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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. Beit_float.dlc +0 -3
  2. Beit_float.onnx.zip +0 -3
  3. Beit_float.tflite +0 -3
  4. README.md +74 -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/beit/web-assets/model_demo.png)
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- # Beit: Optimized for Mobile Deployment
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- ## Imagenet classifier and general purpose backbone
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-
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  Beit is a machine learning model that can classify images from the Imagenet dataset. It can also be used as a backbone in building more complex models for specific use cases.
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- This model is an implementation of Beit found [here](https://github.com/microsoft/unilm/tree/master/beit).
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-
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-
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- This repository provides scripts to run Beit 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/beit).
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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.image_classification
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- - **Model Stats:**
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- - Model checkpoint: Imagenet
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- - Input resolution: 224x224
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- - Number of parameters: 92.0M
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- - Model size (float): 351 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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- | Beit | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 38.212 ms | 0 - 454 MB | NPU | [Beit.tflite](https://huggingface.co/qualcomm/Beit/blob/main/Beit.tflite) |
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- | Beit | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 42.233 ms | 1 - 468 MB | NPU | [Beit.dlc](https://huggingface.co/qualcomm/Beit/blob/main/Beit.dlc) |
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- | Beit | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 19.369 ms | 0 - 481 MB | NPU | [Beit.tflite](https://huggingface.co/qualcomm/Beit/blob/main/Beit.tflite) |
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- | Beit | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 23.194 ms | 1 - 490 MB | NPU | [Beit.dlc](https://huggingface.co/qualcomm/Beit/blob/main/Beit.dlc) |
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- | Beit | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 9.139 ms | 0 - 4 MB | NPU | [Beit.tflite](https://huggingface.co/qualcomm/Beit/blob/main/Beit.tflite) |
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- | Beit | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 12.03 ms | 1 - 3 MB | NPU | [Beit.dlc](https://huggingface.co/qualcomm/Beit/blob/main/Beit.dlc) |
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- | Beit | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | ONNX | 13.497 ms | 0 - 193 MB | NPU | [Beit.onnx.zip](https://huggingface.co/qualcomm/Beit/blob/main/Beit.onnx.zip) |
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- | Beit | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 12.128 ms | 0 - 455 MB | NPU | [Beit.tflite](https://huggingface.co/qualcomm/Beit/blob/main/Beit.tflite) |
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- | Beit | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 67.728 ms | 1 - 468 MB | NPU | [Beit.dlc](https://huggingface.co/qualcomm/Beit/blob/main/Beit.dlc) |
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- | Beit | float | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 38.212 ms | 0 - 454 MB | NPU | [Beit.tflite](https://huggingface.co/qualcomm/Beit/blob/main/Beit.tflite) |
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- | Beit | float | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 42.233 ms | 1 - 468 MB | NPU | [Beit.dlc](https://huggingface.co/qualcomm/Beit/blob/main/Beit.dlc) |
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- | Beit | float | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 16.879 ms | 0 - 447 MB | NPU | [Beit.tflite](https://huggingface.co/qualcomm/Beit/blob/main/Beit.tflite) |
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- | Beit | float | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 19.772 ms | 1 - 457 MB | NPU | [Beit.dlc](https://huggingface.co/qualcomm/Beit/blob/main/Beit.dlc) |
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- | Beit | float | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 12.128 ms | 0 - 455 MB | NPU | [Beit.tflite](https://huggingface.co/qualcomm/Beit/blob/main/Beit.tflite) |
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- | Beit | float | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 67.728 ms | 1 - 468 MB | NPU | [Beit.dlc](https://huggingface.co/qualcomm/Beit/blob/main/Beit.dlc) |
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- | Beit | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 6.384 ms | 0 - 509 MB | NPU | [Beit.tflite](https://huggingface.co/qualcomm/Beit/blob/main/Beit.tflite) |
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- | Beit | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 8.192 ms | 1 - 520 MB | NPU | [Beit.dlc](https://huggingface.co/qualcomm/Beit/blob/main/Beit.dlc) |
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- | Beit | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 9.867 ms | 1 - 525 MB | NPU | [Beit.onnx.zip](https://huggingface.co/qualcomm/Beit/blob/main/Beit.onnx.zip) |
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- | Beit | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | TFLITE | 4.796 ms | 0 - 434 MB | NPU | [Beit.tflite](https://huggingface.co/qualcomm/Beit/blob/main/Beit.tflite) |
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- | Beit | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | QNN_DLC | 6.829 ms | 1 - 451 MB | NPU | [Beit.dlc](https://huggingface.co/qualcomm/Beit/blob/main/Beit.dlc) |
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- | Beit | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | ONNX | 7.128 ms | 0 - 444 MB | NPU | [Beit.onnx.zip](https://huggingface.co/qualcomm/Beit/blob/main/Beit.onnx.zip) |
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- | Beit | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | TFLITE | 4.197 ms | 0 - 433 MB | NPU | [Beit.tflite](https://huggingface.co/qualcomm/Beit/blob/main/Beit.tflite) |
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- | Beit | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | QNN_DLC | 5.21 ms | 1 - 445 MB | NPU | [Beit.dlc](https://huggingface.co/qualcomm/Beit/blob/main/Beit.dlc) |
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- | Beit | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | ONNX | 5.928 ms | 1 - 433 MB | NPU | [Beit.onnx.zip](https://huggingface.co/qualcomm/Beit/blob/main/Beit.onnx.zip) |
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- | Beit | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 12.735 ms | 1 - 1 MB | NPU | [Beit.dlc](https://huggingface.co/qualcomm/Beit/blob/main/Beit.dlc) |
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- | Beit | float | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 14.783 ms | 186 - 186 MB | NPU | [Beit.onnx.zip](https://huggingface.co/qualcomm/Beit/blob/main/Beit.onnx.zip) |
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-
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-
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-
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-
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- ## Installation
70
-
71
-
72
- Install the package via pip:
73
- ```bash
74
- # NOTE: 3.10 <= PYTHON_VERSION < 3.14 is supported.
75
- pip install "qai-hub-models[beit]"
76
- ```
77
-
78
-
79
- ## Configure Qualcomm® AI Hub Workbench to run this model on a cloud-hosted device
80
-
81
- Sign-in to [Qualcomm® AI Hub Workbench](https://workbench.aihub.qualcomm.com/) with your
82
- Qualcomm® ID. Once signed in navigate to `Account -> Settings -> API Token`.
83
-
84
- With this API token, you can configure your client to run models on the cloud
85
- hosted devices.
86
- ```bash
87
- qai-hub configure --api_token API_TOKEN
88
- ```
89
- Navigate to [docs](https://workbench.aihub.qualcomm.com/docs/) for more information.
90
-
91
-
92
-
93
- ## Demo off target
94
-
95
- The package contains a simple end-to-end demo that downloads pre-trained
96
- weights and runs this model on a sample input.
97
-
98
- ```bash
99
- python -m qai_hub_models.models.beit.demo
100
- ```
101
-
102
- The above demo runs a reference implementation of pre-processing, model
103
- inference, and post processing.
104
-
105
- **NOTE**: If you want running in a Jupyter Notebook or Google Colab like
106
- environment, please add the following to your cell (instead of the above).
107
- ```
108
- %run -m qai_hub_models.models.beit.demo
109
- ```
110
-
111
-
112
- ### Run model on a cloud-hosted device
113
-
114
- In addition to the demo, you can also run the model on a cloud-hosted Qualcomm®
115
- device. This script does the following:
116
- * Performance check on-device on a cloud-hosted device
117
- * Downloads compiled assets that can be deployed on-device for Android.
118
- * Accuracy check between PyTorch and on-device outputs.
119
-
120
- ```bash
121
- python -m qai_hub_models.models.beit.export
122
- ```
123
-
124
-
125
-
126
- ## How does this work?
127
-
128
- This [export script](https://aihub.qualcomm.com/models/beit/qai_hub_models/models/Beit/export.py)
129
- leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model
130
- on-device. Lets go through each step below in detail:
131
-
132
- Step 1: **Compile model for on-device deployment**
133
-
134
- To compile a PyTorch model for on-device deployment, we first trace the model
135
- in memory using the `jit.trace` and then call the `submit_compile_job` API.
136
-
137
- ```python
138
- import torch
139
-
140
- import qai_hub as hub
141
- from qai_hub_models.models.beit import Model
142
-
143
- # Load the model
144
- torch_model = Model.from_pretrained()
145
-
146
- # Device
147
- device = hub.Device("Samsung Galaxy S25")
148
-
149
- # Trace model
150
- input_shape = torch_model.get_input_spec()
151
- sample_inputs = torch_model.sample_inputs()
152
-
153
- pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()])
154
-
155
- # Compile model on a specific device
156
- compile_job = hub.submit_compile_job(
157
- model=pt_model,
158
- device=device,
159
- input_specs=torch_model.get_input_spec(),
160
- )
161
-
162
- # Get target model to run on-device
163
- target_model = compile_job.get_target_model()
164
-
165
- ```
166
-
167
-
168
- Step 2: **Performance profiling on cloud-hosted device**
169
-
170
- After compiling models from step 1. Models can be profiled model on-device using the
171
- `target_model`. Note that this scripts runs the model on a device automatically
172
- provisioned in the cloud. Once the job is submitted, you can navigate to a
173
- provided job URL to view a variety of on-device performance metrics.
174
- ```python
175
- profile_job = hub.submit_profile_job(
176
- model=target_model,
177
- device=device,
178
- )
179
-
180
- ```
181
-
182
- Step 3: **Verify on-device accuracy**
183
-
184
- To verify the accuracy of the model on-device, you can run on-device inference
185
- on sample input data on the same cloud hosted device.
186
- ```python
187
- input_data = torch_model.sample_inputs()
188
- inference_job = hub.submit_inference_job(
189
- model=target_model,
190
- device=device,
191
- inputs=input_data,
192
- )
193
- on_device_output = inference_job.download_output_data()
194
-
195
- ```
196
- With the output of the model, you can compute like PSNR, relative errors or
197
- spot check the output with expected output.
198
-
199
- **Note**: This on-device profiling and inference requires access to Qualcomm®
200
- AI Hub Workbench. [Sign up for access](https://myaccount.qualcomm.com/signup).
201
-
202
-
203
-
204
- ## Run demo on a cloud-hosted device
205
-
206
- You can also run the demo on-device.
207
-
208
- ```bash
209
- python -m qai_hub_models.models.beit.demo --eval-mode on-device
210
- ```
211
-
212
- **NOTE**: If you want running in a Jupyter Notebook or Google Colab like
213
- environment, please add the following to your cell (instead of the above).
214
- ```
215
- %run -m qai_hub_models.models.beit.demo -- --eval-mode on-device
216
- ```
217
-
218
-
219
- ## Deploying compiled model to Android
220
-
221
-
222
- The models can be deployed using multiple runtimes:
223
- - TensorFlow Lite (`.tflite` export): [This
224
- tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a
225
- guide to deploy the .tflite model in an Android application.
226
-
227
-
228
- - QNN (`.so` export ): This [sample
229
- app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html)
230
- provides instructions on how to use the `.so` shared library in an Android application.
231
-
232
-
233
- ## View on Qualcomm® AI Hub
234
- Get more details on Beit's performance across various devices [here](https://aihub.qualcomm.com/models/beit).
235
- Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
236
-
237
 
238
  ## License
239
  * The license for the original implementation of Beit can be found
240
  [here](https://github.com/pytorch/vision/blob/main/LICENSE).
241
 
242
-
243
-
244
  ## References
245
  * [BEIT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254)
246
  * [Source Model Implementation](https://github.com/microsoft/unilm/tree/master/beit)
247
 
248
-
249
-
250
  ## Community
251
  * Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
252
  * For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
253
-
254
-
 
10
 
11
  ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/beit/web-assets/model_demo.png)
12
 
13
+ # Beit: Optimized for Qualcomm Devices
 
 
14
 
15
  Beit is a machine learning model that can classify images from the Imagenet dataset. It can also be used as a backbone in building more complex models for specific use cases.
16
 
17
+ This is based on the implementation of Beit found [here](https://github.com/microsoft/unilm/tree/master/beit).
18
+ 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/beit) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
19
+
20
+ 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.
21
+
22
+ ## Getting Started
23
+ There are two ways to deploy this model on your device:
24
+
25
+ ### Option 1: Download Pre-Exported Models
26
+
27
+ Below are pre-exported model assets ready for deployment.
28
+
29
+ | Runtime | Precision | Chipset | SDK Versions | Download |
30
+ |---|---|---|---|---|
31
+ | 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/beit/releases/v0.46.1/beit-onnx-float.zip)
32
+ | QNN_DLC | float | Universal | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/beit/releases/v0.46.1/beit-qnn_dlc-float.zip)
33
+ | QNN_DLC | w8a16 | Universal | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/beit/releases/v0.46.1/beit-qnn_dlc-w8a16.zip)
34
+ | 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/beit/releases/v0.46.1/beit-tflite-float.zip)
35
+
36
+ For more device-specific assets and performance metrics, visit **[Beit on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/beit)**.
37
+
38
+
39
+ ### Option 2: Export with Custom Configurations
40
+
41
+ Use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/beit) Python library to compile and export the model with your own:
42
+ - Custom weights (e.g., fine-tuned checkpoints)
43
+ - Custom input shapes
44
+ - Target device and runtime configurations
45
+
46
+ This option is ideal if you need to customize the model beyond the default configuration provided here.
47
+
48
+ See our repository for [Beit on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/beit) for usage instructions.
49
+
50
+ ## Model Details
51
+
52
+ **Model Type:** Model_use_case.image_classification
53
+
54
+ **Model Stats:**
55
+ - Model checkpoint: Imagenet
56
+ - Input resolution: 224x224
57
+ - Number of parameters: 92.0M
58
+ - Model size (float): 351 MB
59
+
60
+ ## Performance Summary
61
+ | Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
62
+ |---|---|---|---|---|---|---
63
+ | Beit | ONNX | float | Snapdragon® X Elite | 14.768 ms | 186 - 186 MB | NPU
64
+ | Beit | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 9.876 ms | 0 - 524 MB | NPU
65
+ | Beit | ONNX | float | Qualcomm® QCS8550 (Proxy) | 13.457 ms | 0 - 194 MB | NPU
66
+ | Beit | ONNX | float | Qualcomm® QCS9075 | 20.562 ms | 0 - 4 MB | NPU
67
+ | Beit | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 7.167 ms | 1 - 447 MB | NPU
68
+ | Beit | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 5.932 ms | 0 - 436 MB | NPU
69
+ | Beit | QNN_DLC | float | Snapdragon® X Elite | 13.534 ms | 1 - 1 MB | NPU
70
+ | Beit | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 8.535 ms | 0 - 535 MB | NPU
71
+ | Beit | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 44.873 ms | 1 - 485 MB | NPU
72
+ | Beit | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 12.732 ms | 1 - 2 MB | NPU
73
+ | Beit | QNN_DLC | float | Qualcomm® SA8775P | 15.563 ms | 1 - 485 MB | NPU
74
+ | Beit | QNN_DLC | float | Qualcomm® QCS9075 | 16.84 ms | 1 - 3 MB | NPU
75
+ | Beit | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 22.993 ms | 0 - 507 MB | NPU
76
+ | Beit | QNN_DLC | float | Qualcomm® SA7255P | 44.873 ms | 1 - 485 MB | NPU
77
+ | Beit | QNN_DLC | float | Qualcomm® SA8295P | 19.001 ms | 1 - 468 MB | NPU
78
+ | Beit | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 7.003 ms | 1 - 478 MB | NPU
79
+ | Beit | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 6.475 ms | 1 - 481 MB | NPU
80
+ | Beit | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 6.665 ms | 0 - 350 MB | NPU
81
+ | Beit | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 38.644 ms | 0 - 302 MB | NPU
82
+ | Beit | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 9.671 ms | 0 - 3 MB | NPU
83
+ | Beit | TFLITE | float | Qualcomm® SA8775P | 12.131 ms | 0 - 310 MB | NPU
84
+ | Beit | TFLITE | float | Qualcomm® QCS9075 | 13.331 ms | 0 - 187 MB | NPU
85
+ | Beit | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 19.271 ms | 0 - 433 MB | NPU
86
+ | Beit | TFLITE | float | Qualcomm® SA7255P | 38.644 ms | 0 - 302 MB | NPU
87
+ | Beit | TFLITE | float | Qualcomm® SA8295P | 16.047 ms | 0 - 410 MB | NPU
88
+ | Beit | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 4.824 ms | 0 - 302 MB | NPU
89
+ | Beit | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 4.065 ms | 0 - 304 MB | NPU
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
90
 
91
  ## License
92
  * The license for the original implementation of Beit can be found
93
  [here](https://github.com/pytorch/vision/blob/main/LICENSE).
94
 
 
 
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  ## References
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  * [BEIT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254)
97
  * [Source Model Implementation](https://github.com/microsoft/unilm/tree/master/beit)
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  ## Community
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  * Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
101
  * For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
 
 
tool-versions.yaml DELETED
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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