efficient-sam-s-int8-xnnpack-executorch

EfficientSAM-S optimized for Arm-based Cloud CPU

EfficientSAM-S box-prompted image segmentation optimized as an INT8 ExecuTorch .pte model for Arm-based Cloud CPU systems.

Summary

This repository contains an Arm-optimized version of EfficientSAM-S for image segmentation. The model is provided in ExecuTorch .pte (ExecuTorch runtime), targeting Cloud CPU systems.

This version is intended to demonstrate efficient inference on Arm-based platforms while preserving the original model's intended behavior. Arm has evaluated this model on COCO 2017 and measured performance on a representative evaluation target.

Key results

Original model

Model files

Performance

Performance was measured on the reference configuration below. Results are intended to make the optimization reproducible but do not guarantee identical performance on every Arm-based system.

Reference configuration

Performance results

Accuracy

Accuracy was evaluated using the same preprocessing, input resolution, and evaluation protocol described below. Where possible, the optimized model is compared against the original model under the same evaluation conditions.

Evaluation setup

Accuracy results

Accuracy was measured using the evaluation setup described above. Users should re-evaluate the model on their own data before production use.

Arm optimization approach

Arm optimized this model for efficient inference on Arm-based platforms using a hardware-aware conversion and validation flow.

For this release, Arm used:

The goal of this process is to improve deployment characteristics such as latency, memory use, model size, and runtime compatibility while preserving the model's intended behavior. Detailed conversion scripts, calibration configuration, or backend-specific implementation details may be provided separately where appropriate.

Using this model

Install dependencies

The environment was validated on Ubuntu 24.04, AArch64, with glibc 2.39 and no additional Apt packages. The model weight was verified from Arm/efficient-sam-s-int8-xnnpack-executorch at revision 2a5a917a6e6d37e9e2bb69256a574d5d7ad17d79.

This directory ships a uv-locked environment (pyproject.toml, .python-version, uv.lock). Install uv, then:

uv python install
uv sync --frozen

Run the example

uv run example.py

The ordinary run uses sample_input.jpg and the default box prompt 410 510 600 830. It prints a finite summary without changing repository files. To generate and compare the expected results:

uv run example.py --output-dir /tmp/efficient-sam-output
cmp /tmp/efficient-sam-output/sample_output.png sample_output.png
cmp /tmp/efficient-sam-output/segmentation.json segmentation.json

To run segmentation on another image, pass --image and provide --box in the resized 1024x1024 coordinate space. The box is specified as X1 Y1 X2 Y2:

uv run example.py \
  --image /path/to/image.jpg \
  --box 410 510 600 830 \
  --output-dir /tmp/efficient-sam-output

The example stretches the input image to 1024x1024; scale box coordinates from the original image before passing --box. sample_output.png contains the mask overlay and box prompt at the original input image resolution and aspect ratio; segmentation.json records the box and mask coverage.

Runtime notices

ExecuTorch may report that optional TorchAO C++ extensions are incompatible with the locked Torch version, internal-consistency verification is unavailable, and optional CPU identification files cannot be read. These notices appeared during validation but did not affect XNNPACK inference or the byte-identical expected results.

Expected input

Expected output

Intended use

This model is intended for developers evaluating image-segmentation workloads on Arm-based platforms. It is suitable as a reference implementation for benchmarking, prototyping, and integration exploration.

Limitations

• Performance depends on the target device, runtime version, backend/delegate support, memory configuration, and system load.
• Accuracy was evaluated on COCO 2017 val2017 and may not generalize to all domains.
• This release preserves the original model's intended task and behavior, but users should validate it for their own application, data, and deployment environment.
• This repository is not a replacement for the original model documentation.

Additional notes

• Quantization recipe: PT2E dynamic INT8 via XNNPACKQuantizer, symmetric per-channel weights, no layers kept in FP32. Dynamic activation quantization was chosen deliberately.
• Transformer attention and layer norm fall back to the CPU portable kernel path, which bounds the achievable speedup.
• Requires Python 3.13.
• Sample input: sample_input.jpg is derived from Living room (Unsplash) by Jarosław Ceborski, via Wikimedia Commons (CC0 1.0).

About this version

Original Model: efficient_sam_s by Yunyang Xiong et al. (Meta AI Research) - Repository

Optimization/conversion: Arm-Optimized version for execution on Arm-based platforms.

Converted/optimized by: Arm

License: The Original Model and the Optimized Model are subject to Apache-2.0.

This repository contains a converted or optimized version of the Original Model (the “Optimized Model”). The Original Model has been converted or optimized as described above for execution on Arm-based platforms.

No retraining or fine-tuning of the Original Model was performed as part of the conversion or optimization. The conversion or optimization was not intended to change the Original Model’s behavior or intended use.

Original Model and Documentation

For information about the Original Model, including its development, training data, intended uses, limitations and other relevant information, please refer to the Original Model repository. Information in that repository was provided by the original developer or other third parties and, unless expressly stated otherwise, has not been independently verified by Arm.

Licenses and Third-Party Terms

Use of the Original Model and the Optimized Model is subject to the applicable licenses, usage restrictions and other terms identified above and in the relevant repositories. Publication of the Optimized Model does not grant any rights beyond those provided under the applicable license terms.

You are responsible for reviewing those terms and ensuring that your use of the Original Model and the Optimized Model is permitted.

Purpose of this Release

The Optimized Model is provided as a reference implementation to demonstrate and evaluate execution and performance on Arm-based systems. It is not a production-ready or supported solution.

Arm’s publication of the Optimized Model does not constitute an endorsement or certification of the Original Model or a representation that the Optimized Model is suitable for production use or any particular purpose.

To the fullest extent permitted by applicable law (i) the Optimized Model is provided “as is.” Arm makes no representations or warranties that the Original Model, the Optimized Model or their outputs are accurate, safe, secure, non-infringing, legally compliant, suitable for production use or fit for any particular purpose; and (ii) Arm will not be liable for any loss or damage arising from or in connection with the Optimized Model, its use or its outputs.

You are responsible for independently evaluating the Optimized Model, its outputs and its suitability for your intended use, including compliance with applicable legal, regulatory, safety and security requirements.

Arm does not commit to provide ongoing support, maintenance or updates for the Optimized Model. Any use of or reliance on the Optimized Model or its outputs is at your own risk.