Total runs:
2.5K
Run Growth:
1.7K
Growth Rate:
65.92%
Sponsored by
YouArt
qualcomm / RegNetQuantized
Model's Last Updated: April 10 2025
image-classification
Introduction of RegNetQuantized
Model Details of RegNetQuantized
RegNetQuantized: Optimized for Mobile Deployment
Imagenet classifier and general purpose backbone
RegNet 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.
This model is an implementation of RegNetQuantized found here . This repository provides scripts to run RegNetQuantized on Qualcomm® devices. More details on model performance across various devices, can be found here .
Model Details
- Model Type: Image classification
-
Model Stats:
- Model checkpoint: Imagenet
- Input resolution: 224x224
- Number of parameters: 15.3M
- Model size: 15.4 MB
| Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model |
|---|---|---|---|---|---|---|---|
| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 0.883 ms | 0 - 1 MB | INT8 | NPU | RegNetQuantized.tflite |
| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.008 ms | 0 - 134 MB | INT8 | NPU | RegNetQuantized.so |
Installation
This model can be installed as a Python package via pip.
pip install "qai-hub-models[regnet_quantized]"
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.regnet_quantized.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.regnet_quantized.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.regnet_quantized.export
Profile Job summary of RegNetQuantized
--------------------------------------------------
Device: SA8255 (Proxy) (13)
Estimated Inference Time: 1.00 ms
Estimated Peak Memory Range: 0.16-10.78 MB
Compute Units: NPU (113) | Total (113)
Run demo on a cloud-hosted device
You can also run the demo on-device.
python -m qai_hub_models.models.regnet_quantized.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.regnet_quantized.demo -- --on-device
Deploying compiled model to Android
The models can be deployed using multiple runtimes:
-
TensorFlow Lite (
.tfliteexport): This tutorial provides a guide to deploy the .tflite model in an Android application. -
QNN (
.soexport ): This sample app provides instructions on how to use the.soshared library in an Android application.
View on Qualcomm® AI Hub
Get more details on RegNetQuantized's performance across various devices here . Explore all available models on Qualcomm® AI Hub
License
- The license for the original implementation of RegNetQuantized can be found here .
- The license for the compiled assets for on-device deployment can be found here
References
Community
- Join our AI Hub Slack community to collaborate, post questions and learn more about on-device AI.
- For questions or feedback please reach out to us .
Runs of qualcomm RegNetQuantized on huggingface.co
92
Total runs
0
24-hour runs
0
3-day runs
0
7-day runs
0
30-day runs
More Information About RegNetQuantized huggingface.co Model
Url of RegNetQuantized
RegNetQuantized huggingface.co Url
Provider of RegNetQuantized huggingface.co
Other API from qualcomm
Updated:May 16 2025
Total runs:
1.9K
Run Growth:
1.2K
Growth Rate:
84.98%
Total runs:
1.9K
Run Growth:
525
Growth Rate:
28.15%
Total runs:
1.8K
Run Growth:
1.2K
Growth Rate:
65.55%
Total runs:
1.7K
Run Growth:
698
Growth Rate:
41.65%
Total runs:
1.4K
Run Growth:
324
Growth Rate:
29.08%
Total runs:
1.2K
Run Growth:
389
Growth Rate:
37.26%
Total runs:
1.1K
Run Growth:
466
Growth Rate:
44.13%
Total runs:
1.0K
Run Growth:
816
Growth Rate:
78.16%
Total runs:
593
Run Growth:
129
Growth Rate:
21.75%
Total runs:
584
Run Growth:
380
Growth Rate:
65.07%
Total runs:
563
Run Growth:
0
Growth Rate:
0.00%
Total runs:
507
Run Growth:
264
Growth Rate:
70.59%
Total runs:
472
Run Growth:
78
Growth Rate:
16.53%
Total runs:
468
Run Growth:
0
Growth Rate:
0.00%
Total runs:
444
Run Growth:
0
Growth Rate:
0.00%
Total runs:
418
Run Growth:
0
Growth Rate:
0.00%
Total runs:
412
Run Growth:
-13
Growth Rate:
-3.16%
Total runs:
411
Run Growth:
0
Growth Rate:
0.00%
Total runs:
396
Run Growth:
0
Growth Rate:
0.00%
Total runs:
371
Run Growth:
-5
Growth Rate:
-1.35%
Total runs:
336
Run Growth:
0
Growth Rate:
0.00%
Total runs:
316
Run Growth:
-600
Growth Rate:
-189.87%
Total runs:
278
Run Growth:
-643
Growth Rate:
-231.29%
Total runs:
261
Run Growth:
-78
Growth Rate:
-29.89%
Total runs:
260
Run Growth:
169
Growth Rate:
67.33%
Total runs:
245
Run Growth:
30
Growth Rate:
12.24%
Total runs:
234
Run Growth:
27
Growth Rate:
11.54%
Total runs:
205
Run Growth:
0
Growth Rate:
0.00%
Total runs:
204
Run Growth:
95
Growth Rate:
45.89%
Total runs:
202
Run Growth:
40
Growth Rate:
19.80%
Total runs:
183
Run Growth:
-63
Growth Rate:
-34.43%
Total runs:
180
Run Growth:
122
Growth Rate:
67.78%
Total runs:
172
Run Growth:
-20
Growth Rate:
-11.63%
Total runs:
172
Run Growth:
-20
Growth Rate:
-11.63%
Total runs:
163
Run Growth:
-397
Growth Rate:
-243.56%
Total runs:
153
Run Growth:
0
Growth Rate:
0.00%
Total runs:
136
Run Growth:
-83
Growth Rate:
-61.03%
Total runs:
115
Run Growth:
78
Growth Rate:
67.83%
Total runs:
109
Run Growth:
0
Growth Rate:
0.00%
Total runs:
107
Run Growth:
-46
Growth Rate:
-42.99%
Total runs:
105
Run Growth:
0
Growth Rate:
0.00%
Total runs:
97
Run Growth:
0
Growth Rate:
0.00%
Total runs:
93
Run Growth:
0
Growth Rate:
0.00%
Total runs:
87
Run Growth:
0
Growth Rate:
0.00%
Total runs:
82
Run Growth:
-54
Growth Rate:
-65.85%
Total runs:
81
Run Growth:
4
Growth Rate:
4.94%
Total runs:
72
Run Growth:
0
Growth Rate:
0.00%
Total runs:
70
Run Growth:
31
Growth Rate:
44.29%