README.md

---
library_name: pytorch
license: other
tags:
- backbone
- android
pipeline_tag: video-classification

---

![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet_2plus1d/web-assets/model_demo.png)

# ResNet-2Plus1D: Optimized for Qualcomm Devices

ResNet (2+1)D Convolutions is a network which explicitly factorizes 3D convolution into two separate and successive operations, a 2D spatial convolution and a 1D temporal convolution. It used for video understanding applications.

This is based on the implementation of ResNet-2Plus1D found [here](https://github.com/pytorch/vision/blob/main/torchvision/models/video/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/resnet_2plus1d) 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/resnet_2plus1d/releases/v0.49.1/resnet_2plus1d-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/resnet_2plus1d/releases/v0.49.1/resnet_2plus1d-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/resnet_2plus1d/releases/v0.49.1/resnet_2plus1d-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/resnet_2plus1d/releases/v0.49.1/resnet_2plus1d-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/resnet_2plus1d/releases/v0.49.1/resnet_2plus1d-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/resnet_2plus1d/releases/v0.49.1/resnet_2plus1d-tflite-w8a8.zip)

For more device-specific assets and performance metrics, visit **[ResNet-2Plus1D on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/resnet_2plus1d)**.


### 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/resnet_2plus1d) 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 [ResNet-2Plus1D on GitHub](https://github.com/qualcomm/ai-hub-models/blob/main/qai_hub_models/models/resnet_2plus1d) for usage instructions.

## Model Details

**Model Type:** Model_use_case.video_classification

**Model Stats:**
- Model checkpoint: Kinetics-400
- Input resolution: 112x112
- Number of parameters: 31.5M
- Model size (float): 120 MB
- Model size (w8a8): 30.8 MB

## Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
|---|---|---|---|---|---|---
| ResNet-2Plus1D | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 5.521 ms | 2 - 214 MB | NPU
| ResNet-2Plus1D | ONNX | float | Snapdragon® X2 Elite | 6.151 ms | 60 - 60 MB | NPU
| ResNet-2Plus1D | ONNX | float | Snapdragon® X Elite | 12.209 ms | 60 - 60 MB | NPU
| ResNet-2Plus1D | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 9.082 ms | 2 - 299 MB | NPU
| ResNet-2Plus1D | ONNX | float | Qualcomm® QCS8550 (Proxy) | 11.951 ms | 0 - 64 MB | NPU
| ResNet-2Plus1D | ONNX | float | Qualcomm® QCS9075 | 21.812 ms | 2 - 7 MB | NPU
| ResNet-2Plus1D | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 7.227 ms | 0 - 210 MB | NPU
| ResNet-2Plus1D | ONNX | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 1.928 ms | 0 - 191 MB | NPU
| ResNet-2Plus1D | ONNX | w8a8 | Snapdragon® X2 Elite | 2.041 ms | 31 - 31 MB | NPU
| ResNet-2Plus1D | ONNX | w8a8 | Snapdragon® X Elite | 4.529 ms | 31 - 31 MB | NPU
| ResNet-2Plus1D | ONNX | w8a8 | Snapdragon® 8 Gen 3 Mobile | 3.27 ms | 0 - 224 MB | NPU
| ResNet-2Plus1D | ONNX | w8a8 | Qualcomm® QCS6490 | 318.179 ms | 97 - 127 MB | CPU
| ResNet-2Plus1D | ONNX | w8a8 | Qualcomm® QCS8550 (Proxy) | 4.27 ms | 0 - 35 MB | NPU
| ResNet-2Plus1D | ONNX | w8a8 | Qualcomm® QCS9075 | 4.106 ms | 1 - 3 MB | NPU
| ResNet-2Plus1D | ONNX | w8a8 | Qualcomm® QCM6690 | 303.027 ms | 102 - 109 MB | CPU
| ResNet-2Plus1D | ONNX | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 2.625 ms | 0 - 190 MB | NPU
| ResNet-2Plus1D | ONNX | w8a8 | Snapdragon® 7 Gen 4 Mobile | 268.525 ms | 73 - 81 MB | CPU
| ResNet-2Plus1D | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 5.714 ms | 2 - 230 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Snapdragon® X2 Elite | 6.57 ms | 2 - 2 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Snapdragon® X Elite | 13.05 ms | 2 - 2 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 9.246 ms | 0 - 303 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 81.791 ms | 0 - 215 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 12.634 ms | 2 - 4 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Qualcomm® SA8775P | 21.317 ms | 1 - 215 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Qualcomm® QCS9075 | 23.085 ms | 2 - 6 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 28.673 ms | 0 - 278 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Qualcomm® SA7255P | 81.791 ms | 0 - 215 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Qualcomm® SA8295P | 22.729 ms | 0 - 198 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 7.166 ms | 0 - 219 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 1.879 ms | 1 - 183 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Snapdragon® X2 Elite | 2.334 ms | 1 - 1 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Snapdragon® X Elite | 4.879 ms | 1 - 1 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Snapdragon® 8 Gen 3 Mobile | 3.345 ms | 1 - 221 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Qualcomm® QCS6490 | 19.654 ms | 1 - 3 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Qualcomm® QCS8275 (Proxy) | 13.291 ms | 1 - 181 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Qualcomm® QCS8550 (Proxy) | 4.569 ms | 1 - 3 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Qualcomm® SA8775P | 4.691 ms | 1 - 184 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Qualcomm® QCS9075 | 4.8 ms | 3 - 5 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Qualcomm® QCM6690 | 80.276 ms | 1 - 197 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Qualcomm® QCS8450 (Proxy) | 7.728 ms | 1 - 218 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Qualcomm® SA7255P | 13.291 ms | 1 - 181 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Qualcomm® SA8295P | 7.76 ms | 1 - 181 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 2.573 ms | 0 - 179 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Snapdragon® 7 Gen 4 Mobile | 7.809 ms | 1 - 188 MB | NPU
| ResNet-2Plus1D | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 261.931 ms | 0 - 244 MB | NPU
| ResNet-2Plus1D | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 289.227 ms | 0 - 322 MB | NPU
| ResNet-2Plus1D | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 716.995 ms | 0 - 234 MB | NPU
| ResNet-2Plus1D | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 399.605 ms | 0 - 2 MB | NPU
| ResNet-2Plus1D | TFLITE | float | Qualcomm® SA8775P | 384.628 ms | 0 - 233 MB | NPU
| ResNet-2Plus1D | TFLITE | float | Qualcomm® QCS9075 | 384.963 ms | 0 - 66 MB | NPU
| ResNet-2Plus1D | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 444.283 ms | 0 - 311 MB | NPU
| ResNet-2Plus1D | TFLITE | float | Qualcomm® SA7255P | 716.995 ms | 0 - 234 MB | NPU
| ResNet-2Plus1D | TFLITE | float | Qualcomm® SA8295P | 460.281 ms | 0 - 228 MB | NPU
| ResNet-2Plus1D | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 262.69 ms | 0 - 240 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 607.039 ms | 0 - 472 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Snapdragon® 8 Gen 3 Mobile | 573.301 ms | 0 - 539 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Qualcomm® QCS8275 (Proxy) | 1515.475 ms | 0 - 442 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Qualcomm® QCS8550 (Proxy) | 799.577 ms | 0 - 3 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Qualcomm® SA8775P | 3767.32 ms | 1 - 442 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Qualcomm® QCM6690 | 1600.892 ms | 311 - 479 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Qualcomm® QCS8450 (Proxy) | 860.955 ms | 0 - 428 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Qualcomm® SA7255P | 1515.475 ms | 0 - 442 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Qualcomm® SA8295P | 874.889 ms | 0 - 432 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 498.53 ms | 0 - 523 MB | NPU

## License
* The license for the original implementation of ResNet-2Plus1D can be found
  [here](https://github.com/pytorch/vision/blob/main/LICENSE).

## References
* [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://arxiv.org/abs/1711.11248)
* [Source Model Implementation](https://github.com/pytorch/vision/blob/main/torchvision/models/video/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).