---
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/resnet50/web-assets/model_demo.png)

# ResNet50: Optimized for Qualcomm Devices

ResNet50 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 ResNet50 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/qai_hub_models/models/resnet50) 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 1.24.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet50/releases/v0.48.0/resnet50-onnx-float.zip)
| ONNX | w8a8 | Universal | QAIRT 2.42, ONNX Runtime 1.24.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet50/releases/v0.48.0/resnet50-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/resnet50/releases/v0.48.0/resnet50-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/resnet50/releases/v0.48.0/resnet50-qnn_dlc-w8a8.zip)
| TFLITE | float | Universal | QAIRT 2.43, TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet50/releases/v0.48.0/resnet50-tflite-float.zip)
| TFLITE | w8a8 | Universal | QAIRT 2.43, TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet50/releases/v0.48.0/resnet50-tflite-w8a8.zip)

For more device-specific assets and performance metrics, visit **[ResNet50 on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/resnet50)**.


### Option 2: Export with Custom Configurations

Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/main/qai_hub_models/models/resnet50) 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 [ResNet50 on GitHub](https://github.com/qualcomm/ai-hub-models/blob/main/qai_hub_models/models/resnet50) for usage instructions.

## Model Details

**Model Type:** Model_use_case.image_classification

**Model Stats:**
- Model checkpoint: Imagenet
- Input resolution: 224x224
- Number of parameters: 25.5M
- Model size (float): 97.4 MB
- Model size (w8a8): 25.1 MB

## Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
|---|---|---|---|---|---|---
| ResNet50 | ONNX | float | Snapdragon® X2 Elite | 0.998 ms | 49 - 49 MB | NPU
| ResNet50 | ONNX | float | Snapdragon® X Elite | 2.088 ms | 49 - 49 MB | NPU
| ResNet50 | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 1.477 ms | 0 - 86 MB | NPU
| ResNet50 | ONNX | float | Qualcomm® QCS8550 (Proxy) | 1.991 ms | 0 - 4 MB | NPU
| ResNet50 | ONNX | float | Qualcomm® QCS9075 | 3.172 ms | 1 - 4 MB | NPU
| ResNet50 | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 1.246 ms | 0 - 44 MB | NPU
| ResNet50 | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 1.059 ms | 1 - 75 MB | NPU
| ResNet50 | ONNX | w8a8 | Snapdragon® X2 Elite | 0.417 ms | 25 - 25 MB | NPU
| ResNet50 | ONNX | w8a8 | Snapdragon® X Elite | 0.97 ms | 25 - 25 MB | NPU
| ResNet50 | ONNX | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.676 ms | 0 - 79 MB | NPU
| ResNet50 | ONNX | w8a8 | Qualcomm® QCS6490 | 31.854 ms | 9 - 28 MB | CPU
| ResNet50 | ONNX | w8a8 | Qualcomm® QCS8550 (Proxy) | 0.897 ms | 0 - 31 MB | NPU
| ResNet50 | ONNX | w8a8 | Qualcomm® QCS9075 | 0.968 ms | 0 - 3 MB | NPU
| ResNet50 | ONNX | w8a8 | Qualcomm® QCM6690 | 23.355 ms | 7 - 15 MB | CPU
| ResNet50 | ONNX | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 0.578 ms | 0 - 42 MB | NPU
| ResNet50 | ONNX | w8a8 | Snapdragon® 7 Gen 4 Mobile | 17.932 ms | 6 - 15 MB | CPU
| ResNet50 | ONNX | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.554 ms | 0 - 44 MB | NPU
| ResNet50 | QNN_DLC | float | Snapdragon® X2 Elite | 1.254 ms | 1 - 1 MB | NPU
| ResNet50 | QNN_DLC | float | Snapdragon® X Elite | 2.317 ms | 1 - 1 MB | NPU
| ResNet50 | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 1.607 ms | 0 - 77 MB | NPU
| ResNet50 | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 10.626 ms | 1 - 46 MB | NPU
| ResNet50 | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 2.192 ms | 1 - 2 MB | NPU
| ResNet50 | QNN_DLC | float | Qualcomm® SA8775P | 3.361 ms | 1 - 47 MB | NPU
| ResNet50 | QNN_DLC | float | Qualcomm® QCS9075 | 3.35 ms | 3 - 5 MB | NPU
| ResNet50 | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 3.761 ms | 0 - 62 MB | NPU
| ResNet50 | QNN_DLC | float | Qualcomm® SA7255P | 10.626 ms | 1 - 46 MB | NPU
| ResNet50 | QNN_DLC | float | Qualcomm® SA8295P | 3.668 ms | 1 - 30 MB | NPU
| ResNet50 | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 1.262 ms | 1 - 48 MB | NPU
| ResNet50 | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 1.084 ms | 1 - 51 MB | NPU
| ResNet50 | QNN_DLC | w8a8 | Snapdragon® X2 Elite | 0.467 ms | 0 - 0 MB | NPU
| ResNet50 | QNN_DLC | w8a8 | Snapdragon® X Elite | 0.962 ms | 0 - 0 MB | NPU
| ResNet50 | QNN_DLC | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.683 ms | 0 - 70 MB | NPU
| ResNet50 | QNN_DLC | w8a8 | Qualcomm® QCS6490 | 3.008 ms | 0 - 2 MB | NPU
| ResNet50 | QNN_DLC | w8a8 | Qualcomm® QCS8275 (Proxy) | 1.976 ms | 0 - 40 MB | NPU
| ResNet50 | QNN_DLC | w8a8 | Qualcomm® QCS8550 (Proxy) | 0.895 ms | 0 - 2 MB | NPU
| ResNet50 | QNN_DLC | w8a8 | Qualcomm® SA8775P | 1.096 ms | 0 - 44 MB | NPU
| ResNet50 | QNN_DLC | w8a8 | Qualcomm® QCS9075 | 0.972 ms | 0 - 2 MB | NPU
| ResNet50 | QNN_DLC | w8a8 | Qualcomm® QCM6690 | 6.477 ms | 0 - 161 MB | NPU
| ResNet50 | QNN_DLC | w8a8 | Qualcomm® QCS8450 (Proxy) | 1.194 ms | 0 - 72 MB | NPU
| ResNet50 | QNN_DLC | w8a8 | Qualcomm® SA7255P | 1.976 ms | 0 - 40 MB | NPU
| ResNet50 | QNN_DLC | w8a8 | Qualcomm® SA8295P | 1.436 ms | 0 - 38 MB | NPU
| ResNet50 | QNN_DLC | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 0.531 ms | 0 - 39 MB | NPU
| ResNet50 | QNN_DLC | w8a8 | Snapdragon® 7 Gen 4 Mobile | 1.238 ms | 0 - 48 MB | NPU
| ResNet50 | QNN_DLC | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.481 ms | 0 - 42 MB | NPU
| ResNet50 | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 1.584 ms | 0 - 120 MB | NPU
| ResNet50 | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 10.491 ms | 0 - 80 MB | NPU
| ResNet50 | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 2.2 ms | 0 - 2 MB | NPU
| ResNet50 | TFLITE | float | Qualcomm® SA8775P | 3.39 ms | 0 - 81 MB | NPU
| ResNet50 | TFLITE | float | Qualcomm® QCS9075 | 3.35 ms | 0 - 52 MB | NPU
| ResNet50 | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 3.723 ms | 0 - 105 MB | NPU
| ResNet50 | TFLITE | float | Qualcomm® SA7255P | 10.491 ms | 0 - 80 MB | NPU
| ResNet50 | TFLITE | float | Qualcomm® SA8295P | 3.614 ms | 0 - 61 MB | NPU
| ResNet50 | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 1.26 ms | 0 - 85 MB | NPU
| ResNet50 | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 1.084 ms | 0 - 83 MB | NPU
| ResNet50 | TFLITE | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.586 ms | 0 - 78 MB | NPU
| ResNet50 | TFLITE | w8a8 | Qualcomm® QCS6490 | 2.581 ms | 0 - 27 MB | NPU
| ResNet50 | TFLITE | w8a8 | Qualcomm® QCS8275 (Proxy) | 1.746 ms | 0 - 40 MB | NPU
| ResNet50 | TFLITE | w8a8 | Qualcomm® QCS8550 (Proxy) | 0.759 ms | 0 - 145 MB | NPU
| ResNet50 | TFLITE | w8a8 | Qualcomm® SA8775P | 0.977 ms | 0 - 43 MB | NPU
| ResNet50 | TFLITE | w8a8 | Qualcomm® QCS9075 | 0.826 ms | 0 - 27 MB | NPU
| ResNet50 | TFLITE | w8a8 | Qualcomm® QCM6690 | 6.208 ms | 0 - 159 MB | NPU
| ResNet50 | TFLITE | w8a8 | Qualcomm® QCS8450 (Proxy) | 1.052 ms | 0 - 73 MB | NPU
| ResNet50 | TFLITE | w8a8 | Qualcomm® SA7255P | 1.746 ms | 0 - 40 MB | NPU
| ResNet50 | TFLITE | w8a8 | Qualcomm® SA8295P | 1.28 ms | 0 - 37 MB | NPU
| ResNet50 | TFLITE | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 0.481 ms | 0 - 38 MB | NPU
| ResNet50 | TFLITE | w8a8 | Snapdragon® 7 Gen 4 Mobile | 1.086 ms | 0 - 46 MB | NPU
| ResNet50 | TFLITE | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.446 ms | 0 - 42 MB | NPU

## License
* The license for the original implementation of ResNet50 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).