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--- |
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datasets: |
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- imagenet-1k |
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- imagenet-22k |
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library_name: pytorch |
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license: bsd-3-clause |
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pipeline_tag: image-classification |
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tags: |
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- backbone |
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- quantized |
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- android |
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--- |
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# DenseNet-121-Quantized: Optimized for Mobile Deployment |
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## Imagenet classifier and general purpose backbone |
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Densenet 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. |
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This model is an implementation of DenseNet-121-Quantized found [here]({source_repo}). |
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This repository provides scripts to run DenseNet-121-Quantized on Qualcomm® devices. |
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More details on model performance across various devices, can be found |
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[here](https://aihub.qualcomm.com/models/densenet121_quantized). |
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### Model Details |
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- **Model Type:** Image classification |
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- **Model Stats:** |
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- Model checkpoint: Imagenet |
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- Input resolution: 224x224 |
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- Number of parameters: 7.97M |
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- Model size: 9.4 MB |
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| Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model |
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|---|---|---|---|---|---|---|---|---| |
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| DenseNet-121-Quantized | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 1.745 ms | 0 - 272 MB | INT8 | NPU | [DenseNet-121-Quantized.so](https://huggingface.co/qualcomm/DenseNet-121-Quantized/blob/main/DenseNet-121-Quantized.so) | |
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| DenseNet-121-Quantized | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 29.847 ms | 8 - 12 MB | INT8 | NPU | [DenseNet-121-Quantized.onnx](https://huggingface.co/qualcomm/DenseNet-121-Quantized/blob/main/DenseNet-121-Quantized.onnx) | |
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| DenseNet-121-Quantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 1.218 ms | 0 - 22 MB | INT8 | NPU | [DenseNet-121-Quantized.so](https://huggingface.co/qualcomm/DenseNet-121-Quantized/blob/main/DenseNet-121-Quantized.so) | |
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| DenseNet-121-Quantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 22.391 ms | 9 - 1018 MB | INT8 | NPU | [DenseNet-121-Quantized.onnx](https://huggingface.co/qualcomm/DenseNet-121-Quantized/blob/main/DenseNet-121-Quantized.onnx) | |
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| DenseNet-121-Quantized | RB3 Gen 2 (Proxy) | QCS6490 Proxy | QNN | 6.521 ms | 0 - 8 MB | INT8 | NPU | Use Export Script | |
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| DenseNet-121-Quantized | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 1.672 ms | 0 - 1 MB | INT8 | NPU | Use Export Script | |
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| DenseNet-121-Quantized | SA8255 (Proxy) | SA8255P Proxy | QNN | 1.67 ms | 0 - 1 MB | INT8 | NPU | Use Export Script | |
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| DenseNet-121-Quantized | SA8775 (Proxy) | SA8775P Proxy | QNN | 1.684 ms | 0 - 1 MB | INT8 | NPU | Use Export Script | |
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| DenseNet-121-Quantized | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 2.13 ms | 0 - 27 MB | INT8 | NPU | Use Export Script | |
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| DenseNet-121-Quantized | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 1.16 ms | 0 - 26 MB | INT8 | NPU | Use Export Script | |
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| DenseNet-121-Quantized | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 1.822 ms | 0 - 0 MB | INT8 | NPU | Use Export Script | |
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| DenseNet-121-Quantized | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 32.525 ms | 46 - 46 MB | INT8 | NPU | [DenseNet-121-Quantized.onnx](https://huggingface.co/qualcomm/DenseNet-121-Quantized/blob/main/DenseNet-121-Quantized.onnx) | |
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## Installation |
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This model can be installed as a Python package via pip. |
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```bash |
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pip install qai-hub-models |
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``` |
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## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device |
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Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your |
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Qualcomm® ID. Once signed in navigate to `Account -> Settings -> API Token`. |
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With this API token, you can configure your client to run models on the cloud |
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hosted devices. |
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```bash |
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qai-hub configure --api_token API_TOKEN |
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``` |
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Navigate to [docs](https://app.aihub.qualcomm.com/docs/) for more information. |
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## Demo off target |
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The package contains a simple end-to-end demo that downloads pre-trained |
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weights and runs this model on a sample input. |
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```bash |
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python -m qai_hub_models.models.densenet121_quantized.demo |
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``` |
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The above demo runs a reference implementation of pre-processing, model |
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inference, and post processing. |
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**NOTE**: If you want running in a Jupyter Notebook or Google Colab like |
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environment, please add the following to your cell (instead of the above). |
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``` |
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%run -m qai_hub_models.models.densenet121_quantized.demo |
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``` |
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### Run model on a cloud-hosted device |
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In addition to the demo, you can also run the model on a cloud-hosted Qualcomm® |
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device. This script does the following: |
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* Performance check on-device on a cloud-hosted device |
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* Downloads compiled assets that can be deployed on-device for Android. |
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* Accuracy check between PyTorch and on-device outputs. |
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```bash |
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python -m qai_hub_models.models.densenet121_quantized.export |
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``` |
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``` |
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Profiling Results |
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------------------------------------------------------------ |
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DenseNet-121-Quantized |
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Device : Samsung Galaxy S23 (13) |
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Runtime : QNN |
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Estimated inference time (ms) : 1.7 |
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Estimated peak memory usage (MB): [0, 272] |
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Total # Ops : 215 |
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Compute Unit(s) : NPU (215 ops) |
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``` |
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## How does this work? |
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This [export script](https://aihub.qualcomm.com/models/densenet121_quantized/qai_hub_models/models/DenseNet-121-Quantized/export.py) |
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leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model |
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on-device. Lets go through each step below in detail: |
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Step 1: **Compile model for on-device deployment** |
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To compile a PyTorch model for on-device deployment, we first trace the model |
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in memory using the `jit.trace` and then call the `submit_compile_job` API. |
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```python |
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import torch |
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import qai_hub as hub |
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from qai_hub_models.models.densenet121_quantized import |
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# Load the model |
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# Device |
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device = hub.Device("Samsung Galaxy S23") |
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``` |
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Step 2: **Performance profiling on cloud-hosted device** |
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After compiling models from step 1. Models can be profiled model on-device using the |
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`target_model`. Note that this scripts runs the model on a device automatically |
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provisioned in the cloud. Once the job is submitted, you can navigate to a |
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provided job URL to view a variety of on-device performance metrics. |
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```python |
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profile_job = hub.submit_profile_job( |
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model=target_model, |
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device=device, |
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) |
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``` |
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Step 3: **Verify on-device accuracy** |
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To verify the accuracy of the model on-device, you can run on-device inference |
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on sample input data on the same cloud hosted device. |
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```python |
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input_data = torch_model.sample_inputs() |
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inference_job = hub.submit_inference_job( |
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model=target_model, |
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device=device, |
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inputs=input_data, |
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) |
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on_device_output = inference_job.download_output_data() |
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``` |
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With the output of the model, you can compute like PSNR, relative errors or |
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spot check the output with expected output. |
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**Note**: This on-device profiling and inference requires access to Qualcomm® |
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AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup). |
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## Run demo on a cloud-hosted device |
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You can also run the demo on-device. |
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```bash |
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python -m qai_hub_models.models.densenet121_quantized.demo --on-device |
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``` |
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**NOTE**: If you want running in a Jupyter Notebook or Google Colab like |
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environment, please add the following to your cell (instead of the above). |
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``` |
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%run -m qai_hub_models.models.densenet121_quantized.demo -- --on-device |
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``` |
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## Deploying compiled model to Android |
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The models can be deployed using multiple runtimes: |
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- TensorFlow Lite (`.tflite` export): [This |
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tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a |
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guide to deploy the .tflite model in an Android application. |
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- QNN (`.so` export ): This [sample |
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app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html) |
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provides instructions on how to use the `.so` shared library in an Android application. |
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## View on Qualcomm® AI Hub |
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Get more details on DenseNet-121-Quantized's performance across various devices [here](https://aihub.qualcomm.com/models/densenet121_quantized). |
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Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/) |
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## License |
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* The license for the original implementation of DenseNet-121-Quantized can be found [here](https://github.com/pytorch/vision/blob/main/LICENSE). |
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* 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) |
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## References |
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* [Densely Connected Convolutional Networks](https://arxiv.org/abs/1608.06993) |
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* [Source Model Implementation](https://github.com/pytorch/vision/blob/main/torchvision/models/densenet.py) |
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## Community |
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* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI. |
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* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com). |
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