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Depth-Anything

Depth Estimation
PyTorch
android
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v0.46.1

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See https://github.com/quic/ai-hub-models/releases/v0.46.1 for changelog.

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  1. Depth-Anything_float.dlc +0 -3
  2. Depth-Anything_float.onnx.zip +0 -3
  3. Depth-Anything_float.tflite +0 -3
  4. README.md +74 -227
  5. tool-versions.yaml +0 -4
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@@ -9,245 +9,92 @@ pipeline_tag: depth-estimation
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  ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/depth_anything/web-assets/model_demo.png)
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- # Depth-Anything: Optimized for Mobile Deployment
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- ## Deep Convolutional Neural Network model for depth estimation
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-
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  Depth Anything is designed for estimating depth at each point in an image.
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- This model is an implementation of Depth-Anything found [here](https://github.com/huggingface/transformers/tree/main/src/transformers/models/depth_anything).
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-
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-
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- This repository provides scripts to run Depth-Anything on Qualcomm® devices.
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- More details on model performance across various devices, can be found
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- [here](https://aihub.qualcomm.com/models/depth_anything).
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-
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-
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-
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- ### Model Details
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-
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- - **Model Type:** Model_use_case.depth_estimation
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- - **Model Stats:**
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- - Model checkpoint: DepthAnything_Small
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- - Input resolution: 518x518
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- - Number of parameters: 24.7M
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- - Model size (float): 94.3 MB
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-
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- | Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model
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- |---|---|---|---|---|---|---|---|---|
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- | Depth-Anything | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 174.976 ms | 0 - 393 MB | NPU | [Depth-Anything.tflite](https://huggingface.co/qualcomm/Depth-Anything/blob/main/Depth-Anything.tflite) |
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- | Depth-Anything | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 172.292 ms | 3 - 404 MB | NPU | [Depth-Anything.dlc](https://huggingface.co/qualcomm/Depth-Anything/blob/main/Depth-Anything.dlc) |
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- | Depth-Anything | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 110.072 ms | 1 - 533 MB | NPU | [Depth-Anything.tflite](https://huggingface.co/qualcomm/Depth-Anything/blob/main/Depth-Anything.tflite) |
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- | Depth-Anything | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 125.239 ms | 4 - 564 MB | NPU | [Depth-Anything.dlc](https://huggingface.co/qualcomm/Depth-Anything/blob/main/Depth-Anything.dlc) |
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- | Depth-Anything | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 73.28 ms | 1 - 13 MB | NPU | [Depth-Anything.tflite](https://huggingface.co/qualcomm/Depth-Anything/blob/main/Depth-Anything.tflite) |
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- | Depth-Anything | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 75.875 ms | 3 - 5 MB | NPU | [Depth-Anything.dlc](https://huggingface.co/qualcomm/Depth-Anything/blob/main/Depth-Anything.dlc) |
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- | Depth-Anything | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | ONNX | 76.325 ms | 0 - 64 MB | NPU | [Depth-Anything.onnx.zip](https://huggingface.co/qualcomm/Depth-Anything/blob/main/Depth-Anything.onnx.zip) |
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- | Depth-Anything | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 77.116 ms | 1 - 412 MB | NPU | [Depth-Anything.tflite](https://huggingface.co/qualcomm/Depth-Anything/blob/main/Depth-Anything.tflite) |
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- | Depth-Anything | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 365.572 ms | 1 - 403 MB | NPU | [Depth-Anything.dlc](https://huggingface.co/qualcomm/Depth-Anything/blob/main/Depth-Anything.dlc) |
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- | Depth-Anything | float | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 174.976 ms | 0 - 393 MB | NPU | [Depth-Anything.tflite](https://huggingface.co/qualcomm/Depth-Anything/blob/main/Depth-Anything.tflite) |
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- | Depth-Anything | float | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 172.292 ms | 3 - 404 MB | NPU | [Depth-Anything.dlc](https://huggingface.co/qualcomm/Depth-Anything/blob/main/Depth-Anything.dlc) |
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- | Depth-Anything | float | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 102.958 ms | 1 - 410 MB | NPU | [Depth-Anything.tflite](https://huggingface.co/qualcomm/Depth-Anything/blob/main/Depth-Anything.tflite) |
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- | Depth-Anything | float | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 107.601 ms | 0 - 402 MB | NPU | [Depth-Anything.dlc](https://huggingface.co/qualcomm/Depth-Anything/blob/main/Depth-Anything.dlc) |
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- | Depth-Anything | float | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 77.116 ms | 1 - 412 MB | NPU | [Depth-Anything.tflite](https://huggingface.co/qualcomm/Depth-Anything/blob/main/Depth-Anything.tflite) |
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- | Depth-Anything | float | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 365.572 ms | 1 - 403 MB | NPU | [Depth-Anything.dlc](https://huggingface.co/qualcomm/Depth-Anything/blob/main/Depth-Anything.dlc) |
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- | Depth-Anything | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 51.306 ms | 1 - 545 MB | NPU | [Depth-Anything.tflite](https://huggingface.co/qualcomm/Depth-Anything/blob/main/Depth-Anything.tflite) |
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- | Depth-Anything | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 54.804 ms | 3 - 571 MB | NPU | [Depth-Anything.dlc](https://huggingface.co/qualcomm/Depth-Anything/blob/main/Depth-Anything.dlc) |
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- | Depth-Anything | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 59.288 ms | 5 - 831 MB | NPU | [Depth-Anything.onnx.zip](https://huggingface.co/qualcomm/Depth-Anything/blob/main/Depth-Anything.onnx.zip) |
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- | Depth-Anything | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | TFLITE | 38.089 ms | 0 - 408 MB | NPU | [Depth-Anything.tflite](https://huggingface.co/qualcomm/Depth-Anything/blob/main/Depth-Anything.tflite) |
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- | Depth-Anything | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | QNN_DLC | 39.802 ms | 3 - 391 MB | NPU | [Depth-Anything.dlc](https://huggingface.co/qualcomm/Depth-Anything/blob/main/Depth-Anything.dlc) |
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- | Depth-Anything | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | ONNX | 45.599 ms | 3 - 676 MB | NPU | [Depth-Anything.onnx.zip](https://huggingface.co/qualcomm/Depth-Anything/blob/main/Depth-Anything.onnx.zip) |
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- | Depth-Anything | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | TFLITE | 27.46 ms | 3 - 442 MB | NPU | [Depth-Anything.tflite](https://huggingface.co/qualcomm/Depth-Anything/blob/main/Depth-Anything.tflite) |
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- | Depth-Anything | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | QNN_DLC | 34.068 ms | 3 - 414 MB | NPU | [Depth-Anything.dlc](https://huggingface.co/qualcomm/Depth-Anything/blob/main/Depth-Anything.dlc) |
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- | Depth-Anything | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | ONNX | 39.099 ms | 5 - 660 MB | NPU | [Depth-Anything.onnx.zip](https://huggingface.co/qualcomm/Depth-Anything/blob/main/Depth-Anything.onnx.zip) |
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- | Depth-Anything | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 76.584 ms | 3 - 3 MB | NPU | [Depth-Anything.dlc](https://huggingface.co/qualcomm/Depth-Anything/blob/main/Depth-Anything.dlc) |
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- | Depth-Anything | float | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 75.045 ms | 61 - 61 MB | NPU | [Depth-Anything.onnx.zip](https://huggingface.co/qualcomm/Depth-Anything/blob/main/Depth-Anything.onnx.zip) |
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-
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-
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-
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-
68
- ## Installation
69
-
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-
71
- Install the package via pip:
72
- ```bash
73
- # NOTE: 3.10 <= PYTHON_VERSION < 3.14 is supported.
74
- pip install "qai-hub-models[depth-anything]"
75
- ```
76
-
77
-
78
- ## Configure Qualcomm® AI Hub Workbench to run this model on a cloud-hosted device
79
-
80
- Sign-in to [Qualcomm® AI Hub Workbench](https://workbench.aihub.qualcomm.com/) with your
81
- Qualcomm® ID. Once signed in navigate to `Account -> Settings -> API Token`.
82
-
83
- With this API token, you can configure your client to run models on the cloud
84
- hosted devices.
85
- ```bash
86
- qai-hub configure --api_token API_TOKEN
87
- ```
88
- Navigate to [docs](https://workbench.aihub.qualcomm.com/docs/) for more information.
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-
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-
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-
92
- ## Demo off target
93
-
94
- The package contains a simple end-to-end demo that downloads pre-trained
95
- weights and runs this model on a sample input.
96
-
97
- ```bash
98
- python -m qai_hub_models.models.depth_anything.demo
99
- ```
100
-
101
- The above demo runs a reference implementation of pre-processing, model
102
- inference, and post processing.
103
-
104
- **NOTE**: If you want running in a Jupyter Notebook or Google Colab like
105
- environment, please add the following to your cell (instead of the above).
106
- ```
107
- %run -m qai_hub_models.models.depth_anything.demo
108
- ```
109
-
110
-
111
- ### Run model on a cloud-hosted device
112
-
113
- In addition to the demo, you can also run the model on a cloud-hosted Qualcomm®
114
- device. This script does the following:
115
- * Performance check on-device on a cloud-hosted device
116
- * Downloads compiled assets that can be deployed on-device for Android.
117
- * Accuracy check between PyTorch and on-device outputs.
118
-
119
- ```bash
120
- python -m qai_hub_models.models.depth_anything.export
121
- ```
122
-
123
-
124
-
125
- ## How does this work?
126
-
127
- This [export script](https://aihub.qualcomm.com/models/depth_anything/qai_hub_models/models/Depth-Anything/export.py)
128
- leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model
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- on-device. Lets go through each step below in detail:
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-
131
- Step 1: **Compile model for on-device deployment**
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-
133
- To compile a PyTorch model for on-device deployment, we first trace the model
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- in memory using the `jit.trace` and then call the `submit_compile_job` API.
135
-
136
- ```python
137
- import torch
138
-
139
- import qai_hub as hub
140
- from qai_hub_models.models.depth_anything import Model
141
-
142
- # Load the model
143
- torch_model = Model.from_pretrained()
144
-
145
- # Device
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- device = hub.Device("Samsung Galaxy S25")
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-
148
- # Trace model
149
- input_shape = torch_model.get_input_spec()
150
- sample_inputs = torch_model.sample_inputs()
151
-
152
- pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()])
153
-
154
- # Compile model on a specific device
155
- compile_job = hub.submit_compile_job(
156
- model=pt_model,
157
- device=device,
158
- input_specs=torch_model.get_input_spec(),
159
- )
160
-
161
- # Get target model to run on-device
162
- target_model = compile_job.get_target_model()
163
-
164
- ```
165
-
166
-
167
- Step 2: **Performance profiling on cloud-hosted device**
168
-
169
- After compiling models from step 1. Models can be profiled model on-device using the
170
- `target_model`. Note that this scripts runs the model on a device automatically
171
- provisioned in the cloud. Once the job is submitted, you can navigate to a
172
- provided job URL to view a variety of on-device performance metrics.
173
- ```python
174
- profile_job = hub.submit_profile_job(
175
- model=target_model,
176
- device=device,
177
- )
178
-
179
- ```
180
-
181
- Step 3: **Verify on-device accuracy**
182
-
183
- To verify the accuracy of the model on-device, you can run on-device inference
184
- on sample input data on the same cloud hosted device.
185
- ```python
186
- input_data = torch_model.sample_inputs()
187
- inference_job = hub.submit_inference_job(
188
- model=target_model,
189
- device=device,
190
- inputs=input_data,
191
- )
192
- on_device_output = inference_job.download_output_data()
193
-
194
- ```
195
- With the output of the model, you can compute like PSNR, relative errors or
196
- spot check the output with expected output.
197
-
198
- **Note**: This on-device profiling and inference requires access to Qualcomm®
199
- AI Hub Workbench. [Sign up for access](https://myaccount.qualcomm.com/signup).
200
-
201
-
202
-
203
- ## Run demo on a cloud-hosted device
204
-
205
- You can also run the demo on-device.
206
-
207
- ```bash
208
- python -m qai_hub_models.models.depth_anything.demo --eval-mode on-device
209
- ```
210
-
211
- **NOTE**: If you want running in a Jupyter Notebook or Google Colab like
212
- environment, please add the following to your cell (instead of the above).
213
- ```
214
- %run -m qai_hub_models.models.depth_anything.demo -- --eval-mode on-device
215
- ```
216
-
217
-
218
- ## Deploying compiled model to Android
219
-
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-
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- The models can be deployed using multiple runtimes:
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- - TensorFlow Lite (`.tflite` export): [This
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- tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a
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- guide to deploy the .tflite model in an Android application.
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-
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-
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- - QNN (`.so` export ): This [sample
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- app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html)
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- provides instructions on how to use the `.so` shared library in an Android application.
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-
231
-
232
- ## View on Qualcomm® AI Hub
233
- Get more details on Depth-Anything's performance across various devices [here](https://aihub.qualcomm.com/models/depth_anything).
234
- Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
235
-
236
 
237
  ## License
238
  * The license for the original implementation of Depth-Anything can be found
239
  [here](https://github.com/huggingface/transformers/blob/main/LICENSE).
240
 
241
-
242
-
243
  ## References
244
  * [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://arxiv.org/abs/2401.10891)
245
  * [Source Model Implementation](https://github.com/huggingface/transformers/tree/main/src/transformers/models/depth_anything)
246
 
247
-
248
-
249
  ## Community
250
  * Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
251
  * For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
252
-
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-
 
9
 
10
  ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/depth_anything/web-assets/model_demo.png)
11
 
12
+ # Depth-Anything: Optimized for Qualcomm Devices
 
 
13
 
14
  Depth Anything is designed for estimating depth at each point in an image.
15
 
16
+ This is based on the implementation of Depth-Anything found [here](https://github.com/huggingface/transformers/tree/main/src/transformers/models/depth_anything).
17
+ This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/depth_anything) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
18
+
19
+ Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.com) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) to run these models on a hosted Qualcomm® device.
20
+
21
+ ## Getting Started
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+ There are two ways to deploy this model on your device:
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+
24
+ ### Option 1: Download Pre-Exported Models
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+
26
+ Below are pre-exported model assets ready for deployment.
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+
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+ | Runtime | Precision | Chipset | SDK Versions | Download |
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+ |---|---|---|---|---|
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+ | ONNX | float | Universal | QAIRT 2.37, ONNX Runtime 1.23.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/depth_anything/releases/v0.46.1/depth_anything-onnx-float.zip)
31
+ | QNN_DLC | float | Universal | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/depth_anything/releases/v0.46.1/depth_anything-qnn_dlc-float.zip)
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+ | QNN_DLC | w8a16 | Universal | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/depth_anything/releases/v0.46.1/depth_anything-qnn_dlc-w8a16.zip)
33
+ | TFLITE | float | Universal | QAIRT 2.42, TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/depth_anything/releases/v0.46.1/depth_anything-tflite-float.zip)
34
+
35
+ For more device-specific assets and performance metrics, visit **[Depth-Anything on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/depth_anything)**.
36
+
37
+
38
+ ### Option 2: Export with Custom Configurations
39
+
40
+ Use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/depth_anything) Python library to compile and export the model with your own:
41
+ - Custom weights (e.g., fine-tuned checkpoints)
42
+ - Custom input shapes
43
+ - Target device and runtime configurations
44
+
45
+ This option is ideal if you need to customize the model beyond the default configuration provided here.
46
+
47
+ See our repository for [Depth-Anything on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/depth_anything) for usage instructions.
48
+
49
+ ## Model Details
50
+
51
+ **Model Type:** Model_use_case.depth_estimation
52
+
53
+ **Model Stats:**
54
+ - Model checkpoint: DepthAnything_Small
55
+ - Input resolution: 518x518
56
+ - Number of parameters: 24.7M
57
+ - Model size (float): 94.3 MB
58
+
59
+ ## Performance Summary
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+ | Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
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+ |---|---|---|---|---|---|---
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+ | Depth-Anything | ONNX | float | Snapdragon® X Elite | 75.045 ms | 61 - 61 MB | NPU
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+ | Depth-Anything | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 59.344 ms | 0 - 827 MB | NPU
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+ | Depth-Anything | ONNX | float | Qualcomm® QCS8550 (Proxy) | 75.839 ms | 0 - 64 MB | NPU
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+ | Depth-Anything | ONNX | float | Qualcomm® QCS9075 | 92.448 ms | 3 - 9 MB | NPU
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+ | Depth-Anything | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 45.782 ms | 2 - 679 MB | NPU
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+ | Depth-Anything | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 38.518 ms | 2 - 659 MB | NPU
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+ | Depth-Anything | QNN_DLC | float | Snapdragon® X Elite | 75.519 ms | 3 - 3 MB | NPU
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+ | Depth-Anything | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 55.062 ms | 3 - 662 MB | NPU
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+ | Depth-Anything | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 175.317 ms | 0 - 492 MB | NPU
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+ | Depth-Anything | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 74.553 ms | 3 - 6 MB | NPU
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+ | Depth-Anything | QNN_DLC | float | Qualcomm® SA8775P | 80.825 ms | 1 - 494 MB | NPU
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+ | Depth-Anything | QNN_DLC | float | Qualcomm® QCS9075 | 96.626 ms | 3 - 9 MB | NPU
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+ | Depth-Anything | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 117.538 ms | 2 - 610 MB | NPU
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+ | Depth-Anything | QNN_DLC | float | Qualcomm® SA7255P | 175.317 ms | 0 - 492 MB | NPU
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+ | Depth-Anything | QNN_DLC | float | Qualcomm® SA8295P | 104.294 ms | 0 - 520 MB | NPU
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+ | Depth-Anything | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 41.091 ms | 2 - 493 MB | NPU
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+ | Depth-Anything | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 36.448 ms | 3 - 504 MB | NPU
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+ | Depth-Anything | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 51.037 ms | 0 - 604 MB | NPU
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+ | Depth-Anything | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 176.714 ms | 1 - 451 MB | NPU
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+ | Depth-Anything | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 72.648 ms | 1 - 5 MB | NPU
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+ | Depth-Anything | TFLITE | float | Qualcomm® SA8775P | 78.624 ms | 0 - 449 MB | NPU
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+ | Depth-Anything | TFLITE | float | Qualcomm® QCS9075 | 89.195 ms | 0 - 57 MB | NPU
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+ | Depth-Anything | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 105.73 ms | 0 - 583 MB | NPU
85
+ | Depth-Anything | TFLITE | float | Qualcomm® SA7255P | 176.714 ms | 1 - 451 MB | NPU
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+ | Depth-Anything | TFLITE | float | Qualcomm® SA8295P | 102.07 ms | 1 - 442 MB | NPU
87
+ | Depth-Anything | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 35.448 ms | 1 - 446 MB | NPU
88
+ | Depth-Anything | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 31.027 ms | 1 - 460 MB | NPU
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
89
 
90
  ## License
91
  * The license for the original implementation of Depth-Anything can be found
92
  [here](https://github.com/huggingface/transformers/blob/main/LICENSE).
93
 
 
 
94
  ## References
95
  * [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://arxiv.org/abs/2401.10891)
96
  * [Source Model Implementation](https://github.com/huggingface/transformers/tree/main/src/transformers/models/depth_anything)
97
 
 
 
98
  ## Community
99
  * Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
100
  * For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
 
 
tool-versions.yaml DELETED
@@ -1,4 +0,0 @@
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- tool_versions:
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- onnx:
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- qairt: 2.37.1.250807093845_124904
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- onnx_runtime: 1.23.0