---
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/resnet18/web-assets/model_demo.png)

# ResNet18: Optimized for Qualcomm Devices

ResNet18 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 is based on the implementation of ResNet18 found [here](https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py).
This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/main/src/qai_hub_models/models/resnet18) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).

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.

## Getting Started
There are two ways to deploy this model on your device:

### Option 1: Download Pre-Exported Models

Below are pre-exported model assets ready for deployment.

| Runtime | Precision | Chipset | SDK Versions | Download |
|---|---|---|---|---|
| ONNX | float | Universal | QAIRT 2.42, ONNX Runtime 1.24.3 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet18/releases/v0.50.2/resnet18-onnx-float.zip)
| ONNX | w8a8 | Universal | QAIRT 2.42, ONNX Runtime 1.24.3 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet18/releases/v0.50.2/resnet18-onnx-w8a8.zip)
| QNN_DLC | float | Universal | QAIRT 2.43 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet18/releases/v0.50.2/resnet18-qnn_dlc-float.zip)
| QNN_DLC | w8a8 | Universal | QAIRT 2.43 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet18/releases/v0.50.2/resnet18-qnn_dlc-w8a8.zip)
| TFLITE | float | Universal | QAIRT 2.43, TFLite 2.19.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet18/releases/v0.50.2/resnet18-tflite-float.zip)
| TFLITE | w8a8 | Universal | QAIRT 2.43, TFLite 2.19.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet18/releases/v0.50.2/resnet18-tflite-w8a8.zip)

For more device-specific assets and performance metrics, visit **[ResNet18 on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/resnet18)**.


### Option 2: Export with Custom Configurations

Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/main/src/qai_hub_models/models/resnet18) Python library to compile and export the model with your own:
- Custom weights (e.g., fine-tuned checkpoints)
- Custom input shapes
- Target device and runtime configurations

This option is ideal if you need to customize the model beyond the default configuration provided here.

See our repository for [ResNet18 on GitHub](https://github.com/qualcomm/ai-hub-models/blob/main/src/qai_hub_models/models/resnet18) for usage instructions.

## Model Details

**Model Type:** Model_use_case.image_classification

**Model Stats:**
- Model checkpoint: Imagenet
- Input resolution: 224x224
- Number of parameters: 11.7M
- Model size (float): 44.6 MB
- Model size (w8a8): 11.3 MB

## Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
|---|---|---|---|---|---|---
| ResNet18 | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.557 ms | 0 - 26 MB | NPU
| ResNet18 | ONNX | float | Snapdragon® X2 Elite | 0.533 ms | 23 - 23 MB | NPU
| ResNet18 | ONNX | float | Snapdragon® X Elite | 1.175 ms | 22 - 22 MB | NPU
| ResNet18 | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 0.773 ms | 0 - 34 MB | NPU
| ResNet18 | ONNX | float | Qualcomm® QCS8550 (Proxy) | 1.068 ms | 0 - 25 MB | NPU
| ResNet18 | ONNX | float | Qualcomm® QCS9075 | 1.796 ms | 1 - 3 MB | NPU
| ResNet18 | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.626 ms | 0 - 20 MB | NPU
| ResNet18 | ONNX | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.339 ms | 0 - 26 MB | NPU
| ResNet18 | ONNX | w8a8 | Snapdragon® X2 Elite | 0.309 ms | 11 - 11 MB | NPU
| ResNet18 | ONNX | w8a8 | Snapdragon® X Elite | 0.642 ms | 11 - 11 MB | NPU
| ResNet18 | ONNX | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.403 ms | 0 - 49 MB | NPU
| ResNet18 | ONNX | w8a8 | Qualcomm® QCS6490 | 13.652 ms | 7 - 22 MB | CPU
| ResNet18 | ONNX | w8a8 | Qualcomm® QCS8550 (Proxy) | 0.558 ms | 0 - 2 MB | NPU
| ResNet18 | ONNX | w8a8 | Qualcomm® QCS9075 | 0.64 ms | 0 - 3 MB | NPU
| ResNet18 | ONNX | w8a8 | Qualcomm® QCM6690 | 11.409 ms | 8 - 15 MB | CPU
| ResNet18 | ONNX | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 0.357 ms | 0 - 22 MB | NPU
| ResNet18 | ONNX | w8a8 | Snapdragon® 7 Gen 4 Mobile | 8.675 ms | 6 - 13 MB | CPU
| ResNet18 | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.587 ms | 1 - 28 MB | NPU
| ResNet18 | QNN_DLC | float | Snapdragon® X2 Elite | 0.693 ms | 1 - 1 MB | NPU
| ResNet18 | QNN_DLC | float | Snapdragon® X Elite | 1.454 ms | 1 - 1 MB | NPU
| ResNet18 | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 0.906 ms | 0 - 38 MB | NPU
| ResNet18 | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 5.801 ms | 1 - 24 MB | NPU
| ResNet18 | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 1.327 ms | 1 - 103 MB | NPU
| ResNet18 | QNN_DLC | float | Qualcomm® SA8775P | 1.95 ms | 0 - 26 MB | NPU
| ResNet18 | QNN_DLC | float | Qualcomm® QCS9075 | 2.066 ms | 3 - 5 MB | NPU
| ResNet18 | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 2.445 ms | 0 - 34 MB | NPU
| ResNet18 | QNN_DLC | float | Qualcomm® SA7255P | 5.801 ms | 1 - 24 MB | NPU
| ResNet18 | QNN_DLC | float | Qualcomm® SA8295P | 2.305 ms | 0 - 19 MB | NPU
| ResNet18 | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.701 ms | 0 - 23 MB | NPU
| ResNet18 | QNN_DLC | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.265 ms | 0 - 25 MB | NPU
| ResNet18 | QNN_DLC | w8a8 | Snapdragon® X2 Elite | 0.311 ms | 0 - 0 MB | NPU
| ResNet18 | QNN_DLC | w8a8 | Snapdragon® X Elite | 0.623 ms | 0 - 0 MB | NPU
| ResNet18 | QNN_DLC | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.391 ms | 0 - 48 MB | NPU
| ResNet18 | QNN_DLC | w8a8 | Qualcomm® QCS6490 | 1.817 ms | 0 - 2 MB | NPU
| ResNet18 | QNN_DLC | w8a8 | Qualcomm® QCS8275 (Proxy) | 1.274 ms | 0 - 23 MB | NPU
| ResNet18 | QNN_DLC | w8a8 | Qualcomm® QCS8550 (Proxy) | 0.512 ms | 0 - 1 MB | NPU
| ResNet18 | QNN_DLC | w8a8 | Qualcomm® SA8775P | 0.7 ms | 0 - 25 MB | NPU
| ResNet18 | QNN_DLC | w8a8 | Qualcomm® QCS9075 | 0.626 ms | 0 - 2 MB | NPU
| ResNet18 | QNN_DLC | w8a8 | Qualcomm® QCM6690 | 3.227 ms | 0 - 31 MB | NPU
| ResNet18 | QNN_DLC | w8a8 | Qualcomm® QCS8450 (Proxy) | 0.741 ms | 0 - 48 MB | NPU
| ResNet18 | QNN_DLC | w8a8 | Qualcomm® SA7255P | 1.274 ms | 0 - 23 MB | NPU
| ResNet18 | QNN_DLC | w8a8 | Qualcomm® SA8295P | 0.897 ms | 0 - 21 MB | NPU
| ResNet18 | QNN_DLC | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 0.294 ms | 0 - 22 MB | NPU
| ResNet18 | QNN_DLC | w8a8 | Snapdragon® 7 Gen 4 Mobile | 0.685 ms | 0 - 31 MB | NPU
| ResNet18 | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.585 ms | 0 - 30 MB | NPU
| ResNet18 | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 0.91 ms | 0 - 61 MB | NPU
| ResNet18 | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 5.751 ms | 0 - 28 MB | NPU
| ResNet18 | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 1.338 ms | 0 - 8 MB | NPU
| ResNet18 | TFLITE | float | Qualcomm® SA8775P | 1.947 ms | 0 - 30 MB | NPU
| ResNet18 | TFLITE | float | Qualcomm® QCS9075 | 2.035 ms | 0 - 25 MB | NPU
| ResNet18 | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 2.47 ms | 0 - 61 MB | NPU
| ResNet18 | TFLITE | float | Qualcomm® SA7255P | 5.751 ms | 0 - 28 MB | NPU
| ResNet18 | TFLITE | float | Qualcomm® SA8295P | 2.276 ms | 0 - 22 MB | NPU
| ResNet18 | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.703 ms | 0 - 26 MB | NPU
| ResNet18 | TFLITE | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.218 ms | 0 - 26 MB | NPU
| ResNet18 | TFLITE | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.299 ms | 0 - 47 MB | NPU
| ResNet18 | TFLITE | w8a8 | Qualcomm® QCS6490 | 1.528 ms | 0 - 13 MB | NPU
| ResNet18 | TFLITE | w8a8 | Qualcomm® QCS8275 (Proxy) | 1.012 ms | 0 - 23 MB | NPU
| ResNet18 | TFLITE | w8a8 | Qualcomm® QCS8550 (Proxy) | 0.385 ms | 0 - 14 MB | NPU
| ResNet18 | TFLITE | w8a8 | Qualcomm® SA8775P | 0.572 ms | 0 - 25 MB | NPU
| ResNet18 | TFLITE | w8a8 | Qualcomm® QCS9075 | 0.48 ms | 0 - 13 MB | NPU
| ResNet18 | TFLITE | w8a8 | Qualcomm® QCM6690 | 2.794 ms | 0 - 31 MB | NPU
| ResNet18 | TFLITE | w8a8 | Qualcomm® QCS8450 (Proxy) | 0.611 ms | 0 - 49 MB | NPU
| ResNet18 | TFLITE | w8a8 | Qualcomm® SA7255P | 1.012 ms | 0 - 23 MB | NPU
| ResNet18 | TFLITE | w8a8 | Qualcomm® SA8295P | 0.742 ms | 0 - 21 MB | NPU
| ResNet18 | TFLITE | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 0.237 ms | 0 - 26 MB | NPU
| ResNet18 | TFLITE | w8a8 | Snapdragon® 7 Gen 4 Mobile | 0.55 ms | 0 - 31 MB | NPU

## License
* The license for the original implementation of ResNet18 can be found
  [here](https://github.com/pytorch/vision/blob/main/LICENSE).

## References
* [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)
* [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).