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Depth-Anything

Depth Estimation
PyTorch
TF Lite
ONNX
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: Depth estimation
  • Model Stats:
    • Model checkpoint: DepthAnything_Small
    • Input resolution: 518x518
    • Number of parameters: 24.8M
    • Model size: 94 MB
Model Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Precision Primary Compute Unit Target Model
Depth-Anything Samsung Galaxy S23 Snapdragon® 8 Gen 2 TFLITE 327.858 ms 1 - 60 MB FP16 NPU Depth-Anything.tflite
Depth-Anything Samsung Galaxy S23 Snapdragon® 8 Gen 2 QNN 379.27 ms 3 - 83 MB FP16 NPU Depth-Anything.so
Depth-Anything Samsung Galaxy S23 Snapdragon® 8 Gen 2 ONNX 240.144 ms 0 - 181 MB FP16 NPU Depth-Anything.onnx
Depth-Anything Samsung Galaxy S24 Snapdragon® 8 Gen 3 TFLITE 250.711 ms 0 - 246 MB FP16 NPU Depth-Anything.tflite
Depth-Anything Samsung Galaxy S24 Snapdragon® 8 Gen 3 QNN 286.215 ms 0 - 258 MB FP16 NPU Depth-Anything.so
Depth-Anything Samsung Galaxy S24 Snapdragon® 8 Gen 3 ONNX 186.098 ms 5 - 280 MB FP16 NPU Depth-Anything.onnx
Depth-Anything Snapdragon 8 Elite QRD Snapdragon® 8 Elite TFLITE 236.545 ms 1 - 272 MB FP16 NPU Depth-Anything.tflite
Depth-Anything Snapdragon 8 Elite QRD Snapdragon® 8 Elite QNN 242.956 ms 3 - 283 MB FP16 NPU Use Export Script
Depth-Anything Snapdragon 8 Elite QRD Snapdragon® 8 Elite ONNX 158.768 ms 5 - 297 MB FP16 NPU Depth-Anything.onnx
Depth-Anything QCS8550 (Proxy) QCS8550 Proxy TFLITE 317.382 ms 0 - 58 MB FP16 NPU Depth-Anything.tflite
Depth-Anything QCS8550 (Proxy) QCS8550 Proxy QNN 229.212 ms 3 - 6 MB FP16 NPU Use Export Script
Depth-Anything SA7255P ADP SA7255P TFLITE 1141.05 ms 0 - 268 MB FP16 NPU Depth-Anything.tflite
Depth-Anything SA7255P ADP SA7255P QNN 1005.592 ms 3 - 12 MB FP16 NPU Use Export Script
Depth-Anything SA8255 (Proxy) SA8255P Proxy TFLITE 322.962 ms 0 - 61 MB FP16 NPU Depth-Anything.tflite
Depth-Anything SA8255 (Proxy) SA8255P Proxy QNN 230.498 ms 3 - 5 MB FP16 NPU Use Export Script
Depth-Anything SA8295P ADP SA8295P TFLITE 387.756 ms 1 - 271 MB FP16 NPU Depth-Anything.tflite
Depth-Anything SA8295P ADP SA8295P QNN 279.83 ms 0 - 15 MB FP16 NPU Use Export Script
Depth-Anything SA8650 (Proxy) SA8650P Proxy TFLITE 329.299 ms 1 - 66 MB FP16 NPU Depth-Anything.tflite
Depth-Anything SA8650 (Proxy) SA8650P Proxy QNN 241.205 ms 3 - 6 MB FP16 NPU Use Export Script
Depth-Anything SA8775P ADP SA8775P TFLITE 368.831 ms 1 - 270 MB FP16 NPU Depth-Anything.tflite
Depth-Anything SA8775P ADP SA8775P QNN 263.584 ms 2 - 12 MB FP16 NPU Use Export Script
Depth-Anything QCS8450 (Proxy) QCS8450 Proxy TFLITE 379.814 ms 1 - 256 MB FP16 NPU Depth-Anything.tflite
Depth-Anything QCS8450 (Proxy) QCS8450 Proxy QNN 427.625 ms 3 - 276 MB FP16 NPU Use Export Script
Depth-Anything Snapdragon X Elite CRD Snapdragon® X Elite QNN 207.005 ms 3 - 3 MB FP16 NPU Use Export Script
Depth-Anything Snapdragon X Elite CRD Snapdragon® X Elite ONNX 271.796 ms 63 - 63 MB FP16 NPU Depth-Anything.onnx

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

Profiling Results
------------------------------------------------------------
Depth-Anything
Device                          : Samsung Galaxy S23 (13)
Runtime                         : TFLITE                 
Estimated inference time (ms)   : 327.9                  
Estimated peak memory usage (MB): [1, 60]                
Total # Ops                     : 634                    
Compute Unit(s)                 : NPU (634 ops)

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 --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 -- --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

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