--- 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 | 3.585 ms | 0 - 19 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 | 2.618 ms | 0 - 34 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 | 2.456 ms | 0 - 33 MB | INT8 | NPU | Use Export Script | | ConvNext-Tiny-w8a16-Quantized | RB3 Gen 2 (Proxy) | QCS6490 Proxy | QNN | 13.316 ms | 0 - 8 MB | INT8 | NPU | Use Export Script | | ConvNext-Tiny-w8a16-Quantized | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 3.176 ms | 0 - 1 MB | INT8 | NPU | Use Export Script | | ConvNext-Tiny-w8a16-Quantized | SA8255 (Proxy) | SA8255P Proxy | QNN | 3.193 ms | 0 - 2 MB | INT8 | NPU | Use Export Script | | ConvNext-Tiny-w8a16-Quantized | SA8775 (Proxy) | SA8775P Proxy | QNN | 3.188 ms | 0 - 2 MB | INT8 | NPU | Use Export Script | | ConvNext-Tiny-w8a16-Quantized | SA8650 (Proxy) | SA8650P Proxy | QNN | 3.203 ms | 0 - 2 MB | INT8 | NPU | Use Export Script | | ConvNext-Tiny-w8a16-Quantized | SA8295P ADP | SA8295P | QNN | 4.76 ms | 0 - 6 MB | INT8 | NPU | Use Export Script | | ConvNext-Tiny-w8a16-Quantized | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 4.253 ms | 0 - 38 MB | INT8 | NPU | Use Export Script | | ConvNext-Tiny-w8a16-Quantized | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 3.543 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) : 3.6 Estimated peak memory usage (MB): [0, 19] 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:ai-hub-support@qti.qualcomm.com).