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LiteHRNet

Keypoint Detection
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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. LiteHRNet_float.dlc +0 -3
  2. LiteHRNet_float.onnx.zip +0 -3
  3. LiteHRNet_float.tflite +0 -3
  4. README.md +72 -227
  5. tool-versions.yaml +0 -4
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  ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/litehrnet/web-assets/model_demo.png)
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- # LiteHRNet: Optimized for Mobile Deployment
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- ## Human pose estimation
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-
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  LiteHRNet is a machine learning model that detects human pose and returns a location and confidence for each of 17 joints.
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- This model is an implementation of LiteHRNet found [here](https://github.com/HRNet/Lite-HRNet).
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-
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-
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- This repository provides scripts to run LiteHRNet 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/litehrnet).
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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.pose_estimation
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- - **Model Stats:**
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- - Input resolution: 256x192
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- - Number of parameters: 1.11M
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- - Model size (float): 4.49 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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- | LiteHRNet | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 8.616 ms | 0 - 185 MB | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
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- | LiteHRNet | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 4.888 ms | 1 - 161 MB | NPU | [LiteHRNet.dlc](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.dlc) |
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- | LiteHRNet | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 5.321 ms | 0 - 215 MB | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
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- | LiteHRNet | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 2.825 ms | 1 - 193 MB | NPU | [LiteHRNet.dlc](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.dlc) |
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- | LiteHRNet | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 4.181 ms | 0 - 2 MB | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
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- | LiteHRNet | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 2.058 ms | 1 - 3 MB | NPU | [LiteHRNet.dlc](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.dlc) |
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- | LiteHRNet | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | ONNX | 5.58 ms | 0 - 7 MB | NPU | [LiteHRNet.onnx.zip](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.onnx.zip) |
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- | LiteHRNet | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 5.182 ms | 0 - 185 MB | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
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- | LiteHRNet | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 10.911 ms | 1 - 161 MB | NPU | [LiteHRNet.dlc](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.dlc) |
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- | LiteHRNet | float | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 8.616 ms | 0 - 185 MB | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
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- | LiteHRNet | float | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 4.888 ms | 1 - 161 MB | NPU | [LiteHRNet.dlc](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.dlc) |
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- | LiteHRNet | float | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 6.196 ms | 0 - 194 MB | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
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- | LiteHRNet | float | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 3.432 ms | 0 - 170 MB | NPU | [LiteHRNet.dlc](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.dlc) |
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- | LiteHRNet | float | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 5.182 ms | 0 - 185 MB | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
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- | LiteHRNet | float | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 10.911 ms | 1 - 161 MB | NPU | [LiteHRNet.dlc](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.dlc) |
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- | LiteHRNet | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 2.686 ms | 0 - 218 MB | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
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- | LiteHRNet | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 1.351 ms | 1 - 192 MB | NPU | [LiteHRNet.dlc](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.dlc) |
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- | LiteHRNet | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 3.401 ms | 0 - 177 MB | NPU | [LiteHRNet.onnx.zip](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.onnx.zip) |
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- | LiteHRNet | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | TFLITE | 2.26 ms | 0 - 184 MB | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
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- | LiteHRNet | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | QNN_DLC | 1.076 ms | 1 - 168 MB | NPU | [LiteHRNet.dlc](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.dlc) |
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- | LiteHRNet | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | ONNX | 2.914 ms | 0 - 148 MB | NPU | [LiteHRNet.onnx.zip](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.onnx.zip) |
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- | LiteHRNet | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | TFLITE | 2.057 ms | 0 - 187 MB | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
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- | LiteHRNet | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | QNN_DLC | 0.889 ms | 1 - 164 MB | NPU | [LiteHRNet.dlc](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.dlc) |
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- | LiteHRNet | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | ONNX | 2.789 ms | 0 - 149 MB | NPU | [LiteHRNet.onnx.zip](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.onnx.zip) |
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- | LiteHRNet | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 2.376 ms | 1 - 1 MB | NPU | [LiteHRNet.dlc](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.dlc) |
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- | LiteHRNet | float | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 5.863 ms | 4 - 4 MB | NPU | [LiteHRNet.onnx.zip](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.onnx.zip) |
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-
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-
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-
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-
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- ## Installation
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-
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-
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- Install the package via pip:
71
- ```bash
72
- # NOTE: 3.10 <= PYTHON_VERSION < 3.14 is supported.
73
- pip install mmpose==1.2.0 --no-deps
74
- pip install "qai-hub-models[litehrnet]"
75
- ```
76
-
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-
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`.
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-
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.litehrnet.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.litehrnet.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
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- python -m qai_hub_models.models.litehrnet.export
121
- ```
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-
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-
124
-
125
- ## How does this work?
126
-
127
- This [export script](https://aihub.qualcomm.com/models/litehrnet/qai_hub_models/models/LiteHRNet/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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-
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- Step 1: **Compile model for on-device deployment**
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-
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- To compile a PyTorch model for on-device deployment, we first trace the model
134
- 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.litehrnet import Model
141
-
142
- # Load the model
143
- torch_model = Model.from_pretrained()
144
-
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- # Device
146
- 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()
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-
152
- pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()])
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-
154
- # Compile model on a specific device
155
- compile_job = hub.submit_compile_job(
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- model=pt_model,
157
- device=device,
158
- input_specs=torch_model.get_input_spec(),
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- )
160
-
161
- # Get target model to run on-device
162
- target_model = compile_job.get_target_model()
163
-
164
- ```
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-
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-
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
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- `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
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- provided job URL to view a variety of on-device performance metrics.
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- ```python
174
- profile_job = hub.submit_profile_job(
175
- model=target_model,
176
- device=device,
177
- )
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-
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
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- 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,
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- device=device,
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- 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).
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-
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-
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-
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- ## Run demo on a cloud-hosted device
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-
205
- You can also run the demo on-device.
206
-
207
- ```bash
208
- python -m qai_hub_models.models.litehrnet.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.litehrnet.demo -- --eval-mode on-device
215
- ```
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-
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-
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- ## Deploying compiled model to Android
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-
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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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-
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-
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- ## View on Qualcomm® AI Hub
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- Get more details on LiteHRNet's performance across various devices [here](https://aihub.qualcomm.com/models/litehrnet).
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 LiteHRNet can be found
239
  [here](https://github.com/HRNet/Lite-HRNet/blob/hrnet/LICENSE).
240
 
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-
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-
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  ## References
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  * [Lite-HRNet: A Lightweight High-Resolution Network](https://arxiv.org/abs/2104.06403)
245
  * [Source Model Implementation](https://github.com/HRNet/Lite-HRNet)
246
 
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-
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
-
253
-
 
9
 
10
  ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/litehrnet/web-assets/model_demo.png)
11
 
12
+ # LiteHRNet: Optimized for Qualcomm Devices
 
 
13
 
14
  LiteHRNet is a machine learning model that detects human pose and returns a location and confidence for each of 17 joints.
15
 
16
+ This is based on the implementation of LiteHRNet found [here](https://github.com/HRNet/Lite-HRNet).
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/litehrnet) 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
22
+ There are two ways to deploy this model on your device:
23
+
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 |
29
+ |---|---|---|---|---|
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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/litehrnet/releases/v0.46.1/litehrnet-onnx-float.zip)
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+ | QNN_DLC | float | Universal | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/litehrnet/releases/v0.46.1/litehrnet-qnn_dlc-float.zip)
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+ | 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/litehrnet/releases/v0.46.1/litehrnet-tflite-float.zip)
33
+
34
+ For more device-specific assets and performance metrics, visit **[LiteHRNet on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/litehrnet)**.
35
+
36
+
37
+ ### Option 2: Export with Custom Configurations
38
+
39
+ Use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/litehrnet) Python library to compile and export the model with your own:
40
+ - Custom weights (e.g., fine-tuned checkpoints)
41
+ - Custom input shapes
42
+ - Target device and runtime configurations
43
+
44
+ This option is ideal if you need to customize the model beyond the default configuration provided here.
45
+
46
+ See our repository for [LiteHRNet on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/litehrnet) for usage instructions.
47
+
48
+ ## Model Details
49
+
50
+ **Model Type:** Model_use_case.pose_estimation
51
+
52
+ **Model Stats:**
53
+ - Input resolution: 256x192
54
+ - Number of parameters: 1.11M
55
+ - Model size (float): 4.49 MB
56
+
57
+ ## Performance Summary
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+ | Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
59
+ |---|---|---|---|---|---|---
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+ | LiteHRNet | ONNX | float | Snapdragon® X Elite | 5.871 ms | 4 - 4 MB | NPU
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+ | LiteHRNet | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 3.412 ms | 0 - 175 MB | NPU
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+ | LiteHRNet | ONNX | float | Qualcomm® QCS8550 (Proxy) | 5.591 ms | 0 - 158 MB | NPU
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+ | LiteHRNet | ONNX | float | Qualcomm® QCS9075 | 6.235 ms | 1 - 4 MB | NPU
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+ | LiteHRNet | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 2.928 ms | 0 - 149 MB | NPU
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+ | LiteHRNet | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 2.763 ms | 0 - 149 MB | NPU
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+ | LiteHRNet | QNN_DLC | float | Snapdragon® X Elite | 2.393 ms | 1 - 1 MB | NPU
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+ | LiteHRNet | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 1.347 ms | 0 - 110 MB | NPU
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+ | LiteHRNet | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 4.887 ms | 1 - 80 MB | NPU
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+ | LiteHRNet | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 2.065 ms | 1 - 118 MB | NPU
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+ | LiteHRNet | QNN_DLC | float | Qualcomm® SA8775P | 2.662 ms | 1 - 82 MB | NPU
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+ | LiteHRNet | QNN_DLC | float | Qualcomm® QCS9075 | 2.487 ms | 1 - 3 MB | NPU
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+ | LiteHRNet | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 2.915 ms | 0 - 107 MB | NPU
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+ | LiteHRNet | QNN_DLC | float | Qualcomm® SA7255P | 4.887 ms | 1 - 80 MB | NPU
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+ | LiteHRNet | QNN_DLC | float | Qualcomm® SA8295P | 3.449 ms | 0 - 82 MB | NPU
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+ | LiteHRNet | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 1.024 ms | 1 - 85 MB | NPU
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+ | LiteHRNet | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.847 ms | 1 - 84 MB | NPU
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+ | LiteHRNet | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 2.684 ms | 0 - 159 MB | NPU
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+ | LiteHRNet | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 8.657 ms | 0 - 119 MB | NPU
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+ | LiteHRNet | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 4.25 ms | 0 - 12 MB | NPU
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+ | LiteHRNet | TFLITE | float | Qualcomm® SA8775P | 5.259 ms | 0 - 119 MB | NPU
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+ | LiteHRNet | TFLITE | float | Qualcomm® QCS9075 | 5.046 ms | 0 - 10 MB | NPU
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+ | LiteHRNet | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 5.332 ms | 0 - 139 MB | NPU
83
+ | LiteHRNet | TFLITE | float | Qualcomm® SA7255P | 8.657 ms | 0 - 119 MB | NPU
84
+ | LiteHRNet | TFLITE | float | Qualcomm® SA8295P | 6.273 ms | 0 - 117 MB | NPU
85
+ | LiteHRNet | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 2.217 ms | 0 - 122 MB | NPU
86
+ | LiteHRNet | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 2.017 ms | 0 - 122 MB | NPU
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
87
 
88
  ## License
89
  * The license for the original implementation of LiteHRNet can be found
90
  [here](https://github.com/HRNet/Lite-HRNet/blob/hrnet/LICENSE).
91
 
 
 
92
  ## References
93
  * [Lite-HRNet: A Lightweight High-Resolution Network](https://arxiv.org/abs/2104.06403)
94
  * [Source Model Implementation](https://github.com/HRNet/Lite-HRNet)
95
 
 
 
96
  ## Community
97
  * Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
98
  * For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
 
 
tool-versions.yaml DELETED
@@ -1,4 +0,0 @@
1
- tool_versions:
2
- onnx:
3
- qairt: 2.37.1.250807093845_124904
4
- onnx_runtime: 1.23.0