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Depth-Anything

Depth Estimation
PyTorch
LiteRT
android
Model card Files Community

Depth-Anything: Optimized for Mobile Deployment

Deep Convolutional Neural Network model for depth estimation

Depth Anything is designed for estimating depth at each point in an image.

This model is an implementation of Depth-Anything found here.

This repository provides scripts to run Depth-Anything on Qualcomm® devices. More details on model performance across various devices, can be found here.

Model Details

  • Model Type: Model_use_case.depth_estimation
  • Model Stats:
    • Model checkpoint: DepthAnything_Small
    • Input resolution: 518x518
    • Number of parameters: 24.7M
    • Model size (float): 94.3 MB
Model Precision Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Primary Compute Unit Target Model
Depth-Anything float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) TFLITE 196.294 ms 1 - 174 MB NPU Depth-Anything.tflite
Depth-Anything float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN_DLC 182.061 ms 0 - 238 MB NPU Depth-Anything.dlc
Depth-Anything float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 104.508 ms 0 - 184 MB NPU Depth-Anything.tflite
Depth-Anything float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN_DLC 137.094 ms 3 - 224 MB NPU Depth-Anything.dlc
Depth-Anything float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 83.328 ms 1 - 36 MB NPU Depth-Anything.tflite
Depth-Anything float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN_DLC 73.805 ms 3 - 63 MB NPU Depth-Anything.dlc
Depth-Anything float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 89.197 ms 1 - 174 MB NPU Depth-Anything.tflite
Depth-Anything float SA7255P ADP Qualcomm® SA7255P TFLITE 196.294 ms 1 - 174 MB NPU Depth-Anything.tflite
Depth-Anything float SA7255P ADP Qualcomm® SA7255P QNN_DLC 182.061 ms 0 - 238 MB NPU Depth-Anything.dlc
Depth-Anything float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 83.3 ms 0 - 35 MB NPU Depth-Anything.tflite
Depth-Anything float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN_DLC 72.797 ms 3 - 63 MB NPU Depth-Anything.dlc
Depth-Anything float SA8295P ADP Qualcomm® SA8295P TFLITE 112.252 ms 1 - 181 MB NPU Depth-Anything.tflite
Depth-Anything float SA8295P ADP Qualcomm® SA8295P QNN_DLC 104.413 ms 3 - 232 MB NPU Depth-Anything.dlc
Depth-Anything float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 83.375 ms 1 - 38 MB NPU Depth-Anything.tflite
Depth-Anything float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN_DLC 74.2 ms 3 - 60 MB NPU Depth-Anything.dlc
Depth-Anything float SA8775P ADP Qualcomm® SA8775P TFLITE 89.197 ms 1 - 174 MB NPU Depth-Anything.tflite
Depth-Anything float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile TFLITE 83.577 ms 1 - 36 MB NPU Depth-Anything.tflite
Depth-Anything float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN_DLC 72.575 ms 3 - 67 MB NPU Depth-Anything.dlc
Depth-Anything float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile ONNX 147.365 ms 0 - 179 MB NPU Depth-Anything.onnx.zip
Depth-Anything float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 61.924 ms 0 - 177 MB NPU Depth-Anything.tflite
Depth-Anything float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN_DLC 57.032 ms 3 - 252 MB NPU Depth-Anything.dlc
Depth-Anything float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 108.767 ms 296 - 1068 MB NPU Depth-Anything.onnx.zip
Depth-Anything float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile TFLITE 54.364 ms 1 - 176 MB NPU Depth-Anything.tflite
Depth-Anything float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN_DLC 42.946 ms 3 - 275 MB NPU Depth-Anything.dlc
Depth-Anything float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile ONNX 79.568 ms 0 - 765 MB NPU Depth-Anything.onnx.zip
Depth-Anything float Snapdragon X Elite CRD Snapdragon® X Elite QNN_DLC 72.454 ms 89 - 89 MB NPU Depth-Anything.dlc
Depth-Anything float Snapdragon X Elite CRD Snapdragon® X Elite ONNX 140.54 ms 67 - 67 MB NPU Depth-Anything.onnx.zip

Installation

Install the package via pip:

pip install "qai-hub-models[depth-anything]"

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.depth_anything.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.depth_anything.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.depth_anything.export

How does this work?

This export script leverages Qualcomm® AI Hub 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.

import torch

import qai_hub as hub
from qai_hub_models.models.depth_anything 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.

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.

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.

Run demo on a cloud-hosted device

You can also run the demo on-device.

python -m qai_hub_models.models.depth_anything.demo --eval-mode 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.depth_anything.demo -- --eval-mode on-device

Deploying compiled model to Android

The models can be deployed using multiple runtimes:

  • TensorFlow Lite (.tflite export): This tutorial provides a guide to deploy the .tflite model in an Android application.

  • QNN (.so export ): This sample app provides instructions on how to use the .so shared library in an Android application.

View on Qualcomm® AI Hub

Get more details on Depth-Anything's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

  • The license for the original implementation of Depth-Anything can be found here.
  • The license for the compiled assets for on-device deployment can be found here

References

Community

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