Total runs:
3.1K
Run Growth:
-21
Growth Rate:
-0.68%
Sponsored by
Raccoon AI
qualcomm / WideResNet50-Quantized
Model's Last Updated: April 10 2025
image-classification
Introduction of WideResNet50-Quantized
Model Details of WideResNet50-Quantized
WideResNet50-Quantized: Optimized for Mobile Deployment
Imagenet classifier and general purpose backbone
WideResNet50 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 WideResNet50-Quantized found here . This repository provides scripts to run WideResNet50-Quantized 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: 68.9M
- Model size: 66.6 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 | 1.776 ms | 0 - 2 MB | INT8 | NPU | WideResNet50-Quantized.tflite |
| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 2.031 ms | 0 - 361 MB | INT8 | NPU | WideResNet50-Quantized.so |
Installation
This model can be installed as a Python package via pip.
pip install "qai-hub-models[wideresnet50_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.wideresnet50_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.wideresnet50_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.wideresnet50_quantized.export
Profile Job summary of WideResNet50-Quantized
--------------------------------------------------
Device: SA8255 (Proxy) (13)
Estimated Inference Time: 2.03 ms
Estimated Peak Memory Range: 0.01-413.72 MB
Compute Units: NPU (78) | Total (78)
Run demo on a cloud-hosted device
You can also run the demo on-device.
python -m qai_hub_models.models.wideresnet50_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.wideresnet50_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 WideResNet50-Quantized's performance across various devices here . Explore all available models on Qualcomm® AI Hub
License
- The license for the original implementation of WideResNet50-Quantized 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 WideResNet50-Quantized on huggingface.co
97
Total runs
0
24-hour runs
0
3-day runs
0
7-day runs
0
30-day runs
More Information About WideResNet50-Quantized huggingface.co Model
Url of WideResNet50-Quantized
WideResNet50-Quantized huggingface.co Url
Provider of WideResNet50-Quantized huggingface.co
Other API from qualcomm
Updated:April 08 2026
Total runs:
1.4K
Run Growth:
1.2K
Growth Rate:
88.58%
Total runs:
1.1K
Run Growth:
466
Growth Rate:
44.13%
Total runs:
812
Run Growth:
-660
Growth Rate:
-79.81%
Total runs:
728
Run Growth:
36
Growth Rate:
4.80%
Total runs:
701
Run Growth:
585
Growth Rate:
82.74%
Total runs:
661
Run Growth:
-92
Growth Rate:
-13.92%
Total runs:
629
Run Growth:
36
Growth Rate:
5.72%
Total runs:
628
Run Growth:
-112
Growth Rate:
-17.72%
Total runs:
624
Run Growth:
334
Growth Rate:
53.53%
Total runs:
593
Run Growth:
129
Growth Rate:
21.75%
Total runs:
584
Run Growth:
380
Growth Rate:
65.07%
Total runs:
584
Run Growth:
465
Growth Rate:
79.62%
Total runs:
563
Run Growth:
0
Growth Rate:
0.00%
Total runs:
549
Run Growth:
330
Growth Rate:
60.11%
Total runs:
504
Run Growth:
100
Growth Rate:
19.84%
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:
418
Run Growth:
-56
Growth Rate:
-12.70%
Total runs:
411
Run Growth:
0
Growth Rate:
0.00%
Total runs:
399
Run Growth:
352
Growth Rate:
88.22%
Total runs:
396
Run Growth:
0
Growth Rate:
0.00%
Total runs:
380
Run Growth:
282
Growth Rate:
77.05%
Total runs:
371
Run Growth:
-5
Growth Rate:
-1.35%
Total runs:
336
Run Growth:
0
Growth Rate:
0.00%
Total runs:
298
Run Growth:
277
Growth Rate:
92.95%
Total runs:
278
Run Growth:
-643
Growth Rate:
-231.29%
Total runs:
205
Run Growth:
0
Growth Rate:
0.00%
Total runs:
195
Run Growth:
165
Growth Rate:
84.62%
Total runs:
184
Run Growth:
-56
Growth Rate:
-28.14%
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:
148
Run Growth:
-84
Growth Rate:
-49.41%
Total runs:
116
Run Growth:
27
Growth Rate:
22.69%
Total runs:
116
Run Growth:
14
Growth Rate:
12.07%
Total runs:
115
Run Growth:
78
Growth Rate:
67.83%
Total runs:
109
Run Growth:
0
Growth Rate:
0.00%
Total runs:
105
Run Growth:
0
Growth Rate:
0.00%
Total runs:
93
Run Growth:
0
Growth Rate:
0.00%
Total runs:
89
Run Growth:
-96
Growth Rate:
-107.87%
Total runs:
87
Run Growth:
0
Growth Rate:
0.00%
Total runs:
82
Run Growth:
-54
Growth Rate:
-65.85%