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MobileSam

Image Segmentation
PyTorch
LiteRT
ONNX
foundation
android
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MobileSam: Optimized for Mobile Deployment

Faster Segment Anything: Towards lightweight SAM for mobile applications

Transformer based encoder-decoder where prompts specify what to segment in an image thereby allowing segmentation without the need for additional training. The image encoder generates embeddings and the lightweight decoder operates on the embeddings for point and mask based image segmentation.

This model is an implementation of MobileSam found here.

This repository provides scripts to run MobileSam on Qualcomm® devices. More details on model performance across various devices, can be found here.

Model Details

  • Model Type: Model_use_case.semantic_segmentation
  • Model Stats:
    • Model checkpoint: vit_t
    • Input resolution: 720p (720x1280)
    • Number of parameters (MobileSamDecoder): 3.876M
    • Model size (MobileSamDecoder): 19.6 MB
Model Precision Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Primary Compute Unit Target Model
SAMEncoder float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) TFLITE 805.961 ms 33 - 165 MB NPU MobileSam.tflite
SAMEncoder float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN 992.926 ms 12 - 22 MB NPU Use Export Script
SAMEncoder float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 595.924 ms 33 - 171 MB NPU MobileSam.tflite
SAMEncoder float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN 491.545 ms 12 - 591 MB NPU Use Export Script
SAMEncoder float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 432.572 ms 33 - 60 MB NPU MobileSam.tflite
SAMEncoder float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN 268.105 ms 12 - 14 MB NPU Use Export Script
SAMEncoder float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 420.623 ms 33 - 166 MB NPU MobileSam.tflite
SAMEncoder float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN 274.994 ms 1 - 15 MB NPU Use Export Script
SAMEncoder float SA7255P ADP Qualcomm® SA7255P TFLITE 805.961 ms 33 - 165 MB NPU MobileSam.tflite
SAMEncoder float SA7255P ADP Qualcomm® SA7255P QNN 992.926 ms 12 - 22 MB NPU Use Export Script
SAMEncoder float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 428.72 ms 33 - 67 MB NPU MobileSam.tflite
SAMEncoder float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN 269.684 ms 2 - 4 MB NPU Use Export Script
SAMEncoder float SA8295P ADP Qualcomm® SA8295P TFLITE 587.766 ms 28 - 163 MB NPU MobileSam.tflite
SAMEncoder float SA8295P ADP Qualcomm® SA8295P QNN 425.537 ms 0 - 18 MB NPU Use Export Script
SAMEncoder float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 436.151 ms 33 - 58 MB NPU MobileSam.tflite
SAMEncoder float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN 268.611 ms 4 - 6 MB NPU Use Export Script
SAMEncoder float SA8775P ADP Qualcomm® SA8775P TFLITE 420.623 ms 33 - 166 MB NPU MobileSam.tflite
SAMEncoder float SA8775P ADP Qualcomm® SA8775P QNN 274.994 ms 1 - 15 MB NPU Use Export Script
SAMEncoder float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile TFLITE 427.162 ms 33 - 60 MB NPU MobileSam.tflite
SAMEncoder float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN 268.541 ms 12 - 82 MB NPU Use Export Script
SAMEncoder float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile ONNX 385.43 ms 79 - 137 MB NPU MobileSam.onnx
SAMEncoder float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 339.111 ms 26 - 149 MB NPU MobileSam.tflite
SAMEncoder float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN 201.973 ms 12 - 619 MB NPU Use Export Script
SAMEncoder float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 298.164 ms 97 - 212 MB NPU MobileSam.onnx
SAMEncoder float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile TFLITE 237.268 ms 33 - 167 MB NPU MobileSam.tflite
SAMEncoder float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN 175.606 ms 12 - 599 MB NPU Use Export Script
SAMEncoder float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile ONNX 227.732 ms 78 - 207 MB NPU MobileSam.onnx
SAMEncoder float Snapdragon X Elite CRD Snapdragon® X Elite QNN 274.786 ms 12 - 12 MB NPU Use Export Script
SAMEncoder float Snapdragon X Elite CRD Snapdragon® X Elite ONNX 426.719 ms 131 - 131 MB NPU MobileSam.onnx
SAMDecoder float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) TFLITE 16.758 ms 0 - 39 MB NPU MobileSam.tflite
SAMDecoder float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN 13.94 ms 0 - 9 MB NPU Use Export Script
SAMDecoder float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 8.86 ms 0 - 40 MB NPU MobileSam.tflite
SAMDecoder float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN 8.795 ms 4 - 49 MB NPU Use Export Script
SAMDecoder float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 7.372 ms 0 - 29 MB NPU MobileSam.tflite
SAMDecoder float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN 6.219 ms 4 - 15 MB NPU Use Export Script
SAMDecoder float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 8.594 ms 0 - 42 MB NPU MobileSam.tflite
SAMDecoder float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN 7.344 ms 3 - 17 MB NPU Use Export Script
SAMDecoder float SA7255P ADP Qualcomm® SA7255P TFLITE 16.758 ms 0 - 39 MB NPU MobileSam.tflite
SAMDecoder float SA7255P ADP Qualcomm® SA7255P QNN 13.94 ms 0 - 9 MB NPU Use Export Script
SAMDecoder float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 7.425 ms 0 - 28 MB NPU MobileSam.tflite
SAMDecoder float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN 6.176 ms 2 - 4 MB NPU Use Export Script
SAMDecoder float SA8295P ADP Qualcomm® SA8295P TFLITE 9.892 ms 0 - 37 MB NPU MobileSam.tflite
SAMDecoder float SA8295P ADP Qualcomm® SA8295P QNN 7.428 ms 0 - 18 MB NPU Use Export Script
SAMDecoder float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 7.341 ms 0 - 32 MB NPU MobileSam.tflite
SAMDecoder float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN 6.159 ms 4 - 6 MB NPU Use Export Script
SAMDecoder float SA8775P ADP Qualcomm® SA8775P TFLITE 8.594 ms 0 - 42 MB NPU MobileSam.tflite
SAMDecoder float SA8775P ADP Qualcomm® SA8775P QNN 7.344 ms 3 - 17 MB NPU Use Export Script
SAMDecoder float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile TFLITE 7.4 ms 0 - 33 MB NPU MobileSam.tflite
SAMDecoder float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN 6.14 ms 4 - 23 MB NPU Use Export Script
SAMDecoder float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile ONNX 8.837 ms 1 - 66 MB NPU MobileSam.onnx
SAMDecoder float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 5.16 ms 0 - 49 MB NPU MobileSam.tflite
SAMDecoder float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN 4.148 ms 4 - 49 MB NPU Use Export Script
SAMDecoder float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 5.948 ms 6 - 72 MB NPU MobileSam.onnx
SAMDecoder float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile TFLITE 5.022 ms 0 - 42 MB NPU MobileSam.tflite
SAMDecoder float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN 3.123 ms 4 - 43 MB NPU Use Export Script
SAMDecoder float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile ONNX 5.458 ms 0 - 58 MB NPU MobileSam.onnx
SAMDecoder float Snapdragon X Elite CRD Snapdragon® X Elite QNN 6.669 ms 4 - 4 MB NPU Use Export Script
SAMDecoder float Snapdragon X Elite CRD Snapdragon® X Elite ONNX 10.0 ms 11 - 11 MB NPU MobileSam.onnx

Installation

Install the package via pip:

pip install "qai-hub-models[mobilesam]"

Configure Qualcomm® AI Hub to run this model on a cloud-hosted device

Sign-in to Qualcomm® AI Hub 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.

qai-hub configure --api_token API_TOKEN

Navigate to 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.

python -m qai_hub_models.models.mobilesam.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.mobilesam.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.
python -m qai_hub_models.models.mobilesam.export

Profiling Results
------------------------------------------------------------
SAMEncoder
Device                          : cs_8275 (ANDROID 14)                  
Runtime                         : TFLITE                                
Estimated inference time (ms)   : 806.0                                 
Estimated peak memory usage (MB): [33, 165]                             
Total # Ops                     : 592                                   
Compute Unit(s)                 : npu (532 ops) gpu (0 ops) cpu (60 ops)

------------------------------------------------------------
SAMDecoder
Device                          : cs_8275 (ANDROID 14)                 
Runtime                         : TFLITE                               
Estimated inference time (ms)   : 16.8                                 
Estimated peak memory usage (MB): [0, 39]                              
Total # Ops                     : 845                                  
Compute Unit(s)                 : npu (845 ops) gpu (0 ops) cpu (0 ops)

How does this work?

This export script leverages Qualcomm® AI Hub 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.

import torch

import qai_hub as hub
from qai_hub_models.models.mobilesam 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.

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.

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.

Run demo on a cloud-hosted device

You can also run the demo on-device.

python -m qai_hub_models.models.mobilesam.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.mobilesam.demo -- --on-device

Deploying compiled model to Android

The models can be deployed using multiple runtimes:

  • TensorFlow Lite (.tflite export): This tutorial provides a guide to deploy the .tflite model in an Android application.

  • QNN (.so export ): This sample app provides instructions on how to use the .so shared library in an Android application.

View on Qualcomm® AI Hub

Get more details on MobileSam's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

  • The license for the original implementation of MobileSam can be found here.
  • The license for the compiled assets for on-device deployment can be found here

References

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