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 ---
library_name: pytorch
license: other
tags:
- backbone
- android
pipeline_tag: image-classification
---
![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/efficientnet_b0/web-assets/model_demo.png)
# EfficientNet-B0: Optimized for Mobile Deployment
## Imagenet classifier and general purpose backbone
EfficientNetB0 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 model is an implementation of EfficientNet-B0 found [here](https://github.com/pytorch/vision/blob/main/torchvision/models/efficientnet.py).
This repository provides scripts to run EfficientNet-B0 on Qualcomm® devices.
More details on model performance across various devices, can be found
[here](https://aihub.qualcomm.com/models/efficientnet_b0).
### Model Details
- **Model Type:** Model_use_case.image_classification
- **Model Stats:**
- Model checkpoint: Imagenet
- Input resolution: 224x224
- Number of parameters: 5.27M
- Model size (float): 20.2 MB
| Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model
|---|---|---|---|---|---|---|---|---|
| EfficientNet-B0 | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 4.854 ms | 0 - 26 MB | NPU | [EfficientNet-B0.tflite](https://huggingface.co/qualcomm/EfficientNet-B0/blob/main/EfficientNet-B0.tflite) |
| EfficientNet-B0 | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN | 21.996 ms | 1 - 10 MB | NPU | Use Export Script |
| EfficientNet-B0 | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 2.921 ms | 0 - 44 MB | NPU | [EfficientNet-B0.tflite](https://huggingface.co/qualcomm/EfficientNet-B0/blob/main/EfficientNet-B0.tflite) |
| EfficientNet-B0 | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN | 3.444 ms | 1 - 31 MB | NPU | Use Export Script |
| EfficientNet-B0 | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 1.462 ms | 0 - 117 MB | NPU | [EfficientNet-B0.tflite](https://huggingface.co/qualcomm/EfficientNet-B0/blob/main/EfficientNet-B0.tflite) |
| EfficientNet-B0 | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN | 1.463 ms | 0 - 3 MB | NPU | Use Export Script |
| EfficientNet-B0 | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 1.958 ms | 0 - 27 MB | NPU | [EfficientNet-B0.tflite](https://huggingface.co/qualcomm/EfficientNet-B0/blob/main/EfficientNet-B0.tflite) |
| EfficientNet-B0 | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN | 1.94 ms | 1 - 13 MB | NPU | Use Export Script |
| EfficientNet-B0 | float | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 4.854 ms | 0 - 26 MB | NPU | [EfficientNet-B0.tflite](https://huggingface.co/qualcomm/EfficientNet-B0/blob/main/EfficientNet-B0.tflite) |
| EfficientNet-B0 | float | SA7255P ADP | Qualcomm® SA7255P | QNN | 21.996 ms | 1 - 10 MB | NPU | Use Export Script |
| EfficientNet-B0 | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | TFLITE | 1.465 ms | 0 - 118 MB | NPU | [EfficientNet-B0.tflite](https://huggingface.co/qualcomm/EfficientNet-B0/blob/main/EfficientNet-B0.tflite) |
| EfficientNet-B0 | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN | 1.462 ms | 1 - 3 MB | NPU | Use Export Script |
| EfficientNet-B0 | float | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 3.576 ms | 0 - 33 MB | NPU | [EfficientNet-B0.tflite](https://huggingface.co/qualcomm/EfficientNet-B0/blob/main/EfficientNet-B0.tflite) |
| EfficientNet-B0 | float | SA8295P ADP | Qualcomm® SA8295P | QNN | 3.515 ms | 0 - 18 MB | NPU | Use Export Script |
| EfficientNet-B0 | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | TFLITE | 1.465 ms | 0 - 118 MB | NPU | [EfficientNet-B0.tflite](https://huggingface.co/qualcomm/EfficientNet-B0/blob/main/EfficientNet-B0.tflite) |
| EfficientNet-B0 | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN | 1.466 ms | 1 - 3 MB | NPU | Use Export Script |
| EfficientNet-B0 | float | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 1.958 ms | 0 - 27 MB | NPU | [EfficientNet-B0.tflite](https://huggingface.co/qualcomm/EfficientNet-B0/blob/main/EfficientNet-B0.tflite) |
| EfficientNet-B0 | float | SA8775P ADP | Qualcomm® SA8775P | QNN | 1.94 ms | 1 - 13 MB | NPU | Use Export Script |
| EfficientNet-B0 | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | TFLITE | 1.467 ms | 0 - 118 MB | NPU | [EfficientNet-B0.tflite](https://huggingface.co/qualcomm/EfficientNet-B0/blob/main/EfficientNet-B0.tflite) |
| EfficientNet-B0 | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN | 1.466 ms | 0 - 93 MB | NPU | Use Export Script |
| EfficientNet-B0 | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 1.34 ms | 0 - 63 MB | NPU | [EfficientNet-B0.onnx](https://huggingface.co/qualcomm/EfficientNet-B0/blob/main/EfficientNet-B0.onnx) |
| EfficientNet-B0 | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 1.049 ms | 0 - 40 MB | NPU | [EfficientNet-B0.tflite](https://huggingface.co/qualcomm/EfficientNet-B0/blob/main/EfficientNet-B0.tflite) |
| EfficientNet-B0 | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN | 1.02 ms | 1 - 34 MB | NPU | Use Export Script |
| EfficientNet-B0 | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 0.958 ms | 0 - 40 MB | NPU | [EfficientNet-B0.onnx](https://huggingface.co/qualcomm/EfficientNet-B0/blob/main/EfficientNet-B0.onnx) |
| EfficientNet-B0 | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | TFLITE | 1.023 ms | 0 - 27 MB | NPU | [EfficientNet-B0.tflite](https://huggingface.co/qualcomm/EfficientNet-B0/blob/main/EfficientNet-B0.tflite) |
| EfficientNet-B0 | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN | 0.817 ms | 1 - 26 MB | NPU | Use Export Script |
| EfficientNet-B0 | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 0.805 ms | 1 - 23 MB | NPU | [EfficientNet-B0.onnx](https://huggingface.co/qualcomm/EfficientNet-B0/blob/main/EfficientNet-B0.onnx) |
| EfficientNet-B0 | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 1.67 ms | 1 - 1 MB | NPU | Use Export Script |
| EfficientNet-B0 | float | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 1.447 ms | 13 - 13 MB | NPU | [EfficientNet-B0.onnx](https://huggingface.co/qualcomm/EfficientNet-B0/blob/main/EfficientNet-B0.onnx) |
| EfficientNet-B0 | w8a16 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN | 3.229 ms | 0 - 10 MB | NPU | Use Export Script |
| EfficientNet-B0 | w8a16 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN | 1.952 ms | 0 - 32 MB | NPU | Use Export Script |
| EfficientNet-B0 | w8a16 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN | 1.674 ms | 0 - 4 MB | NPU | Use Export Script |
| EfficientNet-B0 | w8a16 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN | 1.957 ms | 0 - 11 MB | NPU | Use Export Script |
| EfficientNet-B0 | w8a16 | RB3 Gen 2 (Proxy) | Qualcomm® QCS6490 (Proxy) | QNN | 4.94 ms | 0 - 14 MB | NPU | Use Export Script |
| EfficientNet-B0 | w8a16 | SA7255P ADP | Qualcomm® SA7255P | QNN | 3.229 ms | 0 - 10 MB | NPU | Use Export Script |
| EfficientNet-B0 | w8a16 | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN | 1.675 ms | 0 - 3 MB | NPU | Use Export Script |
| EfficientNet-B0 | w8a16 | SA8295P ADP | Qualcomm® SA8295P | QNN | 2.33 ms | 0 - 18 MB | NPU | Use Export Script |
| EfficientNet-B0 | w8a16 | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN | 1.674 ms | 0 - 4 MB | NPU | Use Export Script |
| EfficientNet-B0 | w8a16 | SA8775P ADP | Qualcomm® SA8775P | QNN | 1.957 ms | 0 - 11 MB | NPU | Use Export Script |
| EfficientNet-B0 | w8a16 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN | 1.674 ms | 0 - 40 MB | NPU | Use Export Script |
| EfficientNet-B0 | w8a16 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 1.644 ms | 0 - 59 MB | NPU | [EfficientNet-B0.onnx](https://huggingface.co/qualcomm/EfficientNet-B0/blob/main/EfficientNet-B0_w8a16.onnx) |
| EfficientNet-B0 | w8a16 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN | 1.124 ms | 0 - 39 MB | NPU | Use Export Script |
| EfficientNet-B0 | w8a16 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 1.064 ms | 0 - 47 MB | NPU | [EfficientNet-B0.onnx](https://huggingface.co/qualcomm/EfficientNet-B0/blob/main/EfficientNet-B0_w8a16.onnx) |
| EfficientNet-B0 | w8a16 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN | 0.95 ms | 0 - 28 MB | NPU | Use Export Script |
| EfficientNet-B0 | w8a16 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 0.844 ms | 0 - 38 MB | NPU | [EfficientNet-B0.onnx](https://huggingface.co/qualcomm/EfficientNet-B0/blob/main/EfficientNet-B0_w8a16.onnx) |
| EfficientNet-B0 | w8a16 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 1.893 ms | 0 - 0 MB | NPU | Use Export Script |
| EfficientNet-B0 | w8a16 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 1.737 ms | 8 - 8 MB | NPU | [EfficientNet-B0.onnx](https://huggingface.co/qualcomm/EfficientNet-B0/blob/main/EfficientNet-B0_w8a16.onnx) |
## Installation
Install the package via pip:
```bash
pip install qai-hub-models
```
## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device
Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your
Qualcomm® ID. Once signed in navigate to `Account -> Settings -> API Token`.
With this API token, you can configure your client to run models on the cloud
hosted devices.
```bash
qai-hub configure --api_token API_TOKEN
```
Navigate to [docs](https://app.aihub.qualcomm.com/docs/) for more information.
## Demo off target
The package contains a simple end-to-end demo that downloads pre-trained
weights and runs this model on a sample input.
```bash
python -m qai_hub_models.models.efficientnet_b0.demo
```
The above demo runs a reference implementation of pre-processing, model
inference, and post processing.
**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
environment, please add the following to your cell (instead of the above).
```
%run -m qai_hub_models.models.efficientnet_b0.demo
```
### Run model on a cloud-hosted device
In addition to the demo, you can also run the model on a cloud-hosted Qualcomm®
device. This script does the following:
* Performance check on-device on a cloud-hosted device
* Downloads compiled assets that can be deployed on-device for Android.
* Accuracy check between PyTorch and on-device outputs.
```bash
python -m qai_hub_models.models.efficientnet_b0.export
```
```
Profiling Results
------------------------------------------------------------
EfficientNet-B0
Device : cs_8275 (ANDROID 14)
Runtime : TFLITE
Estimated inference time (ms) : 4.9
Estimated peak memory usage (MB): [0, 26]
Total # Ops : 245
Compute Unit(s) : npu (245 ops) gpu (0 ops) cpu (0 ops)
```
## How does this work?
This [export script](https://aihub.qualcomm.com/models/efficientnet_b0/qai_hub_models/models/EfficientNet-B0/export.py)
leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model
on-device. Lets go through each step below in detail:
Step 1: **Compile model for on-device deployment**
To compile a PyTorch model for on-device deployment, we first trace the model
in memory using the `jit.trace` and then call the `submit_compile_job` API.
```python
import torch
import qai_hub as hub
from qai_hub_models.models.efficientnet_b0 import Model
# Load the model
torch_model = Model.from_pretrained()
# Device
device = hub.Device("Samsung Galaxy S24")
# Trace model
input_shape = torch_model.get_input_spec()
sample_inputs = torch_model.sample_inputs()
pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()])
# Compile model on a specific device
compile_job = hub.submit_compile_job(
model=pt_model,
device=device,
input_specs=torch_model.get_input_spec(),
)
# Get target model to run on-device
target_model = compile_job.get_target_model()
```
Step 2: **Performance profiling on cloud-hosted device**
After compiling models from step 1. Models can be profiled model on-device using the
`target_model`. Note that this scripts runs the model on a device automatically
provisioned in the cloud. Once the job is submitted, you can navigate to a
provided job URL to view a variety of on-device performance metrics.
```python
profile_job = hub.submit_profile_job(
model=target_model,
device=device,
)
```
Step 3: **Verify on-device accuracy**
To verify the accuracy of the model on-device, you can run on-device inference
on sample input data on the same cloud hosted device.
```python
input_data = torch_model.sample_inputs()
inference_job = hub.submit_inference_job(
model=target_model,
device=device,
inputs=input_data,
)
on_device_output = inference_job.download_output_data()
```
With the output of the model, you can compute like PSNR, relative errors or
spot check the output with expected output.
**Note**: This on-device profiling and inference requires access to Qualcomm®
AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup).
## Run demo on a cloud-hosted device
You can also run the demo on-device.
```bash
python -m qai_hub_models.models.efficientnet_b0.demo --eval-mode on-device
```
**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
environment, please add the following to your cell (instead of the above).
```
%run -m qai_hub_models.models.efficientnet_b0.demo -- --eval-mode on-device
```
## Deploying compiled model to Android
The models can be deployed using multiple runtimes:
- TensorFlow Lite (`.tflite` export): [This
tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a
guide to deploy the .tflite model in an Android application.
- QNN (`.so` export ): This [sample
app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html)
provides instructions on how to use the `.so` shared library in an Android application.
## View on Qualcomm® AI Hub
Get more details on EfficientNet-B0's performance across various devices [here](https://aihub.qualcomm.com/models/efficientnet_b0).
Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
## License
* The license for the original implementation of EfficientNet-B0 can be found
[here](https://github.com/pytorch/vision/blob/main/LICENSE).
* The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf)
## References
* [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946)
* [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:[email protected]).