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/v0.59.0/src/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 2.45, ONNX Runtime 1.27.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet_2plus1d/releases/v0.59.0/resnet_2plus1d-onnx-float.zip)
| ONNX | w8a8 | Universal | QAIRT 2.45, ONNX Runtime 1.27.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet_2plus1d/releases/v0.59.0/resnet_2plus1d-onnx-w8a8.zip)
| QNN_DLC | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet_2plus1d/releases/v0.59.0/resnet_2plus1d-qnn_dlc-float.zip)
| QNN_DLC | w8a8 | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet_2plus1d/releases/v0.59.0/resnet_2plus1d-qnn_dlc-w8a8.zip)
| TFLITE | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet_2plus1d/releases/v0.59.0/resnet_2plus1d-tflite-float.zip)
| TFLITE | w8a8 | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet_2plus1d/releases/v0.59.0/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/v0.59.0/src/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/v0.59.0/src/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® X2 Elite | 33.888 ms | 12 - 12 MB | NPU
| ResNet-2Plus1D | ONNX | float | Snapdragon® X Elite | 66.932 ms | 57 - 57 MB | NPU
| ResNet-2Plus1D | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 52.628 ms | 4 - 766 MB | NPU
| ResNet-2Plus1D | ONNX | float | Snapdragon® 8 Gen 1 Mobile | 141.817 ms | 1 - 586 MB | NPU
| ResNet-2Plus1D | ONNX | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 69.204 ms | 0 - 71 MB | NPU
| ResNet-2Plus1D | ONNX | float | Qualcomm® QCS8450 | 141.817 ms | 1 - 586 MB | NPU
| ResNet-2Plus1D | ONNX | float | Qualcomm® Dragonwing™ IQ-9075 | 111.28 ms | 11 - 26 MB | NPU
| ResNet-2Plus1D | ONNX | float | Qualcomm® Dragonwing™ IQ-X7181 | 66.932 ms | 57 - 57 MB | NPU
| ResNet-2Plus1D | ONNX | float | Qualcomm® Dragonwing™ Q-8750 | 42.336 ms | 0 - 549 MB | NPU
| ResNet-2Plus1D | ONNX | float | Snapdragon® 8 Elite Mobile | 42.336 ms | 0 - 549 MB | NPU
| ResNet-2Plus1D | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 32.148 ms | 3 - 570 MB | NPU
| ResNet-2Plus1D | ONNX | w8a8 | Snapdragon® X2 Elite | 11.93 ms | 3 - 3 MB | NPU
| ResNet-2Plus1D | ONNX | w8a8 | Snapdragon® X Elite | 25.02 ms | 32 - 32 MB | NPU
| ResNet-2Plus1D | ONNX | w8a8 | Snapdragon® 8 Gen 3 Mobile | 19.526 ms | 0 - 468 MB | NPU
| ResNet-2Plus1D | ONNX | w8a8 | Snapdragon® 8 Gen 1 Mobile | 44.772 ms | 0 - 460 MB | NPU
| ResNet-2Plus1D | ONNX | w8a8 | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 26.111 ms | 0 - 471 MB | NPU
| ResNet-2Plus1D | ONNX | w8a8 | Qualcomm® QCS8450 | 44.772 ms | 0 - 460 MB | NPU
| ResNet-2Plus1D | ONNX | w8a8 | Qualcomm® Dragonwing™ IQ-9075 | 23.847 ms | 0 - 6 MB | NPU
| ResNet-2Plus1D | ONNX | w8a8 | Qualcomm® Dragonwing™ IQ-X7181 | 25.02 ms | 32 - 32 MB | NPU
| ResNet-2Plus1D | ONNX | w8a8 | Qualcomm® Dragonwing™ Q-8750 | 15.767 ms | 0 - 373 MB | NPU
| ResNet-2Plus1D | ONNX | w8a8 | Snapdragon® 8 Elite Mobile | 15.767 ms | 0 - 373 MB | NPU
| ResNet-2Plus1D | ONNX | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 12.029 ms | 0 - 386 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Snapdragon® X2 Elite | 34.377 ms | 12 - 12 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Snapdragon® X Elite | 66.745 ms | 12 - 12 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 51.95 ms | 11 - 717 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Snapdragon® 8 Gen 1 Mobile | 144.962 ms | 3 - 595 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8275 | 424.692 ms | 1 - 492 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 68.213 ms | 12 - 857 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Qualcomm® SA8775P | 110.909 ms | 1 - 490 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Qualcomm® SA8650P | 110.909 ms | 1 - 490 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Qualcomm® SA8255P | 110.909 ms | 1 - 490 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Qualcomm® QCS8450 | 144.962 ms | 3 - 595 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 112.001 ms | 12 - 25 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 66.745 ms | 12 - 12 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 42.196 ms | 0 - 489 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Qualcomm® SA7255P | 424.692 ms | 1 - 492 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Qualcomm® SA8295P | 118.429 ms | 0 - 398 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 42.196 ms | 0 - 489 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 29.863 ms | 12 - 509 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Snapdragon® X2 Elite | 13.564 ms | 3 - 3 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Snapdragon® X Elite | 26.962 ms | 3 - 3 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Snapdragon® 8 Gen 3 Mobile | 21.45 ms | 3 - 465 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Snapdragon® 8 Gen 1 Mobile | 47.823 ms | 3 - 461 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Qualcomm® Dragonwing™ QCS6490 | 131.579 ms | 2 - 6 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Qualcomm® Dragonwing™ QCS8275 | 75.115 ms | 2 - 372 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 27.969 ms | 0 - 19 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Qualcomm® SA8775P | 24.465 ms | 3 - 371 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Qualcomm® SA8650P | 24.465 ms | 3 - 371 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Qualcomm® SA8255P | 24.465 ms | 3 - 371 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Qualcomm® QCS8450 | 47.823 ms | 3 - 461 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Qualcomm® Dragonwing™ IQ-9075 | 24.708 ms | 0 - 5 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Qualcomm® Dragonwing™ IQ-X7181 | 26.962 ms | 3 - 3 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Qualcomm® Dragonwing™ Q-6690 | 435.241 ms | 3 - 425 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Qualcomm® Dragonwing™ Q-7790 | 47.007 ms | 3 - 389 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Qualcomm® Dragonwing™ Q-8750 | 16.967 ms | 3 - 374 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Qualcomm® SA7255P | 75.115 ms | 2 - 372 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Qualcomm® SA8295P | 41.916 ms | 0 - 367 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Snapdragon® 8 Elite Mobile | 16.967 ms | 3 - 374 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 13.242 ms | 3 - 390 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Snapdragon® 7 Gen 4 Mobile | 47.007 ms | 3 - 389 MB | NPU
| ResNet-2Plus1D | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 1500.788 ms | 0 - 798 MB | NPU
| ResNet-2Plus1D | TFLITE | float | Snapdragon® 8 Gen 1 Mobile | 2377.92 ms | 1 - 736 MB | NPU
| ResNet-2Plus1D | TFLITE | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 1942.958 ms | 0 - 4 MB | NPU
| ResNet-2Plus1D | TFLITE | float | Qualcomm® SA8775P | 1944.202 ms | 1 - 579 MB | NPU
| ResNet-2Plus1D | TFLITE | float | Qualcomm® SA8650P | 1944.202 ms | 1 - 579 MB | NPU
| ResNet-2Plus1D | TFLITE | float | Qualcomm® SA8255P | 1944.202 ms | 1 - 579 MB | NPU
| ResNet-2Plus1D | TFLITE | float | Qualcomm® QCS8450 | 2377.92 ms | 1 - 736 MB | NPU
| ResNet-2Plus1D | TFLITE | float | Qualcomm® Dragonwing™ IQ-9075 | 2008.742 ms | 0 - 82 MB | NPU
| ResNet-2Plus1D | TFLITE | float | Qualcomm® Dragonwing™ Q-8750 | 1410.823 ms | 1 - 571 MB | NPU
| ResNet-2Plus1D | TFLITE | float | Qualcomm® SA8295P | 2444.504 ms | 1 - 540 MB | NPU
| ResNet-2Plus1D | TFLITE | float | Snapdragon® 8 Elite Mobile | 1410.823 ms | 1 - 571 MB | NPU
| ResNet-2Plus1D | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 1268.766 ms | 0 - 573 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Snapdragon® 8 Gen 3 Mobile | 3090.411 ms | 1 - 1621 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Snapdragon® 8 Gen 1 Mobile | 4717.092 ms | 0 - 1336 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Qualcomm® Dragonwing™ QCS6490 | 8663.477 ms | 1447 - 2239 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 4137.099 ms | 2 - 6 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Qualcomm® SA8775P | 4293.165 ms | 2 - 1380 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Qualcomm® SA8650P | 4293.165 ms | 2 - 1380 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Qualcomm® SA8255P | 4293.165 ms | 2 - 1380 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Qualcomm® QCS8450 | 4717.092 ms | 0 - 1336 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Qualcomm® Dragonwing™ IQ-9075 | 4295.693 ms | 0 - 79 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Qualcomm® Dragonwing™ Q-6690 | 8068.589 ms | 1393 - 1638 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Qualcomm® Dragonwing™ Q-7790 | 6288.272 ms | 1342 - 1725 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Qualcomm® Dragonwing™ Q-8750 | 2804.666 ms | 1 - 1676 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Qualcomm® SA8295P | 4657.152 ms | 2 - 1091 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Snapdragon® 8 Elite Mobile | 2804.666 ms | 1 - 1676 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 3452.104 ms | 2 - 1455 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Snapdragon® 7 Gen 4 Mobile | 6288.272 ms | 1342 - 1725 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).