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Segment-Anything-Model-2

Image Segmentation
PyTorch
LiteRT
foundation
android
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Segment-Anything-Model-2: Optimized for Mobile Deployment

High-quality segmentation in images and videos with real-time performance and minimal user interaction

SAM 2, the successor to Meta's Segment Anything Model (SAM), is a cutting-edge tool designed for comprehensive object segmentation in both images and videos. It excels in handling complex visual data through a unified, promptable model architecture that supports real-time processing and zero-shot generalization.

This model is an implementation of Segment-Anything-Model-2 found here.

This repository provides scripts to run Segment-Anything-Model-2 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: sam2.1_hiera_t
    • Input resolution: 720p (720x1280)
    • Number of parameters (SAM2Encoder): 33.5M
    • Model size (SAM2Encoder) (float): 128 MB
    • Number of parameters (SAM2Decoder): 6.22M
    • Model size (SAM2Decoder) (float): 23.7 MB
Model Precision Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Primary Compute Unit Target Model
SAM2Encoder float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) TFLITE 609.767 ms 92 - 296 MB NPU Segment-Anything-Model-2.tflite
SAM2Encoder float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 367.288 ms 85 - 296 MB NPU Segment-Anything-Model-2.tflite
SAM2Encoder float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN_DLC 384.843 ms 12 - 2403 MB NPU Segment-Anything-Model-2.dlc
SAM2Encoder float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 252.662 ms 92 - 117 MB NPU Segment-Anything-Model-2.tflite
SAM2Encoder float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN_DLC 212.081 ms 15 - 86 MB NPU Segment-Anything-Model-2.dlc
SAM2Encoder float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 290.379 ms 92 - 295 MB NPU Segment-Anything-Model-2.tflite
SAM2Encoder float SA7255P ADP Qualcomm® SA7255P TFLITE 609.767 ms 92 - 296 MB NPU Segment-Anything-Model-2.tflite
SAM2Encoder float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 259.454 ms 92 - 117 MB NPU Segment-Anything-Model-2.tflite
SAM2Encoder float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN_DLC 214.113 ms 12 - 93 MB NPU Segment-Anything-Model-2.dlc
SAM2Encoder float SA8295P ADP Qualcomm® SA8295P TFLITE 380.286 ms 92 - 307 MB NPU Segment-Anything-Model-2.tflite
SAM2Encoder float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 263.816 ms 92 - 117 MB NPU Segment-Anything-Model-2.tflite
SAM2Encoder float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN_DLC 214.315 ms 12 - 89 MB NPU Segment-Anything-Model-2.dlc
SAM2Encoder float SA8775P ADP Qualcomm® SA8775P TFLITE 290.379 ms 92 - 295 MB NPU Segment-Anything-Model-2.tflite
SAM2Encoder float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile TFLITE 265.703 ms 92 - 115 MB NPU Segment-Anything-Model-2.tflite
SAM2Encoder float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN_DLC 215.128 ms 12 - 87 MB NPU Segment-Anything-Model-2.dlc
SAM2Encoder float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 191.967 ms 92 - 308 MB NPU Segment-Anything-Model-2.tflite
SAM2Encoder float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile TFLITE 178.194 ms 90 - 298 MB NPU Segment-Anything-Model-2.tflite
SAM2Encoder float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN_DLC 139.549 ms 12 - 1333 MB NPU Segment-Anything-Model-2.dlc
SAM2Encoder float Snapdragon X Elite CRD Snapdragon® X Elite QNN_DLC 260.348 ms 800 - 800 MB NPU Segment-Anything-Model-2.dlc
SAM2Decoder float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) TFLITE 17.97 ms 0 - 54 MB NPU Segment-Anything-Model-2.tflite
SAM2Decoder float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 10.167 ms 0 - 57 MB NPU Segment-Anything-Model-2.tflite
SAM2Decoder float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN_DLC 11.324 ms 15 - 76 MB NPU Segment-Anything-Model-2.dlc
SAM2Decoder float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 8.133 ms 0 - 30 MB NPU Segment-Anything-Model-2.tflite
SAM2Decoder float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN_DLC 6.962 ms 16 - 35 MB NPU Segment-Anything-Model-2.dlc
SAM2Decoder float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 9.556 ms 0 - 55 MB NPU Segment-Anything-Model-2.tflite
SAM2Decoder float SA7255P ADP Qualcomm® SA7255P TFLITE 17.97 ms 0 - 54 MB NPU Segment-Anything-Model-2.tflite
SAM2Decoder float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 8.173 ms 0 - 33 MB NPU Segment-Anything-Model-2.tflite
SAM2Decoder float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN_DLC 6.96 ms 16 - 39 MB NPU Segment-Anything-Model-2.dlc
SAM2Decoder float SA8295P ADP Qualcomm® SA8295P TFLITE 11.752 ms 0 - 52 MB NPU Segment-Anything-Model-2.tflite
SAM2Decoder float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 8.166 ms 0 - 32 MB NPU Segment-Anything-Model-2.tflite
SAM2Decoder float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN_DLC 6.987 ms 16 - 37 MB NPU Segment-Anything-Model-2.dlc
SAM2Decoder float SA8775P ADP Qualcomm® SA8775P TFLITE 9.556 ms 0 - 55 MB NPU Segment-Anything-Model-2.tflite
SAM2Decoder float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile TFLITE 8.209 ms 0 - 33 MB NPU Segment-Anything-Model-2.tflite
SAM2Decoder float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN_DLC 6.982 ms 15 - 38 MB NPU Segment-Anything-Model-2.dlc
SAM2Decoder float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 5.59 ms 0 - 65 MB NPU Segment-Anything-Model-2.tflite
SAM2Decoder float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile TFLITE 5.651 ms 0 - 56 MB NPU Segment-Anything-Model-2.tflite
SAM2Decoder float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN_DLC 4.238 ms 8 - 62 MB NPU Segment-Anything-Model-2.dlc
SAM2Decoder float Snapdragon X Elite CRD Snapdragon® X Elite QNN_DLC 7.652 ms 16 - 16 MB NPU Segment-Anything-Model-2.dlc

Installation

Install the package via pip:

pip install "qai-hub-models[sam2]"

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.sam2.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.sam2.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.sam2.export

Profiling Results
------------------------------------------------------------
SAM2Encoder
Device                          : cs_8275 (ANDROID 14)                  
Runtime                         : TFLITE                                
Estimated inference time (ms)   : 609.8                                 
Estimated peak memory usage (MB): [92, 296]                             
Total # Ops                     : 647                                   
Compute Unit(s)                 : npu (593 ops) gpu (0 ops) cpu (54 ops)

------------------------------------------------------------
SAM2Decoder
Device                          : cs_8275 (ANDROID 14)                 
Runtime                         : TFLITE                               
Estimated inference time (ms)   : 18.0                                 
Estimated peak memory usage (MB): [0, 54]                              
Total # Ops                     : 889                                  
Compute Unit(s)                 : npu (889 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.sam2 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.sam2.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.sam2.demo -- --eval-mode 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 Segment-Anything-Model-2's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

  • The license for the original implementation of Segment-Anything-Model-2 can be found here.
  • The license for the compiled assets for on-device deployment can be found here

References

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