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README.md

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+---
+library_name: pytorch
+license: mit
+pipeline_tag: depth-estimation
+tags:
+- quantized
+- android
+
+---
+
+![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/midas_quantized/web-assets/model_demo.png)
+
+# Midas-V2-Quantized: Optimized for Mobile Deployment
+## Quantized Deep Convolutional Neural Network model for depth estimation
+
+Midas is designed for estimating depth at each point in an image.
+
+This model is an implementation of Midas-V2-Quantized found [here](https://github.com/isl-org/MiDaS).
+This repository provides scripts to run Midas-V2-Quantized on Qualcomm® devices.
+More details on model performance across various devices, can be found
+[here](https://aihub.qualcomm.com/models/midas_quantized).
+
+
+### Model Details
+
+- **Model Type:** Depth estimation
+- **Model Stats:**
+  - Model checkpoint: MiDaS_small
+  - Input resolution: 256x256
+  - Number of parameters: 16.6M
+  - Model size: 16.6 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 | 1.154 ms | 0 - 2 MB | INT8 | NPU |  [Midas-V2-Quantized.tflite](https://huggingface.co/qualcomm/Midas-V2-Quantized/blob/main/Midas-V2-Quantized.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.482 ms | 0 - 275 MB | INT8 | NPU |  [Midas-V2-Quantized.so](https://huggingface.co/qualcomm/Midas-V2-Quantized/blob/main/Midas-V2-Quantized.so) 
+
+
+
+## Installation
+
+This model can be installed as a Python package via pip.
+
+```bash
+pip install "qai-hub-models[midas_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.midas_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.midas_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.midas_quantized.export
+```
+
+```
+Profile Job summary of Midas-V2-Quantized
+--------------------------------------------------
+Device: Snapdragon X Elite CRD (11)
+Estimated Inference Time: 1.52 ms
+Estimated Peak Memory Range: 0.46-0.46 MB
+Compute Units: NPU (148) | Total (148)
+
+
+```
+
+
+
+
+## Run demo on a cloud-hosted device
+
+You can also run the demo on-device.
+
+```bash
+python -m qai_hub_models.models.midas_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.midas_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 Midas-V2-Quantized's performance across various devices [here](https://aihub.qualcomm.com/models/midas_quantized).
+Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
+
+## License
+- The license for the original implementation of Midas-V2-Quantized can be found
+  [here](https://github.com/isl-org/MiDaS/blob/master/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
+* [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://arxiv.org/abs/1907.01341v3)
+* [Source Model Implementation](https://github.com/isl-org/MiDaS)
+
+## Community
+* Join [our AI Hub Slack community](https://qualcomm-ai-hub.slack.com/join/shared_invite/zt-2d5zsmas3-Sj0Q9TzslueCjS31eXG2UA#/shared-invite/email) to collaborate, post questions and learn more about on-device AI.
+* For questions or feedback please [reach out to us](mailto:[email protected]).
+
+