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EfficientNet-V2-s

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EfficientNet-V2-s
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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/efficientnet_v2_s/web-assets/model_demo.png)

# EfficientNet-V2-s: Optimized for Qualcomm Devices

EfficientNetV2-s 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 EfficientNet-V2-s found [here](https://github.com/pytorch/vision/blob/main/torchvision/models/efficientnet.py).
This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/efficientnet_v2_s) 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 2.37, ONNX Runtime 1.23.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/efficientnet_v2_s/releases/v0.46.0/efficientnet_v2_s-onnx-float.zip)
| ONNX | w8a16 | Universal | QAIRT 2.37, ONNX Runtime 1.23.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/efficientnet_v2_s/releases/v0.46.0/efficientnet_v2_s-onnx-w8a16.zip)
| QNN_DLC | float | Universal | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/efficientnet_v2_s/releases/v0.46.0/efficientnet_v2_s-qnn_dlc-float.zip)
| QNN_DLC | w8a16 | Universal | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/efficientnet_v2_s/releases/v0.46.0/efficientnet_v2_s-qnn_dlc-w8a16.zip)
| TFLITE | float | Universal | QAIRT 2.42, TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/efficientnet_v2_s/releases/v0.46.0/efficientnet_v2_s-tflite-float.zip)

For more device-specific assets and performance metrics, visit **[EfficientNet-V2-s on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/efficientnet_v2_s)**.


### Option 2: Export with Custom Configurations

Use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/efficientnet_v2_s) 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 [EfficientNet-V2-s on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/efficientnet_v2_s) for usage instructions.

## Model Details

**Model Type:** Model_use_case.image_classification

**Model Stats:**
- Model checkpoint: Imagenet
- Input resolution: 384x384
- Number of parameters: 21.4M
- Model size (float): 81.7 MB
- Model size (w8a16): 27.2 MB

## Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
|---|---|---|---|---|---|---
| EfficientNet-V2-s | ONNX | float | Snapdragon® X Elite | 2.668 ms | 47 - 47 MB | NPU
| EfficientNet-V2-s | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 2.012 ms | 0 - 211 MB | NPU
| EfficientNet-V2-s | ONNX | float | Qualcomm® QCS8550 (Proxy) | 2.716 ms | 0 - 134 MB | NPU
| EfficientNet-V2-s | ONNX | float | Qualcomm® QCS9075 | 3.615 ms | 0 - 4 MB | NPU
| EfficientNet-V2-s | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 1.591 ms | 0 - 134 MB | NPU
| EfficientNet-V2-s | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 1.358 ms | 0 - 134 MB | NPU
| EfficientNet-V2-s | ONNX | w8a16 | Snapdragon® X Elite | 2.631 ms | 24 - 24 MB | NPU
| EfficientNet-V2-s | ONNX | w8a16 | Snapdragon® 8 Gen 3 Mobile | 1.826 ms | 0 - 228 MB | NPU
| EfficientNet-V2-s | ONNX | w8a16 | Qualcomm® QCS6490 | 311.643 ms | 25 - 32 MB | CPU
| EfficientNet-V2-s | ONNX | w8a16 | Qualcomm® QCS8550 (Proxy) | 2.624 ms | 0 - 31 MB | NPU
| EfficientNet-V2-s | ONNX | w8a16 | Qualcomm® QCS9075 | 2.989 ms | 0 - 3 MB | NPU
| EfficientNet-V2-s | ONNX | w8a16 | Qualcomm® QCM6690 | 142.621 ms | 13 - 26 MB | CPU
| EfficientNet-V2-s | ONNX | w8a16 | Snapdragon® 8 Elite For Galaxy Mobile | 1.335 ms | 0 - 183 MB | NPU
| EfficientNet-V2-s | ONNX | w8a16 | Snapdragon® 7 Gen 4 Mobile | 130.814 ms | 15 - 28 MB | CPU
| EfficientNet-V2-s | ONNX | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 1.121 ms | 0 - 183 MB | NPU
| EfficientNet-V2-s | QNN_DLC | float | Snapdragon® X Elite | 2.943 ms | 1 - 1 MB | NPU
| EfficientNet-V2-s | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 1.959 ms | 0 - 145 MB | NPU
| EfficientNet-V2-s | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 10.921 ms | 1 - 66 MB | NPU
| EfficientNet-V2-s | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 2.671 ms | 1 - 2 MB | NPU
| EfficientNet-V2-s | QNN_DLC | float | Qualcomm® QCS9075 | 3.659 ms | 1 - 3 MB | NPU
| EfficientNet-V2-s | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 5.634 ms | 0 - 155 MB | NPU
| EfficientNet-V2-s | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 1.549 ms | 0 - 68 MB | NPU
| EfficientNet-V2-s | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 1.218 ms | 0 - 69 MB | NPU
| EfficientNet-V2-s | QNN_DLC | w8a16 | Snapdragon® X Elite | 2.929 ms | 0 - 0 MB | NPU
| EfficientNet-V2-s | QNN_DLC | w8a16 | Snapdragon® 8 Gen 3 Mobile | 1.775 ms | 0 - 147 MB | NPU
| EfficientNet-V2-s | QNN_DLC | w8a16 | Qualcomm® QCS6490 | 6.816 ms | 0 - 2 MB | NPU
| EfficientNet-V2-s | QNN_DLC | w8a16 | Qualcomm® QCS8275 (Proxy) | 5.339 ms | 0 - 105 MB | NPU
| EfficientNet-V2-s | QNN_DLC | w8a16 | Qualcomm® QCS8550 (Proxy) | 2.61 ms | 0 - 2 MB | NPU
| EfficientNet-V2-s | QNN_DLC | w8a16 | Qualcomm® QCS9075 | 2.937 ms | 0 - 2 MB | NPU
| EfficientNet-V2-s | QNN_DLC | w8a16 | Qualcomm® QCM6690 | 14.296 ms | 0 - 226 MB | NPU
| EfficientNet-V2-s | QNN_DLC | w8a16 | Qualcomm® QCS8450 (Proxy) | 3.23 ms | 0 - 153 MB | NPU
| EfficientNet-V2-s | QNN_DLC | w8a16 | Snapdragon® 8 Elite For Galaxy Mobile | 1.235 ms | 0 - 108 MB | NPU
| EfficientNet-V2-s | QNN_DLC | w8a16 | Snapdragon® 7 Gen 4 Mobile | 2.919 ms | 0 - 105 MB | NPU
| EfficientNet-V2-s | QNN_DLC | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 1.001 ms | 0 - 109 MB | NPU
| EfficientNet-V2-s | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 1.952 ms | 0 - 196 MB | NPU
| EfficientNet-V2-s | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 10.991 ms | 0 - 113 MB | NPU
| EfficientNet-V2-s | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 2.632 ms | 0 - 2 MB | NPU
| EfficientNet-V2-s | TFLITE | float | Qualcomm® QCS9075 | 3.673 ms | 0 - 50 MB | NPU
| EfficientNet-V2-s | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 5.714 ms | 0 - 205 MB | NPU
| EfficientNet-V2-s | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 1.504 ms | 0 - 120 MB | NPU
| EfficientNet-V2-s | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 1.226 ms | 0 - 117 MB | NPU

## License
* The license for the original implementation of EfficientNet-V2-s can be found
  [here](https://github.com/pytorch/vision/blob/main/LICENSE).

## References
* [EfficientNetV2: Smaller Models and Faster Training](https://arxiv.org/abs/2104.00298)
* [Source Model Implementation](https://github.com/pytorch/vision/blob/main/torchvision/models/efficientnet.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).