--- library_name: pytorch license: mit pipeline_tag: depth-estimation tags: - android --- ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/midas/web-assets/model_demo.png) # Midas-V2: Optimized for Mobile Deployment ## 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 found [here](https://github.com/isl-org/MiDaS). This repository provides scripts to run Midas-V2 on Qualcomm® devices. More details on model performance across various devices, can be found [here](https://aihub.qualcomm.com/models/midas). ### Model Details - **Model Type:** Depth estimation - **Model Stats:** - Model checkpoint: MiDaS_small - Input resolution: 256x256 - Number of parameters: 16.6M - Model size: 63.2 MB | Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model |---|---|---|---|---|---|---|---|---| | Midas-V2 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 3.133 ms | 0 - 162 MB | FP16 | NPU | [Midas-V2.tflite](https://huggingface.co/qualcomm/Midas-V2/blob/main/Midas-V2.tflite) | | Midas-V2 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 3.271 ms | 0 - 111 MB | FP16 | NPU | [Midas-V2.so](https://huggingface.co/qualcomm/Midas-V2/blob/main/Midas-V2.so) | | Midas-V2 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 3.327 ms | 0 - 138 MB | FP16 | NPU | [Midas-V2.onnx](https://huggingface.co/qualcomm/Midas-V2/blob/main/Midas-V2.onnx) | | Midas-V2 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 2.208 ms | 0 - 31 MB | FP16 | NPU | [Midas-V2.tflite](https://huggingface.co/qualcomm/Midas-V2/blob/main/Midas-V2.tflite) | | Midas-V2 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 2.315 ms | 1 - 29 MB | FP16 | NPU | [Midas-V2.so](https://huggingface.co/qualcomm/Midas-V2/blob/main/Midas-V2.so) | | Midas-V2 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 2.386 ms | 0 - 37 MB | FP16 | NPU | [Midas-V2.onnx](https://huggingface.co/qualcomm/Midas-V2/blob/main/Midas-V2.onnx) | | Midas-V2 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 2.024 ms | 0 - 27 MB | FP16 | NPU | [Midas-V2.tflite](https://huggingface.co/qualcomm/Midas-V2/blob/main/Midas-V2.tflite) | | Midas-V2 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 1.792 ms | 1 - 28 MB | FP16 | NPU | Use Export Script | | Midas-V2 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 1.912 ms | 1 - 32 MB | FP16 | NPU | [Midas-V2.onnx](https://huggingface.co/qualcomm/Midas-V2/blob/main/Midas-V2.onnx) | | Midas-V2 | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 3.141 ms | 0 - 141 MB | FP16 | NPU | [Midas-V2.tflite](https://huggingface.co/qualcomm/Midas-V2/blob/main/Midas-V2.tflite) | | Midas-V2 | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 3.033 ms | 1 - 4 MB | FP16 | NPU | Use Export Script | | Midas-V2 | SA7255P ADP | SA7255P | TFLITE | 84.338 ms | 0 - 23 MB | FP16 | NPU | [Midas-V2.tflite](https://huggingface.co/qualcomm/Midas-V2/blob/main/Midas-V2.tflite) | | Midas-V2 | SA7255P ADP | SA7255P | QNN | 84.188 ms | 1 - 10 MB | FP16 | NPU | Use Export Script | | Midas-V2 | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 3.148 ms | 0 - 151 MB | FP16 | NPU | [Midas-V2.tflite](https://huggingface.co/qualcomm/Midas-V2/blob/main/Midas-V2.tflite) | | Midas-V2 | SA8255 (Proxy) | SA8255P Proxy | QNN | 3.048 ms | 1 - 4 MB | FP16 | NPU | Use Export Script | | Midas-V2 | SA8295P ADP | SA8295P | TFLITE | 5.662 ms | 0 - 25 MB | FP16 | NPU | [Midas-V2.tflite](https://huggingface.co/qualcomm/Midas-V2/blob/main/Midas-V2.tflite) | | Midas-V2 | SA8295P ADP | SA8295P | QNN | 5.628 ms | 1 - 15 MB | FP16 | NPU | Use Export Script | | Midas-V2 | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 3.127 ms | 0 - 141 MB | FP16 | NPU | [Midas-V2.tflite](https://huggingface.co/qualcomm/Midas-V2/blob/main/Midas-V2.tflite) | | Midas-V2 | SA8650 (Proxy) | SA8650P Proxy | QNN | 3.043 ms | 1 - 4 MB | FP16 | NPU | Use Export Script | | Midas-V2 | SA8775P ADP | SA8775P | TFLITE | 5.312 ms | 0 - 23 MB | FP16 | NPU | [Midas-V2.tflite](https://huggingface.co/qualcomm/Midas-V2/blob/main/Midas-V2.tflite) | | Midas-V2 | SA8775P ADP | SA8775P | QNN | 5.221 ms | 1 - 11 MB | FP16 | NPU | Use Export Script | | Midas-V2 | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 4.76 ms | 0 - 29 MB | FP16 | NPU | [Midas-V2.tflite](https://huggingface.co/qualcomm/Midas-V2/blob/main/Midas-V2.tflite) | | Midas-V2 | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 4.902 ms | 0 - 29 MB | FP16 | NPU | Use Export Script | | Midas-V2 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 3.187 ms | 1 - 1 MB | FP16 | NPU | Use Export Script | | Midas-V2 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 3.359 ms | 37 - 37 MB | FP16 | NPU | [Midas-V2.onnx](https://huggingface.co/qualcomm/Midas-V2/blob/main/Midas-V2.onnx) | ## Installation Install the package via pip: ```bash pip install "qai-hub-models[midas]" ``` ## 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.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.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.export ``` ``` Profiling Results ------------------------------------------------------------ Midas-V2 Device : Samsung Galaxy S23 (13) Runtime : TFLITE Estimated inference time (ms) : 3.1 Estimated peak memory usage (MB): [0, 162] Total # Ops : 138 Compute Unit(s) : NPU (138 ops) ``` ## How does this work? This [export script](https://aihub.qualcomm.com/models/midas/qai_hub_models/models/Midas-V2/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.midas import Model # Load the model torch_model = Model.from_pretrained() # Device device = hub.Device("Samsung Galaxy S24") # Trace model input_shape = torch_model.get_input_spec() sample_inputs = torch_model.sample_inputs() pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()]) # Compile model on a specific device compile_job = hub.submit_compile_job( model=pt_model, device=device, input_specs=torch_model.get_input_spec(), ) # Get target model to run on-device target_model = compile_job.get_target_model() ``` 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.midas.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.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's performance across various devices [here](https://aihub.qualcomm.com/models/midas). Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/) ## License * The license for the original implementation of Midas-V2 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://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).