---
title: EfficientNet-Lite4
canonical_url: "https://www.modelscope.cn/models/Qualcomm-AI-Hub/EfficientNet-Lite4"
md_url: "https://www.modelscope.cn/models/Qualcomm-AI-Hub/EfficientNet-Lite4.md"
repository: Qualcomm-AI-Hub/EfficientNet-Lite4
last_updated: 2026-10-16
license: apache-2.0
pipeline_tag: image-classification
tasks:
  - image-classification
downloads: 0
stars: 0
tags:
  - backbone
  - bu_auto
  - android
---

# EfficientNet-Lite4

> EfficientNet-Lite4 - Qualcomm-AI-Hub 在 ModelScope 开源的模型。EfficientNet-Lite4: Optimized for Qualcomm Devices

Qualcomm-AI-Hub/EfficientNet-Lite4 是 ModelScope 魔搭社区上的image-classification模型，采用 apache-2.0 许可。

- **Repository**: Qualcomm-AI-Hub/EfficientNet-Lite4
- **License**: apache-2.0
- **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-Lite4

---

![](https://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/qai-hub-models/models/efficientnet_lite4/web-assets/model_demo.png)

# EfficientNet-Lite4: Optimized for Qualcomm Devices

EfficientNet-Lite4 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-Lite4 found [here](https://github.com/RangiLyu/EfficientNet-Lite).
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_lite4) 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_lite4/releases/v0.62.1/efficientnet_lite4-onnx-float.zip)
| ONNX | w8a8 | 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_lite4/releases/v0.62.1/efficientnet_lite4-onnx-w8a8.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_lite4/releases/v0.62.1/efficientnet_lite4-qnn_dlc-float.zip)
| QNN_DLC | w8a8 | Universal | QAIRT 2.45 | [Download](https://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/qai-hub-models/models/efficientnet_lite4/releases/v0.62.1/efficientnet_lite4-qnn_dlc-w8a8.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_lite4/releases/v0.62.1/efficientnet_lite4-tflite-float.zip)
| TFLITE | w8a8 | Universal | QAIRT 2.45 | [Download](https://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/qai-hub-models/models/efficientnet_lite4/releases/v0.62.1/efficientnet_lite4-tflite-w8a8.zip)

For more device-specific assets and performance metrics, visit **[EfficientNet-Lite4 on Qualcomm® AI Hub](https://aihub.qualcomm.cn/models/efficientnet_lite4)**.


### 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_lite4) 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-Lite4 on GitHub](https://github.com/qualcomm/ai-hub-models-cn/blob/main/src/qai_hub_models/models/efficientnet_lite4) for usage instructions.

## Model Details

**Model Type:** Model_use_case.image_classification

**Model Stats:**
- Input resolution: 300x300
- Model checkpoint: Imagenet
- Model size (float): 51.9 MB
- Number of parameters: 13.01M

## Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
|---|---|---|---|---|---|---
| EfficientNet-Lite4 | ONNX | float | Snapdragon® X2 Elite | 1.448 ms | 1 - 1 MB | NPU
| EfficientNet-Lite4 | ONNX | float | Snapdragon® X Elite | 2.736 ms | 29 - 29 MB | NPU
| EfficientNet-Lite4 | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 1.913 ms | 0 - 102 MB | NPU
| EfficientNet-Lite4 | ONNX | float | Snapdragon® 8 Gen 1 Mobile | 5.536 ms | 0 - 100 MB | NPU
| EfficientNet-Lite4 | ONNX | float | Qualcomm® Dragonwing™ IQ-8275 | 4.631 ms | 1 - 5 MB | NPU
| EfficientNet-Lite4 | ONNX | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 2.623 ms | 0 - 33 MB | NPU
| EfficientNet-Lite4 | ONNX | float | Qualcomm® QCS8450 | 5.536 ms | 0 - 100 MB | NPU
| EfficientNet-Lite4 | ONNX | float | Qualcomm® Dragonwing™ IQ-9075 | 4.145 ms | 1 - 4 MB | NPU
| EfficientNet-Lite4 | ONNX | float | Qualcomm® Dragonwing™ IQ-X7181 | 2.736 ms | 29 - 29 MB | NPU
| EfficientNet-Lite4 | ONNX | float | Qualcomm® Dragonwing™ Q-8750 | 1.499 ms | 0 - 55 MB | NPU
| EfficientNet-Lite4 | ONNX | float | Snapdragon® 8 Elite Mobile | 1.499 ms | 0 - 55 MB | NPU
| EfficientNet-Lite4 | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 1.145 ms | 0 - 58 MB | NPU
| EfficientNet-Lite4 | ONNX | w8a8 | Snapdragon® X2 Elite | 0.646 ms | 1 - 1 MB | NPU
| EfficientNet-Lite4 | ONNX | w8a8 | Snapdragon® X Elite | 1.283 ms | 15 - 15 MB | NPU
| EfficientNet-Lite4 | ONNX | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.874 ms | 0 - 110 MB | NPU
| EfficientNet-Lite4 | ONNX | w8a8 | Snapdragon® 8 Gen 1 Mobile | 2.077 ms | 0 - 117 MB | NPU
| EfficientNet-Lite4 | ONNX | w8a8 | Qualcomm® Dragonwing™ QCS6490 | 5.978 ms | 0 - 3 MB | NPU
| EfficientNet-Lite4 | ONNX | w8a8 | Qualcomm® Dragonwing™ IQ-8275 | 1.335 ms | 0 - 4 MB | NPU
| EfficientNet-Lite4 | ONNX | w8a8 | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 1.24 ms | 0 - 18 MB | NPU
| EfficientNet-Lite4 | ONNX | w8a8 | Qualcomm® QCS8450 | 2.077 ms | 0 - 117 MB | NPU
| EfficientNet-Lite4 | ONNX | w8a8 | Qualcomm® Dragonwing™ IQ-9075 | 1.422 ms | 0 - 3 MB | NPU
| EfficientNet-Lite4 | ONNX | w8a8 | Qualcomm® Dragonwing™ IQ-X7181 | 1.283 ms | 15 - 15 MB | NPU
| EfficientNet-Lite4 | ONNX | w8a8 | Qualcomm® Dragonwing™ Q-6690 | 10.939 ms | 0 - 194 MB | NPU
| EfficientNet-Lite4 | ONNX | w8a8 | Qualcomm® Dragonwing™ Q-7790 | 1.592 ms | 0 - 72 MB | NPU
| EfficientNet-Lite4 | ONNX | w8a8 | Qualcomm® Dragonwing™ Q-8750 | 0.715 ms | 0 - 73 MB | NPU
| EfficientNet-Lite4 | ONNX | w8a8 | Snapdragon® 8 Elite Mobile | 0.715 ms | 0 - 73 MB | NPU
| EfficientNet-Lite4 | ONNX | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.601 ms | 0 - 73 MB | NPU
| EfficientNet-Lite4 | ONNX | w8a8 | Snapdragon® 7 Gen 4 Mobile | 1.592 ms | 0 - 72 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | float | Snapdragon® X2 Elite | 1.878 ms | 1 - 1 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | float | Snapdragon® X Elite | 3.486 ms | 1 - 1 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 2.209 ms | 0 - 98 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | float | Snapdragon® 8 Gen 1 Mobile | 6.89 ms | 0 - 99 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-8275 | 4.841 ms | 1 - 4 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 3.16 ms | 1 - 3 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | float | Qualcomm® SA8775P | 4.785 ms | 1 - 55 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | float | Qualcomm® SA8650P | 4.785 ms | 1 - 55 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | float | Qualcomm® SA8255P | 4.785 ms | 1 - 55 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | float | Qualcomm® QCS8450 | 6.89 ms | 0 - 99 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 4.512 ms | 1 - 4 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 3.486 ms | 1 - 1 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 1.689 ms | 1 - 57 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | float | Qualcomm® SA7255P | 15.278 ms | 1 - 53 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | float | Qualcomm® SA8295P | 6.462 ms | 1 - 50 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 1.689 ms | 1 - 57 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 1.267 ms | 1 - 58 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | w8a8 | Snapdragon® X2 Elite | 0.734 ms | 0 - 0 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | w8a8 | Snapdragon® X Elite | 1.444 ms | 0 - 0 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.907 ms | 0 - 98 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | w8a8 | Snapdragon® 8 Gen 1 Mobile | 2.352 ms | 0 - 107 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | w8a8 | Qualcomm® Dragonwing™ QCS6490 | 7.183 ms | 0 - 2 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | w8a8 | Qualcomm® Dragonwing™ IQ-8275 | 1.329 ms | 0 - 3 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | w8a8 | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 1.263 ms | 0 - 95 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | w8a8 | Qualcomm® SA8775P | 1.611 ms | 0 - 66 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | w8a8 | Qualcomm® SA8650P | 1.611 ms | 0 - 66 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | w8a8 | Qualcomm® SA8255P | 1.611 ms | 0 - 66 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | w8a8 | Qualcomm® QCS8450 | 2.352 ms | 0 - 107 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | w8a8 | Qualcomm® Dragonwing™ IQ-9075 | 1.419 ms | 0 - 2 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | w8a8 | Qualcomm® Dragonwing™ IQ-X7181 | 1.444 ms | 0 - 0 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | w8a8 | Qualcomm® Dragonwing™ Q-6690 | 11.29 ms | 0 - 183 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | w8a8 | Qualcomm® Dragonwing™ Q-7790 | 1.614 ms | 0 - 65 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | w8a8 | Qualcomm® Dragonwing™ Q-8750 | 0.662 ms | 0 - 63 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | w8a8 | Qualcomm® SA7255P | 3.364 ms | 0 - 63 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | w8a8 | Qualcomm® SA8295P | 2.532 ms | 0 - 61 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | w8a8 | Snapdragon® 8 Elite Mobile | 0.662 ms | 0 - 63 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.526 ms | 0 - 67 MB | NPU
| EfficientNet-Lite4 | QNN_DLC | w8a8 | Snapdragon® 7 Gen 4 Mobile | 1.614 ms | 0 - 65 MB | NPU
| EfficientNet-Lite4 | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 2.198 ms | 0 - 112 MB | NPU
| EfficientNet-Lite4 | TFLITE | float | Snapdragon® 8 Gen 1 Mobile | 6.673 ms | 0 - 112 MB | NPU
| EfficientNet-Lite4 | TFLITE | float | Qualcomm® Dragonwing™ IQ-8275 | 4.852 ms | 0 - 33 MB | NPU
| EfficientNet-Lite4 | TFLITE | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 3.128 ms | 0 - 3 MB | NPU
| EfficientNet-Lite4 | TFLITE | float | Qualcomm® SA8775P | 4.792 ms | 0 - 66 MB | NPU
| EfficientNet-Lite4 | TFLITE | float | Qualcomm® SA8650P | 4.792 ms | 0 - 66 MB | NPU
| EfficientNet-Lite4 | TFLITE | float | Qualcomm® SA8255P | 4.792 ms | 0 - 66 MB | NPU
| EfficientNet-Lite4 | TFLITE | float | Qualcomm® QCS8450 | 6.673 ms | 0 - 112 MB | NPU
| EfficientNet-Lite4 | TFLITE | float | Qualcomm® Dragonwing™ IQ-9075 | 4.539 ms | 0 - 32 MB | NPU
| EfficientNet-Lite4 | TFLITE | float | Qualcomm® Dragonwing™ Q-8750 | 1.689 ms | 0 - 67 MB | NPU
| EfficientNet-Lite4 | TFLITE | float | Qualcomm® SA7255P | 15.322 ms | 0 - 65 MB | NPU
| EfficientNet-Lite4 | TFLITE | float | Qualcomm® SA8295P | 6.457 ms | 0 - 55 MB | NPU
| EfficientNet-Lite4 | TFLITE | float | Snapdragon® 8 Elite Mobile | 1.689 ms | 0 - 67 MB | NPU
| EfficientNet-Lite4 | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 1.26 ms | 0 - 68 MB | NPU
| EfficientNet-Lite4 | TFLITE | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.657 ms | 0 - 98 MB | NPU
| EfficientNet-Lite4 | TFLITE | w8a8 | Snapdragon® 8 Gen 1 Mobile | 1.755 ms | 0 - 106 MB | NPU
| EfficientNet-Lite4 | TFLITE | w8a8 | Qualcomm® Dragonwing™ QCS6490 | 5.752 ms | 0 - 31 MB | NPU
| EfficientNet-Lite4 | TFLITE | w8a8 | Qualcomm® Dragonwing™ IQ-8275 | 1.046 ms | 0 - 18 MB | NPU
| EfficientNet-Lite4 | TFLITE | w8a8 | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 0.917 ms | 0 - 5 MB | NPU
| EfficientNet-Lite4 | TFLITE | w8a8 | Qualcomm® SA8775P | 1.278 ms | 0 - 63 MB | NPU
| EfficientNet-Lite4 | TFLITE | w8a8 | Qualcomm® SA8650P | 1.278 ms | 0 - 63 MB | NPU
| EfficientNet-Lite4 | TFLITE | w8a8 | Qualcomm® SA8255P | 1.278 ms | 0 - 63 MB | NPU
| EfficientNet-Lite4 | TFLITE | w8a8 | Qualcomm® QCS8450 | 1.755 ms | 0 - 106 MB | NPU
| EfficientNet-Lite4 | TFLITE | w8a8 | Qualcomm® Dragonwing™ IQ-9075 | 1.069 ms | 0 - 17 MB | NPU
| EfficientNet-Lite4 | TFLITE | w8a8 | Qualcomm® Dragonwing™ Q-6690 | 10.033 ms | 0 - 178 MB | NPU
| EfficientNet-Lite4 | TFLITE | w8a8 | Qualcomm® Dragonwing™ Q-7790 | 1.246 ms | 0 - 175 MB | NPU
| EfficientNet-Lite4 | TFLITE | w8a8 | Qualcomm® Dragonwing™ Q-8750 | 0.52 ms | 0 - 57 MB | NPU
| EfficientNet-Lite4 | TFLITE | w8a8 | Qualcomm® SA7255P | 2.762 ms | 0 - 59 MB | NPU
| EfficientNet-Lite4 | TFLITE | w8a8 | Qualcomm® SA8295P | 2.14 ms | 0 - 56 MB | NPU
| EfficientNet-Lite4 | TFLITE | w8a8 | Snapdragon® 8 Elite Mobile | 0.52 ms | 0 - 57 MB | NPU
| EfficientNet-Lite4 | TFLITE | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.415 ms | 0 - 62 MB | NPU
| EfficientNet-Lite4 | TFLITE | w8a8 | Snapdragon® 7 Gen 4 Mobile | 1.246 ms | 0 - 175 MB | NPU

## License
* The license for the original implementation of EfficientNet-Lite4 can be found
  [here](https://github.com/RangiLyu/EfficientNet-Lite/blob/main/LICENSE).

## References
* [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946)
* [Source Model Implementation](https://github.com/RangiLyu/EfficientNet-Lite)

## Community
* For questions or feedback, chat with our engineers in [Qualcomm® AI Hub Workbench](https://workbench.aihub.qualcomm.cn).