--- datasets: - imagenet-1k - imagenet-22k library_name: pytorch license: bsd-3-clause pipeline_tag: image-classification tags: - backbone - quantized - android --- ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet50_quantized/web-assets/model_demo.png) # ResNet50Quantized: Optimized for Mobile Deployment ## Imagenet classifier and general purpose backbone ResNet50 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 ResNet50Quantized found [here]({source_repo}). This repository provides scripts to run ResNet50Quantized on Qualcomm® devices. More details on model performance across various devices, can be found [here](https://aihub.qualcomm.com/models/resnet50_quantized). ### Model Details - **Model Type:** Image classification - **Model Stats:** - Model checkpoint: Imagenet - Input resolution: 224x224 - Number of parameters: 25.5M - Model size: 25.1 MB | Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model |---|---|---|---|---|---|---|---|---| | ResNet50Quantized | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 0.788 ms | 0 - 11 MB | INT8 | NPU | [ResNet50Quantized.tflite](https://huggingface.co/qualcomm/ResNet50Quantized/blob/main/ResNet50Quantized.tflite) | | ResNet50Quantized | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 1.001 ms | 0 - 32 MB | INT8 | NPU | [ResNet50Quantized.so](https://huggingface.co/qualcomm/ResNet50Quantized/blob/main/ResNet50Quantized.so) | | ResNet50Quantized | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 1.526 ms | 0 - 30 MB | INT8 | NPU | [ResNet50Quantized.onnx](https://huggingface.co/qualcomm/ResNet50Quantized/blob/main/ResNet50Quantized.onnx) | | ResNet50Quantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 0.584 ms | 0 - 63 MB | INT8 | NPU | [ResNet50Quantized.tflite](https://huggingface.co/qualcomm/ResNet50Quantized/blob/main/ResNet50Quantized.tflite) | | ResNet50Quantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 0.749 ms | 0 - 15 MB | INT8 | NPU | [ResNet50Quantized.so](https://huggingface.co/qualcomm/ResNet50Quantized/blob/main/ResNet50Quantized.so) | | ResNet50Quantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 1.128 ms | 0 - 93 MB | INT8 | NPU | [ResNet50Quantized.onnx](https://huggingface.co/qualcomm/ResNet50Quantized/blob/main/ResNet50Quantized.onnx) | | ResNet50Quantized | RB3 Gen 2 (Proxy) | QCS6490 Proxy | TFLITE | 2.827 ms | 0 - 27 MB | INT8 | NPU | [ResNet50Quantized.tflite](https://huggingface.co/qualcomm/ResNet50Quantized/blob/main/ResNet50Quantized.tflite) | | ResNet50Quantized | RB3 Gen 2 (Proxy) | QCS6490 Proxy | QNN | 4.072 ms | 0 - 8 MB | INT8 | NPU | Use Export Script | | ResNet50Quantized | RB5 (Proxy) | QCS8250 Proxy | TFLITE | 11.444 ms | 0 - 7 MB | INT8 | NPU | [ResNet50Quantized.tflite](https://huggingface.co/qualcomm/ResNet50Quantized/blob/main/ResNet50Quantized.tflite) | | ResNet50Quantized | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 0.785 ms | 0 - 3 MB | INT8 | NPU | [ResNet50Quantized.tflite](https://huggingface.co/qualcomm/ResNet50Quantized/blob/main/ResNet50Quantized.tflite) | | ResNet50Quantized | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 0.943 ms | 0 - 1 MB | INT8 | NPU | Use Export Script | | ResNet50Quantized | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 0.782 ms | 0 - 15 MB | INT8 | NPU | [ResNet50Quantized.tflite](https://huggingface.co/qualcomm/ResNet50Quantized/blob/main/ResNet50Quantized.tflite) | | ResNet50Quantized | SA8255 (Proxy) | SA8255P Proxy | QNN | 0.947 ms | 0 - 2 MB | INT8 | NPU | Use Export Script | | ResNet50Quantized | SA8775 (Proxy) | SA8775P Proxy | TFLITE | 0.787 ms | 0 - 16 MB | INT8 | NPU | [ResNet50Quantized.tflite](https://huggingface.co/qualcomm/ResNet50Quantized/blob/main/ResNet50Quantized.tflite) | | ResNet50Quantized | SA8775 (Proxy) | SA8775P Proxy | QNN | 0.948 ms | 0 - 2 MB | INT8 | NPU | Use Export Script | | ResNet50Quantized | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 0.909 ms | 0 - 64 MB | INT8 | NPU | [ResNet50Quantized.tflite](https://huggingface.co/qualcomm/ResNet50Quantized/blob/main/ResNet50Quantized.tflite) | | ResNet50Quantized | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 1.135 ms | 0 - 18 MB | INT8 | NPU | Use Export Script | | ResNet50Quantized | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 0.518 ms | 0 - 23 MB | INT8 | NPU | [ResNet50Quantized.tflite](https://huggingface.co/qualcomm/ResNet50Quantized/blob/main/ResNet50Quantized.tflite) | | ResNet50Quantized | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 0.693 ms | 0 - 17 MB | INT8 | NPU | Use Export Script | | ResNet50Quantized | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 0.918 ms | 0 - 39 MB | INT8 | NPU | [ResNet50Quantized.onnx](https://huggingface.co/qualcomm/ResNet50Quantized/blob/main/ResNet50Quantized.onnx) | | ResNet50Quantized | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 1.009 ms | 0 - 0 MB | INT8 | NPU | Use Export Script | | ResNet50Quantized | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 1.569 ms | 28 - 28 MB | INT8 | NPU | [ResNet50Quantized.onnx](https://huggingface.co/qualcomm/ResNet50Quantized/blob/main/ResNet50Quantized.onnx) | ## Installation This model can be installed as a Python package via pip. ```bash pip install qai-hub-models ``` ## 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.resnet50_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.resnet50_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.resnet50_quantized.export ``` ``` Profiling Results ------------------------------------------------------------ ResNet50Quantized Device : Samsung Galaxy S23 (13) Runtime : TFLITE Estimated inference time (ms) : 0.8 Estimated peak memory usage (MB): [0, 11] Total # Ops : 82 Compute Unit(s) : NPU (82 ops) ``` ## How does this work? This [export script](https://aihub.qualcomm.com/models/resnet50_quantized/qai_hub_models/models/ResNet50Quantized/export.py) leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model on-device. Lets go through each step below in detail: Step 1: **Compile model for on-device deployment** To compile a PyTorch model for on-device deployment, we first trace the model in memory using the `jit.trace` and then call the `submit_compile_job` API. ```python import torch import qai_hub as hub from qai_hub_models.models.resnet50_quantized import # Load the model # Device device = hub.Device("Samsung Galaxy S23") ``` Step 2: **Performance profiling on cloud-hosted device** After compiling models from step 1. Models can be profiled model on-device using the `target_model`. Note that this scripts runs the model on a device automatically provisioned in the cloud. Once the job is submitted, you can navigate to a provided job URL to view a variety of on-device performance metrics. ```python profile_job = hub.submit_profile_job( model=target_model, device=device, ) ``` Step 3: **Verify on-device accuracy** To verify the accuracy of the model on-device, you can run on-device inference on sample input data on the same cloud hosted device. ```python input_data = torch_model.sample_inputs() inference_job = hub.submit_inference_job( model=target_model, device=device, inputs=input_data, ) on_device_output = inference_job.download_output_data() ``` With the output of the model, you can compute like PSNR, relative errors or spot check the output with expected output. **Note**: This on-device profiling and inference requires access to Qualcomm® AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup). ## Run demo on a cloud-hosted device You can also run the demo on-device. ```bash python -m qai_hub_models.models.resnet50_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.resnet50_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 ResNet50Quantized's performance across various devices [here](https://aihub.qualcomm.com/models/resnet50_quantized). Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/) ## License * The license for the original implementation of ResNet50Quantized 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 * [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) * [Source Model Implementation](https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.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).