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ResNeXt50

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 ---
library_name: pytorch
license: other
tags:
- backbone
- bu_auto
- android
pipeline_tag: image-classification
---
![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnext50/web-assets/model_demo.png)
# ResNeXt50: Optimized for Mobile Deployment
## Imagenet classifier and general purpose backbone
ResNeXt50 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 ResNeXt50 found [here](https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py).
This repository provides scripts to run ResNeXt50 on Qualcomm® devices.
More details on model performance across various devices, can be found
[here](https://aihub.qualcomm.com/models/resnext50).
### Model Details
- **Model Type:** Model_use_case.image_classification
- **Model Stats:**
- Model checkpoint: Imagenet
- Input resolution: 224x224
- Number of parameters: 25.0M
- Model size (float): 95.4 MB
- Model size (w8a8): 24.8 MB
| Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model
|---|---|---|---|---|---|---|---|---|
| ResNeXt50 | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 12.002 ms | 0 - 201 MB | NPU | [ResNeXt50.tflite](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50.tflite) |
| ResNeXt50 | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 11.907 ms | 1 - 158 MB | NPU | [ResNeXt50.dlc](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50.dlc) |
| ResNeXt50 | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 3.887 ms | 0 - 246 MB | NPU | [ResNeXt50.tflite](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50.tflite) |
| ResNeXt50 | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 3.896 ms | 1 - 207 MB | NPU | [ResNeXt50.dlc](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50.dlc) |
| ResNeXt50 | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 2.453 ms | 0 - 3 MB | NPU | [ResNeXt50.tflite](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50.tflite) |
| ResNeXt50 | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 2.451 ms | 1 - 2 MB | NPU | [ResNeXt50.dlc](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50.dlc) |
| ResNeXt50 | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | ONNX | 2.505 ms | 0 - 58 MB | NPU | [ResNeXt50.onnx.zip](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50.onnx.zip) |
| ResNeXt50 | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 3.795 ms | 0 - 201 MB | NPU | [ResNeXt50.tflite](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50.tflite) |
| ResNeXt50 | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 15.89 ms | 1 - 159 MB | NPU | [ResNeXt50.dlc](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50.dlc) |
| ResNeXt50 | float | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 12.002 ms | 0 - 201 MB | NPU | [ResNeXt50.tflite](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50.tflite) |
| ResNeXt50 | float | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 11.907 ms | 1 - 158 MB | NPU | [ResNeXt50.dlc](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50.dlc) |
| ResNeXt50 | float | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 4.056 ms | 0 - 188 MB | NPU | [ResNeXt50.tflite](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50.tflite) |
| ResNeXt50 | float | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 4.059 ms | 0 - 148 MB | NPU | [ResNeXt50.dlc](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50.dlc) |
| ResNeXt50 | float | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 3.795 ms | 0 - 201 MB | NPU | [ResNeXt50.tflite](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50.tflite) |
| ResNeXt50 | float | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 15.89 ms | 1 - 159 MB | NPU | [ResNeXt50.dlc](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50.dlc) |
| ResNeXt50 | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 1.778 ms | 0 - 265 MB | NPU | [ResNeXt50.tflite](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50.tflite) |
| ResNeXt50 | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 1.781 ms | 1 - 223 MB | NPU | [ResNeXt50.dlc](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50.dlc) |
| ResNeXt50 | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 1.837 ms | 0 - 196 MB | NPU | [ResNeXt50.onnx.zip](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50.onnx.zip) |
| ResNeXt50 | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | TFLITE | 1.441 ms | 0 - 206 MB | NPU | [ResNeXt50.tflite](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50.tflite) |
| ResNeXt50 | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | QNN_DLC | 1.469 ms | 0 - 159 MB | NPU | [ResNeXt50.dlc](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50.dlc) |
| ResNeXt50 | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | ONNX | 1.562 ms | 0 - 131 MB | NPU | [ResNeXt50.onnx.zip](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50.onnx.zip) |
| ResNeXt50 | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | TFLITE | 1.19 ms | 0 - 202 MB | NPU | [ResNeXt50.tflite](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50.tflite) |
| ResNeXt50 | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | QNN_DLC | 1.203 ms | 1 - 164 MB | NPU | [ResNeXt50.dlc](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50.dlc) |
| ResNeXt50 | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | ONNX | 1.303 ms | 1 - 135 MB | NPU | [ResNeXt50.onnx.zip](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50.onnx.zip) |
| ResNeXt50 | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 2.621 ms | 1 - 1 MB | NPU | [ResNeXt50.dlc](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50.dlc) |
| ResNeXt50 | float | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 2.422 ms | 50 - 50 MB | NPU | [ResNeXt50.onnx.zip](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50.onnx.zip) |
| ResNeXt50 | w8a8 | Dragonwing Q-6690 MTP | Qualcomm® QCM6690 | TFLITE | 7.168 ms | 0 - 172 MB | NPU | [ResNeXt50.tflite](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50_w8a8.tflite) |
| ResNeXt50 | w8a8 | Dragonwing Q-6690 MTP | Qualcomm® QCM6690 | QNN_DLC | 7.635 ms | 0 - 178 MB | NPU | [ResNeXt50.dlc](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50_w8a8.dlc) |
| ResNeXt50 | w8a8 | Dragonwing Q-6690 MTP | Qualcomm® QCM6690 | ONNX | 26.127 ms | 0 - 15 MB | CPU | [ResNeXt50.onnx.zip](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50_w8a8.onnx.zip) |
| ResNeXt50 | w8a8 | Dragonwing RB3 Gen 2 Vision Kit | Qualcomm® QCS6490 | TFLITE | 2.831 ms | 0 - 27 MB | NPU | [ResNeXt50.tflite](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50_w8a8.tflite) |
| ResNeXt50 | w8a8 | Dragonwing RB3 Gen 2 Vision Kit | Qualcomm® QCS6490 | QNN_DLC | 3.102 ms | 0 - 2 MB | NPU | [ResNeXt50.dlc](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50_w8a8.dlc) |
| ResNeXt50 | w8a8 | Dragonwing RB3 Gen 2 Vision Kit | Qualcomm® QCS6490 | ONNX | 47.504 ms | 9 - 25 MB | CPU | [ResNeXt50.onnx.zip](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50_w8a8.onnx.zip) |
| ResNeXt50 | w8a8 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 2.172 ms | 0 - 159 MB | NPU | [ResNeXt50.tflite](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50_w8a8.tflite) |
| ResNeXt50 | w8a8 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 2.47 ms | 0 - 160 MB | NPU | [ResNeXt50.dlc](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50_w8a8.dlc) |
| ResNeXt50 | w8a8 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 1.267 ms | 0 - 188 MB | NPU | [ResNeXt50.tflite](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50_w8a8.tflite) |
| ResNeXt50 | w8a8 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 1.446 ms | 0 - 191 MB | NPU | [ResNeXt50.dlc](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50_w8a8.dlc) |
| ResNeXt50 | w8a8 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 0.896 ms | 0 - 2 MB | NPU | [ResNeXt50.tflite](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50_w8a8.tflite) |
| ResNeXt50 | w8a8 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 1.091 ms | 0 - 2 MB | NPU | [ResNeXt50.dlc](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50_w8a8.dlc) |
| ResNeXt50 | w8a8 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | ONNX | 1.363 ms | 0 - 31 MB | NPU | [ResNeXt50.onnx.zip](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50_w8a8.onnx.zip) |
| ResNeXt50 | w8a8 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 1.284 ms | 0 - 158 MB | NPU | [ResNeXt50.tflite](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50_w8a8.tflite) |
| ResNeXt50 | w8a8 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 1.463 ms | 0 - 160 MB | NPU | [ResNeXt50.dlc](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50_w8a8.dlc) |
| ResNeXt50 | w8a8 | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 2.172 ms | 0 - 159 MB | NPU | [ResNeXt50.tflite](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50_w8a8.tflite) |
| ResNeXt50 | w8a8 | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 2.47 ms | 0 - 160 MB | NPU | [ResNeXt50.dlc](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50_w8a8.dlc) |
| ResNeXt50 | w8a8 | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 1.539 ms | 0 - 166 MB | NPU | [ResNeXt50.tflite](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50_w8a8.tflite) |
| ResNeXt50 | w8a8 | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 1.735 ms | 0 - 167 MB | NPU | [ResNeXt50.dlc](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50_w8a8.dlc) |
| ResNeXt50 | w8a8 | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 1.284 ms | 0 - 158 MB | NPU | [ResNeXt50.tflite](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50_w8a8.tflite) |
| ResNeXt50 | w8a8 | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 1.463 ms | 0 - 160 MB | NPU | [ResNeXt50.dlc](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50_w8a8.dlc) |
| ResNeXt50 | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 0.678 ms | 0 - 192 MB | NPU | [ResNeXt50.tflite](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50_w8a8.tflite) |
| ResNeXt50 | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 0.805 ms | 0 - 190 MB | NPU | [ResNeXt50.dlc](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50_w8a8.dlc) |
| ResNeXt50 | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 0.966 ms | 0 - 170 MB | NPU | [ResNeXt50.onnx.zip](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50_w8a8.onnx.zip) |
| ResNeXt50 | w8a8 | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | TFLITE | 0.559 ms | 0 - 160 MB | NPU | [ResNeXt50.tflite](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50_w8a8.tflite) |
| ResNeXt50 | w8a8 | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | QNN_DLC | 0.628 ms | 0 - 163 MB | NPU | [ResNeXt50.dlc](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50_w8a8.dlc) |
| ResNeXt50 | w8a8 | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | ONNX | 0.829 ms | 0 - 138 MB | NPU | [ResNeXt50.onnx.zip](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50_w8a8.onnx.zip) |
| ResNeXt50 | w8a8 | Snapdragon 7 Gen 4 QRD | Snapdragon® 7 Gen 4 Mobile | TFLITE | 1.229 ms | 0 - 163 MB | NPU | [ResNeXt50.tflite](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50_w8a8.tflite) |
| ResNeXt50 | w8a8 | Snapdragon 7 Gen 4 QRD | Snapdragon® 7 Gen 4 Mobile | QNN_DLC | 1.363 ms | 0 - 169 MB | NPU | [ResNeXt50.dlc](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50_w8a8.dlc) |
| ResNeXt50 | w8a8 | Snapdragon 7 Gen 4 QRD | Snapdragon® 7 Gen 4 Mobile | ONNX | 25.266 ms | 0 - 17 MB | CPU | [ResNeXt50.onnx.zip](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50_w8a8.onnx.zip) |
| ResNeXt50 | w8a8 | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | TFLITE | 0.486 ms | 0 - 161 MB | NPU | [ResNeXt50.tflite](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50_w8a8.tflite) |
| ResNeXt50 | w8a8 | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | QNN_DLC | 0.526 ms | 0 - 164 MB | NPU | [ResNeXt50.dlc](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50_w8a8.dlc) |
| ResNeXt50 | w8a8 | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | ONNX | 0.762 ms | 0 - 141 MB | NPU | [ResNeXt50.onnx.zip](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50_w8a8.onnx.zip) |
| ResNeXt50 | w8a8 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 1.217 ms | 0 - 0 MB | NPU | [ResNeXt50.dlc](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50_w8a8.dlc) |
| ResNeXt50 | w8a8 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 1.248 ms | 26 - 26 MB | NPU | [ResNeXt50.onnx.zip](https://huggingface.co/qualcomm/ResNeXt50/blob/main/ResNeXt50_w8a8.onnx.zip) |
## Installation
Install the package via pip:
```bash
pip install qai-hub-models
```
## Configure Qualcomm® AI Hub Workbench to run this model on a cloud-hosted device
Sign-in to [Qualcomm® AI Hub Workbench](https://workbench.aihub.qualcomm.com/) 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.
```bash
qai-hub configure --api_token API_TOKEN
```
Navigate to [docs](https://workbench.aihub.qualcomm.com/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.
```bash
python -m qai_hub_models.models.resnext50.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.resnext50.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.
```bash
python -m qai_hub_models.models.resnext50.export
```
## How does this work?
This [export script](https://aihub.qualcomm.com/models/resnext50/qai_hub_models/models/ResNeXt50/export.py)
leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model
on-device. Lets go through each step below in detail:
Step 1: **Compile model for on-device deployment**
To compile a PyTorch model for on-device deployment, we first trace the model
in memory using the `jit.trace` and then call the `submit_compile_job` API.
```python
import torch
import qai_hub as hub
from qai_hub_models.models.resnext50 import Model
# Load the model
torch_model = Model.from_pretrained()
# Device
device = hub.Device("Samsung Galaxy S25")
# Trace model
input_shape = torch_model.get_input_spec()
sample_inputs = torch_model.sample_inputs()
pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()])
# Compile model on a specific device
compile_job = hub.submit_compile_job(
model=pt_model,
device=device,
input_specs=torch_model.get_input_spec(),
)
# Get target model to run on-device
target_model = compile_job.get_target_model()
```
Step 2: **Performance profiling on cloud-hosted device**
After compiling models from step 1. Models can be profiled model on-device using the
`target_model`. Note that this scripts runs the model on a device automatically
provisioned in the cloud. Once the job is submitted, you can navigate to a
provided job URL to view a variety of on-device performance metrics.
```python
profile_job = hub.submit_profile_job(
model=target_model,
device=device,
)
```
Step 3: **Verify on-device accuracy**
To verify the accuracy of the model on-device, you can run on-device inference
on sample input data on the same cloud hosted device.
```python
input_data = torch_model.sample_inputs()
inference_job = hub.submit_inference_job(
model=target_model,
device=device,
inputs=input_data,
)
on_device_output = inference_job.download_output_data()
```
With the output of the model, you can compute like PSNR, relative errors or
spot check the output with expected output.
**Note**: This on-device profiling and inference requires access to Qualcomm®
AI Hub Workbench. [Sign up for access](https://myaccount.qualcomm.com/signup).
## Run demo on a cloud-hosted device
You can also run the demo on-device.
```bash
python -m qai_hub_models.models.resnext50.demo --eval-mode 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.resnext50.demo -- --eval-mode on-device
```
## Deploying compiled model to Android
The models can be deployed using multiple runtimes:
- TensorFlow Lite (`.tflite` export): [This
tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a
guide to deploy the .tflite model in an Android application.
- QNN (`.so` export ): This [sample
app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html)
provides instructions on how to use the `.so` shared library in an Android application.
## View on Qualcomm® AI Hub
Get more details on ResNeXt50's performance across various devices [here](https://aihub.qualcomm.com/models/resnext50).
Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
## License
* The license for the original implementation of ResNeXt50 can be found
[here](https://github.com/pytorch/vision/blob/main/LICENSE).
## References
* [Aggregated Residual Transformations for Deep Neural Networks](https://arxiv.org/abs/1611.05431)
* [Source Model Implementation](https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py)
## Community
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).