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qualcomm / Midas-V2-Quantized
Model's Last Updated: Abril 10 2025
depth-estimation
Introduction of Midas-V2-Quantized
Model Details of Midas-V2-Quantized
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 . This repository provides scripts to run Midas-V2-Quantized on Qualcomm® devices. More details on model performance across various devices, can be found here .
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 | 1.147 ms | 0 - 3 MB | INT8 | NPU | Midas-V2-Quantized.tflite |
| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.488 ms | 0 - 65 MB | INT8 | NPU | Midas-V2-Quantized.so |
Installation
This model can be installed as a Python package via pip.
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
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.
qai-hub configure --api_token API_TOKEN
Navigate to 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.
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.
python -m qai_hub_models.models.midas_quantized.export
Profile Job summary of Midas-V2-Quantized
--------------------------------------------------
Device: SA8255 (Proxy) (13)
Estimated Inference Time: 1.49 ms
Estimated Peak Memory Range: 0.02-54.71 MB
Compute Units: NPU (147) | Total (147)
Run demo on a cloud-hosted device
You can also run the demo on-device.
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 (
.tfliteexport): This tutorial provides a guide to deploy the .tflite model in an Android application. -
QNN (
.soexport ): This sample app provides instructions on how to use the.soshared library in an Android application.
View on Qualcomm® AI Hub
Get more details on Midas-V2-Quantized's performance across various devices here . Explore all available models on Qualcomm® AI Hub
License
- The license for the original implementation of Midas-V2-Quantized can be found here .
- The license for the compiled assets for on-device deployment can be found here
References
- Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer
- Source Model Implementation
Community
- Join our AI Hub Slack community to collaborate, post questions and learn more about on-device AI.
- For questions or feedback please reach out to us .
Runs of qualcomm Midas-V2-Quantized on huggingface.co
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More Information About Midas-V2-Quantized huggingface.co Model
Url of Midas-V2-Quantized
Midas-V2-Quantized huggingface.co Url
Provider of Midas-V2-Quantized huggingface.co
Other API from qualcomm
Updated:Abril 08 2026
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