255 (Proxy) | SA8255P Proxy | QNN | 3.095 ms | 0 - 1 MB | INT8 | NPU | Use Export Script |
| ConvNext-Tiny-w8a16-Quantized | SA8295P ADP | SA8295P | QNN | 4.658 ms | 0 - 6 MB | INT8 | NPU | Use Export Script |
| ConvNext-Tiny-w8a16-Quantized | SA8650 (Proxy) | SA8650P Proxy | QNN | 3.108 ms | 0 - 2 MB | INT8 | NPU | Use Export Script |
| ConvNext-Tiny-w8a16-Quantized | SA8775P ADP | SA8775P | QNN | 4.446 ms | 0 - 6 MB | INT8 | NPU | Use Export Script |
| ConvNext-Tiny-w8a16-Quantized | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 4.258 ms | 0 - 38 MB | INT8 | NPU | Use Export Script |
| ConvNext-Tiny-w8a16-Quantized | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 3.38 ms | 0 - 0 MB | INT8 | NPU | Use Export Script |




## Installation

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

```bash
pip install "qai-hub-models[convnext_tiny_w8a16_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.convnext_tiny_w8a16_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.convnext_tiny_w8a16_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.convnext_tiny_w8a16_quantized.export
```
```
Profiling Results
------------------------------------------------------------
ConvNext-Tiny-w8a16-Quantized
Device                          : Samsung Galaxy S23 (13)
Runtime                         : QNN                    
Estimated inference time (ms)   : 3.4                    
Estimated peak memory usage (MB): [0, 136]               
Total # Ops                     : 215                    
Compute Unit(s)                 : NPU (215 ops)          
```




## Run demo on a cloud-hosted device

You can also run the demo on-device.

```bash
python -m qai_hub_models.models.convnext_tiny_w8a16_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.convnext_tiny_w8a16_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 ConvNext-Tiny-w8a16-Quantized's performance across various devices [here](https://aihub.qualcomm.com/models/convnext_tiny_w8a16_quantized).
Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)


## License
* The license for the original implementation of ConvNext-Tiny-w8a16-Quantized can be found [here](https://github.com/pytorch/vision/blob/main/LICENSE).
* The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf)



## References
* [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545)
* [Source Model Implementation](https://github.com/pytorch/vision/blob/main/torchvision/models/convnext.py)



## 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:[email protected]).