Arm / efficientvit-int8-xnnpack-executorch-raspberrypi5

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Total runs: 32
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7-day runs: 3
30-day runs: 3
Model's Last Updated: October 20 2026
image-classification

Introduction of efficientvit-int8-xnnpack-executorch-raspberrypi5

Model Details of efficientvit-int8-xnnpack-executorch-raspberrypi5

EfficientViT optimized for Arm-based Edge Linux

EfficientViT image classification model, optimized with INT8 quantization and exported as an ExecuTorch program for the XNNPACK CPU backend on Arm-based platforms.

This model was optimized using Arm AI Model Optimizer .

Summary

This repository contains an optimized version of EfficientViT for image classification. The model is provided as an ExecuTorch .pte file using the XNNPACK with KleidiAI delegate, targeting Edge Linux systems. Precision: INT8 weights (quantization details not recorded in metadata).

Key results

Area Result
Model format ExecuTorch (.pte)
Target device class Edge Linux
Reference device Raspberry Pi 5 (16GB) (Cortex-A76, 4 cores)
Primary performance result 1.24x lower p50 latency (26.01 ms to 20.90 ms)
Accuracy result Top-1 accuracy 78.68% (-0.68 pp vs. baseline)
Size / memory result 2.06x smaller model (37.39 MB to 18.12 MB), 31.69 MB peak memory
Original model
Field Value
Original model EfficientViT
Original source GitHub
Original developer MIT HAN Lab
Original model card EfficientViT
Original license Apache-2.0
Model files
File Description
model.pte Optimized INT8 ExecuTorch program for deployment
metadata.yaml Optimized model metadata
benchmarks/ Baseline and optimized benchmark records
Performance

Performance was measured on the reference configuration below. Results do not guarantee identical performance on every Arm-based system.

Reference configuration

Field Value
Device / platform Raspberry Pi 5 (16GB)
CPU / accelerator Cortex-A76, 4 cores, 2.4 GHz
OS Raspberry Pi OS Bookworm
Runtime ExecuTorch 1.1.0
Backend / delegate XNNPACK with KleidiAI, 4 threads
Batch size 1
Input resolution 224x224
Precision INT8 weights (quantization details not recorded in metadata)
Runs 200 measured, 20 warm-up

Performance results

Metric Original / baseline Optimized Improvement
p50 end-to-end latency 26.01 ms 20.90 ms 1.24x
Peak memory 50.92 MB 31.69 MB 1.61x less
Model size 37.39 MB 18.12 MB 2.06x smaller
Accuracy

Evaluation setup

Field Value
Dataset ImageNet-1k
Split val
Number of samples 5,000
Evaluation runtime ExecuTorch

Accuracy results

Metric Original / baseline Optimized Change
Top-1 accuracy 79.36% 78.68% -0.68 pp
Top-5 accuracy - 94.34% -

Users should re-evaluate the model on their own data before production use.

Optimization approach
Optimization area Applied? Notes
Model conversion Yes PyTorch model exported to ExecuTorch (.pte)
Quantization Yes INT8 weights (quantization details not recorded in metadata)
Runtime/backend selection Yes XNNPACK with KleidiAI, 4 threads
Accuracy validation Yes ImageNet-1k, 5000 samples
Performance validation Yes Measured on Raspberry Pi 5 (16GB)
Intended use

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

Limitations
  • Performance depends on the target device, runtime version, backend support, memory configuration, and system load.
  • Accuracy was measured on a 5,000-image subset of the ImageNet-1k validation set, not the full 50,000 images.
  • This repository is not a replacement for the original model documentation.
About this version

Original Model: EfficientViT by MIT HAN Lab - Repository

Optimization/conversion: INT8 quantization and ExecuTorch .pte export with XNNPACK + KleidiAI backend support for execution on Arm-based platforms.

Converted/optimized by: Nota AI, using Arm AI Model Optimizer

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.

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