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---
library_name: pytorch
license: agpl-3.0
pipeline_tag: image-segmentation
tags:
- android
---
# FastSam-X: Optimized for Mobile Deployment
## Generate high quality segmentation mask on device
The Fast Segment Anything Model (FastSAM) is a novel, real-time CNN-based solution for the Segment Anything task. This task is designed to segment any object within an image based on various possible user interaction prompts. The model performs competitively despite significantly reduced computation, making it a practical choice for a variety of vision tasks.
This model is an implementation of FastSam-X found [here]({source_repo}).
This repository provides scripts to run FastSam-X on Qualcomm® devices.
More details on model performance across various devices, can be found
[here](https://aihub.qualcomm.com/models/fastsam_x).
### Model Details
- **Model Type:** Semantic segmentation
- **Model Stats:**
- Model checkpoint: fastsam-x.pt
- Inference latency: RealTime
- Input resolution: 640x640
- Number of parameters: 72.2M
- Model size: 276 MB
| Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
|---|---|---|---|---|---|---|---|---|
| FastSam-X | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 45.671 ms | 5 - 19 MB | FP16 | NPU | [FastSam-X.so](https://huggingface.co/qualcomm/FastSam-X/blob/main/FastSam-X.so) |
| FastSam-X | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 48.826 ms | 0 - 157 MB | FP16 | NPU | [FastSam-X.onnx](https://huggingface.co/qualcomm/FastSam-X/blob/main/FastSam-X.onnx) |
| FastSam-X | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 38.249 ms | 5 - 63 MB | FP16 | NPU | [FastSam-X.so](https://huggingface.co/qualcomm/FastSam-X/blob/main/FastSam-X.so) |
| FastSam-X | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 39.188 ms | 1 - 157 MB | FP16 | NPU | [FastSam-X.onnx](https://huggingface.co/qualcomm/FastSam-X/blob/main/FastSam-X.onnx) |
| FastSam-X | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 43.275 ms | 5 - 6 MB | FP16 | NPU | Use Export Script |
| FastSam-X | SA8255 (Proxy) | SA8255P Proxy | QNN | 43.253 ms | 5 - 11 MB | FP16 | NPU | Use Export Script |
| FastSam-X | SA8775 (Proxy) | SA8775P Proxy | QNN | 42.992 ms | 5 - 12 MB | FP16 | NPU | Use Export Script |
| FastSam-X | SA8650 (Proxy) | SA8650P Proxy | QNN | 43.064 ms | 5 - 13 MB | FP16 | NPU | Use Export Script |
| FastSam-X | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 90.623 ms | 5 - 67 MB | FP16 | NPU | Use Export Script |
| FastSam-X | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 30.823 ms | 5 - 61 MB | FP16 | NPU | Use Export Script |
| FastSam-X | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 31.678 ms | 1 - 77 MB | FP16 | NPU | [FastSam-X.onnx](https://huggingface.co/qualcomm/FastSam-X/blob/main/FastSam-X.onnx) |
| FastSam-X | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 44.484 ms | 5 - 5 MB | FP16 | NPU | Use Export Script |
| FastSam-X | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 49.5 ms | 139 - 139 MB | FP16 | NPU | [FastSam-X.onnx](https://huggingface.co/qualcomm/FastSam-X/blob/main/FastSam-X.onnx) |
## Installation
This model can be installed as a Python package via pip.
```bash
pip install "qai-hub-models[fastsam_x]"
```
## 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.fastsam_x.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.fastsam_x.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.fastsam_x.export
```
```
Profiling Results
------------------------------------------------------------
FastSam-X
Device : Samsung Galaxy S23 (13)
Runtime : QNN
Estimated inference time (ms) : 45.7
Estimated peak memory usage (MB): [5, 19]
Total # Ops : 418
Compute Unit(s) : NPU (418 ops)
```
## How does this work?
This [export script](https://aihub.qualcomm.com/models/fastsam_x/qai_hub_models/models/FastSam-X/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.fastsam_x import
# Load the model
# Device
device = hub.Device("Samsung Galaxy S23")
```
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.fastsam_x.demo --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.fastsam_x.demo -- --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 FastSam-X's performance across various devices [here](https://aihub.qualcomm.com/models/fastsam_x).
Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
## License
* The license for the original implementation of FastSam-X can be found [here](https://github.com/CASIA-IVA-Lab/FastSAM/blob/main/LICENSE).
* The license for the compiled assets for on-device deployment can be found [here](https://github.com/CASIA-IVA-Lab/FastSAM/blob/main/LICENSE)
## References
* [Fast Segment Anything](https://arxiv.org/abs/2306.12156)
* [Source Model Implementation](https://github.com/CASIA-IVA-Lab/FastSAM)
## 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).
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