---
title: EfficientNet-V2-s
canonical_url: "https://www.modelscope.cn/models/Qualcomm-AI-Hub/EfficientNet-V2-s"
md_url: "https://www.modelscope.cn/models/Qualcomm-AI-Hub/EfficientNet-V2-s.md"
repository: Qualcomm-AI-Hub/EfficientNet-V2-s
last_updated: 2026-10-16
license: bsd-3-clause
pipeline_tag: image-classification
tasks:
  - image-classification
downloads: 0
stars: 0
tags:
  - backbone
  - bu_auto
  - android
---

# EfficientNet-V2-s

> EfficientNet-V2-s - Qualcomm-AI-Hub 在 ModelScope 开源的模型。EfficientNet-V2-s: Optimized for Qualcomm Devices

Qualcomm-AI-Hub/EfficientNet-V2-s 是 ModelScope 魔搭社区上的image-classification模型，采用 bsd-3-clause 许可。

- **Repository**: Qualcomm-AI-Hub/EfficientNet-V2-s
- **License**: bsd-3-clause
- **Tasks**: image-classification
- **Tags**: backbone, bu_auto, android
- **Downloads**: 0
- **Stars**: 0
- **Last updated**: 2026-10-16

Source: https://www.modelscope.cn/models/Qualcomm-AI-Hub/EfficientNet-V2-s

---

![](https://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/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/qualcomm/ai-hub-models-cn/blob/main/src/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.cn) 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://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/qai-hub-models/models/efficientnet_v2_s/releases/v0.62.1/efficientnet_v2_s-onnx-float.zip)
| ONNX | w8a16 | Universal | QAIRT 2.45, ONNX Runtime 1.27.1 | [Download](https://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/qai-hub-models/models/efficientnet_v2_s/releases/v0.62.1/efficientnet_v2_s-onnx-w8a16.zip)
| QNN_DLC | float | Universal | QAIRT 2.45 | [Download](https://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/qai-hub-models/models/efficientnet_v2_s/releases/v0.62.1/efficientnet_v2_s-qnn_dlc-float.zip)
| QNN_DLC | w8a16 | Universal | QAIRT 2.45 | [Download](https://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/qai-hub-models/models/efficientnet_v2_s/releases/v0.62.1/efficientnet_v2_s-qnn_dlc-w8a16.zip)
| TFLITE | float | Universal | QAIRT 2.45 | [Download](https://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/qai-hub-models/models/efficientnet_v2_s/releases/v0.62.1/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.cn/models/efficientnet_v2_s)**.


### Option 2: Export with Custom Configurations

Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models-cn/blob/main/src/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/qualcomm/ai-hub-models-cn/blob/main/src/qai_hub_models/models/efficientnet_v2_s) for usage instructions.

## Model Details

**Model Type:** Model_use_case.image_classification

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

## Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
|---|---|---|---|---|---|---
| EfficientNet-V2-s | ONNX | float | Snapdragon® X2 Elite | 3.041 ms | 2 - 2 MB | NPU
| EfficientNet-V2-s | ONNX | float | Snapdragon® X Elite | 5.591 ms | 46 - 46 MB | NPU
| EfficientNet-V2-s | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 4.055 ms | 0 - 165 MB | NPU
| EfficientNet-V2-s | ONNX | float | Snapdragon® 8 Gen 1 Mobile | 11.642 ms | 1 - 197 MB | NPU
| EfficientNet-V2-s | ONNX | float | Qualcomm® Dragonwing™ IQ-8275 | 8.114 ms | 2 - 7 MB | NPU
| EfficientNet-V2-s | ONNX | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 5.353 ms | 0 - 232 MB | NPU
| EfficientNet-V2-s | ONNX | float | Qualcomm® QCS8450 | 11.642 ms | 1 - 197 MB | NPU
| EfficientNet-V2-s | ONNX | float | Qualcomm® Dragonwing™ IQ-9075 | 7.648 ms | 1 - 6 MB | NPU
| EfficientNet-V2-s | ONNX | float | Qualcomm® Dragonwing™ IQ-X7181 | 5.591 ms | 46 - 46 MB | NPU
| EfficientNet-V2-s | ONNX | float | Qualcomm® Dragonwing™ Q-8750 | 3.1 ms | 0 - 202 MB | NPU
| EfficientNet-V2-s | ONNX | float | Snapdragon® 8 Elite Mobile | 3.1 ms | 0 - 202 MB | NPU
| EfficientNet-V2-s | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 2.318 ms | 0 - 205 MB | NPU
| EfficientNet-V2-s | ONNX | w8a16 | Snapdragon® X2 Elite | 2.363 ms | 2 - 2 MB | NPU
| EfficientNet-V2-s | ONNX | w8a16 | Snapdragon® X Elite | 5.696 ms | 24 - 24 MB | NPU
| EfficientNet-V2-s | ONNX | w8a16 | Snapdragon® 8 Gen 3 Mobile | 3.641 ms | 0 - 207 MB | NPU
| EfficientNet-V2-s | ONNX | w8a16 | Snapdragon® 8 Gen 1 Mobile | 6.733 ms | 1 - 215 MB | NPU
| EfficientNet-V2-s | ONNX | w8a16 | Qualcomm® Dragonwing™ QCS6490 | 20.194 ms | 1 - 4 MB | NPU
| EfficientNet-V2-s | ONNX | w8a16 | Qualcomm® Dragonwing™ IQ-8275 | 5.131 ms | 1 - 4 MB | NPU
| EfficientNet-V2-s | ONNX | w8a16 | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 5.453 ms | 0 - 240 MB | NPU
| EfficientNet-V2-s | ONNX | w8a16 | Qualcomm® QCS8450 | 6.733 ms | 1 - 215 MB | NPU
| EfficientNet-V2-s | ONNX | w8a16 | Qualcomm® Dragonwing™ IQ-9075 | 5.788 ms | 1 - 4 MB | NPU
| EfficientNet-V2-s | ONNX | w8a16 | Qualcomm® Dragonwing™ IQ-X7181 | 5.696 ms | 24 - 24 MB | NPU
| EfficientNet-V2-s | ONNX | w8a16 | Qualcomm® Dragonwing™ Q-6690 | 39.079 ms | 0 - 271 MB | NPU
| EfficientNet-V2-s | ONNX | w8a16 | Qualcomm® Dragonwing™ Q-7790 | 6.572 ms | 1 - 271 MB | NPU
| EfficientNet-V2-s | ONNX | w8a16 | Qualcomm® Dragonwing™ Q-8750 | 2.5 ms | 0 - 162 MB | NPU
| EfficientNet-V2-s | ONNX | w8a16 | Snapdragon® 8 Elite Mobile | 2.5 ms | 0 - 162 MB | NPU
| EfficientNet-V2-s | ONNX | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 2.021 ms | 0 - 165 MB | NPU
| EfficientNet-V2-s | ONNX | w8a16 | Snapdragon® 7 Gen 4 Mobile | 6.572 ms | 1 - 271 MB | NPU
| EfficientNet-V2-s | QNN_DLC | float | Snapdragon® X2 Elite | 3.435 ms | 2 - 2 MB | NPU
| EfficientNet-V2-s | QNN_DLC | float | Snapdragon® X Elite | 6.384 ms | 2 - 2 MB | NPU
| EfficientNet-V2-s | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 4.343 ms | 0 - 158 MB | NPU
| EfficientNet-V2-s | QNN_DLC | float | Snapdragon® 8 Gen 1 Mobile | 13.548 ms | 0 - 188 MB | NPU
| EfficientNet-V2-s | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-8275 | 8.213 ms | 2 - 6 MB | NPU
| EfficientNet-V2-s | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 5.804 ms | 2 - 4 MB | NPU
| EfficientNet-V2-s | QNN_DLC | float | Qualcomm® SA8775P | 8.209 ms | 2 - 76 MB | NPU
| EfficientNet-V2-s | QNN_DLC | float | Qualcomm® SA8650P | 8.209 ms | 2 - 76 MB | NPU
| EfficientNet-V2-s | QNN_DLC | float | Qualcomm® SA8255P | 8.209 ms | 2 - 76 MB | NPU
| EfficientNet-V2-s | QNN_DLC | float | Qualcomm® QCS8450 | 13.548 ms | 0 - 188 MB | NPU
| EfficientNet-V2-s | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 7.909 ms | 2 - 5 MB | NPU
| EfficientNet-V2-s | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 6.384 ms | 2 - 2 MB | NPU
| EfficientNet-V2-s | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 3.132 ms | 0 - 78 MB | NPU
| EfficientNet-V2-s | QNN_DLC | float | Qualcomm® SA7255P | 25.729 ms | 2 - 75 MB | NPU
| EfficientNet-V2-s | QNN_DLC | float | Qualcomm® SA8295P | 13.03 ms | 2 - 107 MB | NPU
| EfficientNet-V2-s | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 3.132 ms | 0 - 78 MB | NPU
| EfficientNet-V2-s | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 2.36 ms | 2 - 85 MB | NPU
| EfficientNet-V2-s | QNN_DLC | w8a16 | Snapdragon® X2 Elite | 2.913 ms | 1 - 1 MB | NPU
| EfficientNet-V2-s | QNN_DLC | w8a16 | Snapdragon® X Elite | 6.767 ms | 1 - 1 MB | NPU
| EfficientNet-V2-s | QNN_DLC | w8a16 | Snapdragon® 8 Gen 3 Mobile | 4.183 ms | 0 - 175 MB | NPU
| EfficientNet-V2-s | QNN_DLC | w8a16 | Snapdragon® 8 Gen 1 Mobile | 8.035 ms | 1 - 187 MB | NPU
| EfficientNet-V2-s | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ QCS6490 | 21.432 ms | 1 - 3 MB | NPU
| EfficientNet-V2-s | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ IQ-8275 | 5.795 ms | 1 - 4 MB | NPU
| EfficientNet-V2-s | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 6.242 ms | 1 - 3 MB | NPU
| EfficientNet-V2-s | QNN_DLC | w8a16 | Qualcomm® SA8775P | 6.716 ms | 1 - 136 MB | NPU
| EfficientNet-V2-s | QNN_DLC | w8a16 | Qualcomm® SA8650P | 6.716 ms | 1 - 136 MB | NPU
| EfficientNet-V2-s | QNN_DLC | w8a16 | Qualcomm® SA8255P | 6.716 ms | 1 - 136 MB | NPU
| EfficientNet-V2-s | QNN_DLC | w8a16 | Qualcomm® QCS8450 | 8.035 ms | 1 - 187 MB | NPU
| EfficientNet-V2-s | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ IQ-9075 | 6.473 ms | 3 - 5 MB | NPU
| EfficientNet-V2-s | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ IQ-X7181 | 6.767 ms | 1 - 1 MB | NPU
| EfficientNet-V2-s | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ Q-6690 | 40.733 ms | 1 - 253 MB | NPU
| EfficientNet-V2-s | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ Q-7790 | 7.206 ms | 1 - 254 MB | NPU
| EfficientNet-V2-s | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ Q-8750 | 2.825 ms | 0 - 143 MB | NPU
| EfficientNet-V2-s | QNN_DLC | w8a16 | Qualcomm® SA7255P | 12.076 ms | 1 - 134 MB | NPU
| EfficientNet-V2-s | QNN_DLC | w8a16 | Qualcomm® SA8295P | 8.38 ms | 1 - 134 MB | NPU
| EfficientNet-V2-s | QNN_DLC | w8a16 | Snapdragon® 8 Elite Mobile | 2.825 ms | 0 - 143 MB | NPU
| EfficientNet-V2-s | QNN_DLC | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 2.245 ms | 1 - 146 MB | NPU
| EfficientNet-V2-s | QNN_DLC | w8a16 | Snapdragon® 7 Gen 4 Mobile | 7.206 ms | 1 - 254 MB | NPU
| EfficientNet-V2-s | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 4.324 ms | 0 - 201 MB | NPU
| EfficientNet-V2-s | TFLITE | float | Snapdragon® 8 Gen 1 Mobile | 13.403 ms | 0 - 230 MB | NPU
| EfficientNet-V2-s | TFLITE | float | Qualcomm® Dragonwing™ IQ-8275 | 8.214 ms | 0 - 52 MB | NPU
| EfficientNet-V2-s | TFLITE | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 5.756 ms | 0 - 2 MB | NPU
| EfficientNet-V2-s | TFLITE | float | Qualcomm® SA8775P | 8.252 ms | 0 - 118 MB | NPU
| EfficientNet-V2-s | TFLITE | float | Qualcomm® SA8650P | 8.252 ms | 0 - 118 MB | NPU
| EfficientNet-V2-s | TFLITE | float | Qualcomm® SA8255P | 8.252 ms | 0 - 118 MB | NPU
| EfficientNet-V2-s | TFLITE | float | Qualcomm® QCS8450 | 13.403 ms | 0 - 230 MB | NPU
| EfficientNet-V2-s | TFLITE | float | Qualcomm® Dragonwing™ IQ-9075 | 7.951 ms | 0 - 51 MB | NPU
| EfficientNet-V2-s | TFLITE | float | Qualcomm® Dragonwing™ Q-8750 | 3.135 ms | 0 - 124 MB | NPU
| EfficientNet-V2-s | TFLITE | float | Qualcomm® SA7255P | 25.847 ms | 0 - 115 MB | NPU
| EfficientNet-V2-s | TFLITE | float | Qualcomm® SA8295P | 13.025 ms | 0 - 148 MB | NPU
| EfficientNet-V2-s | TFLITE | float | Snapdragon® 8 Elite Mobile | 3.135 ms | 0 - 124 MB | NPU
| EfficientNet-V2-s | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 2.33 ms | 0 - 120 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
* For questions or feedback, chat with our engineers in [Qualcomm® AI Hub Workbench](https://workbench.aihub.qualcomm.cn).