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---
library_name: pytorch
license: other
tags:
- android
pipeline_tag: depth-estimation
---
# Depth-Anything-V2: 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-V2 found [here](https://github.com/huggingface/transformers/tree/main/src/transformers/models/depth_anything).
This repository provides scripts to run Depth-Anything-V2 on Qualcomm® devices.
More details on model performance across various devices, can be found
[here](https://aihub.qualcomm.com/models/depth_anything_v2).
### Model Details
- **Model Type:** Model_use_case.depth_estimation
- **Model Stats:**
- Model checkpoint: DepthAnything_V2_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-V2 | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 171.771 ms | 1 - 419 MB | NPU | [Depth-Anything-V2.tflite](https://huggingface.co/qualcomm/Depth-Anything-V2/blob/main/Depth-Anything-V2.tflite) |
| Depth-Anything-V2 | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 172.982 ms | 0 - 402 MB | NPU | [Depth-Anything-V2.dlc](https://huggingface.co/qualcomm/Depth-Anything-V2/blob/main/Depth-Anything-V2.dlc) |
| Depth-Anything-V2 | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 109.967 ms | 1 - 535 MB | NPU | [Depth-Anything-V2.tflite](https://huggingface.co/qualcomm/Depth-Anything-V2/blob/main/Depth-Anything-V2.tflite) |
| Depth-Anything-V2 | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 121.891 ms | 3 - 556 MB | NPU | [Depth-Anything-V2.dlc](https://huggingface.co/qualcomm/Depth-Anything-V2/blob/main/Depth-Anything-V2.dlc) |
| Depth-Anything-V2 | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 72.957 ms | 1 - 13 MB | NPU | [Depth-Anything-V2.tflite](https://huggingface.co/qualcomm/Depth-Anything-V2/blob/main/Depth-Anything-V2.tflite) |
| Depth-Anything-V2 | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 76.275 ms | 3 - 5 MB | NPU | [Depth-Anything-V2.dlc](https://huggingface.co/qualcomm/Depth-Anything-V2/blob/main/Depth-Anything-V2.dlc) |
| Depth-Anything-V2 | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | ONNX | 78.795 ms | 0 - 79 MB | NPU | [Depth-Anything-V2.onnx.zip](https://huggingface.co/qualcomm/Depth-Anything-V2/blob/main/Depth-Anything-V2.onnx.zip) |
| Depth-Anything-V2 | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 78.898 ms | 1 - 415 MB | NPU | [Depth-Anything-V2.tflite](https://huggingface.co/qualcomm/Depth-Anything-V2/blob/main/Depth-Anything-V2.tflite) |
| Depth-Anything-V2 | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 82.592 ms | 3 - 404 MB | NPU | [Depth-Anything-V2.dlc](https://huggingface.co/qualcomm/Depth-Anything-V2/blob/main/Depth-Anything-V2.dlc) |
| Depth-Anything-V2 | float | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 171.771 ms | 1 - 419 MB | NPU | [Depth-Anything-V2.tflite](https://huggingface.co/qualcomm/Depth-Anything-V2/blob/main/Depth-Anything-V2.tflite) |
| Depth-Anything-V2 | float | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 172.982 ms | 0 - 402 MB | NPU | [Depth-Anything-V2.dlc](https://huggingface.co/qualcomm/Depth-Anything-V2/blob/main/Depth-Anything-V2.dlc) |
| Depth-Anything-V2 | float | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 103.039 ms | 1 - 409 MB | NPU | [Depth-Anything-V2.tflite](https://huggingface.co/qualcomm/Depth-Anything-V2/blob/main/Depth-Anything-V2.tflite) |
| Depth-Anything-V2 | float | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 116.172 ms | 0 - 392 MB | NPU | [Depth-Anything-V2.dlc](https://huggingface.co/qualcomm/Depth-Anything-V2/blob/main/Depth-Anything-V2.dlc) |
| Depth-Anything-V2 | float | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 78.898 ms | 1 - 415 MB | NPU | [Depth-Anything-V2.tflite](https://huggingface.co/qualcomm/Depth-Anything-V2/blob/main/Depth-Anything-V2.tflite) |
| Depth-Anything-V2 | float | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 82.592 ms | 3 - 404 MB | NPU | [Depth-Anything-V2.dlc](https://huggingface.co/qualcomm/Depth-Anything-V2/blob/main/Depth-Anything-V2.dlc) |
| Depth-Anything-V2 | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 48.753 ms | 0 - 568 MB | NPU | [Depth-Anything-V2.tflite](https://huggingface.co/qualcomm/Depth-Anything-V2/blob/main/Depth-Anything-V2.tflite) |
| Depth-Anything-V2 | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 55.342 ms | 3 - 573 MB | NPU | [Depth-Anything-V2.dlc](https://huggingface.co/qualcomm/Depth-Anything-V2/blob/main/Depth-Anything-V2.dlc) |
| Depth-Anything-V2 | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 60.987 ms | 5 - 883 MB | NPU | [Depth-Anything-V2.onnx.zip](https://huggingface.co/qualcomm/Depth-Anything-V2/blob/main/Depth-Anything-V2.onnx.zip) |
| Depth-Anything-V2 | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | TFLITE | 39.002 ms | 1 - 412 MB | NPU | [Depth-Anything-V2.tflite](https://huggingface.co/qualcomm/Depth-Anything-V2/blob/main/Depth-Anything-V2.tflite) |
| Depth-Anything-V2 | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | QNN_DLC | 39.729 ms | 3 - 394 MB | NPU | [Depth-Anything-V2.dlc](https://huggingface.co/qualcomm/Depth-Anything-V2/blob/main/Depth-Anything-V2.dlc) |
| Depth-Anything-V2 | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | ONNX | 49.166 ms | 2 - 853 MB | NPU | [Depth-Anything-V2.onnx.zip](https://huggingface.co/qualcomm/Depth-Anything-V2/blob/main/Depth-Anything-V2.onnx.zip) |
| Depth-Anything-V2 | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | TFLITE | 27.597 ms | 3 - 443 MB | NPU | [Depth-Anything-V2.tflite](https://huggingface.co/qualcomm/Depth-Anything-V2/blob/main/Depth-Anything-V2.tflite) |
| Depth-Anything-V2 | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | QNN_DLC | 33.894 ms | 3 - 414 MB | NPU | [Depth-Anything-V2.dlc](https://huggingface.co/qualcomm/Depth-Anything-V2/blob/main/Depth-Anything-V2.dlc) |
| Depth-Anything-V2 | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | ONNX | 41.679 ms | 5 - 800 MB | NPU | [Depth-Anything-V2.onnx.zip](https://huggingface.co/qualcomm/Depth-Anything-V2/blob/main/Depth-Anything-V2.onnx.zip) |
| Depth-Anything-V2 | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 76.633 ms | 3 - 3 MB | NPU | [Depth-Anything-V2.dlc](https://huggingface.co/qualcomm/Depth-Anything-V2/blob/main/Depth-Anything-V2.dlc) |
| Depth-Anything-V2 | float | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 77.304 ms | 62 - 62 MB | NPU | [Depth-Anything-V2.onnx.zip](https://huggingface.co/qualcomm/Depth-Anything-V2/blob/main/Depth-Anything-V2.onnx.zip) |
## Installation
Install the package via pip:
```bash
# NOTE: 3.10 <= PYTHON_VERSION < 3.14 is supported.
pip install "qai-hub-models[depth-anything-v2]"
```
## Configure Qualcomm® AI Hub Workbench to run this model on a cloud-hosted device
Sign-in to [Qualcomm® AI Hub Workbench](https://workbench.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://workbench.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.depth_anything_v2.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_v2.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.depth_anything_v2.export
```
## How does this work?
This [export script](https://aihub.qualcomm.com/models/depth_anything_v2/qai_hub_models/models/Depth-Anything-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.depth_anything_v2 import Model
# Load the model
torch_model = Model.from_pretrained()
# Device
device = hub.Device("Samsung Galaxy S25")
# 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 Workbench. [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.depth_anything_v2.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_v2.demo -- --eval-mode 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 Depth-Anything-V2's performance across various devices [here](https://aihub.qualcomm.com/models/depth_anything_v2).
Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
## License
* The license for the original implementation of Depth-Anything-V2 can be found
[here](https://github.com/huggingface/transformers/blob/main/LICENSE).
## References
* [Depth Anything V2](https://arxiv.org/abs/2406.09414)
* [Source Model Implementation](https://github.com/huggingface/transformers/tree/main/src/transformers/models/depth_anything)
## 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).
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