Arm/swin-tiny-int8-litert

Swin-Tiny Patch4-Window7-224 optimized for Arm-based mobile CPUs with SME2

Swin-Tiny Patch4-Window7-224 optimized with INT8 dynamic post-training quantization for image classification, exported to LiteRT .tflite format for efficient inference on Arm-based mobile CPUs with SME2.

Summary

This repository contains an Arm-optimized version of microsoft/swin-tiny-patch4-window7-224 for image classification, quantized to INT8 via dynamic post-training quantization — per-channel symmetric INT8 weights, with activations quantized to INT8 dynamically at inference. The model is provided in LiteRT .tflite format, targeting Mobile CPU systems.

Swin-Tiny is a hierarchical vision transformer that applies self-attention within shifted local windows, which keeps compute linear in image size. It accepts 224x224 RGB inputs and produces logits for 1,000 ImageNet-1k classes.

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 ImageNet-1k and measured performance on a representative evaluation target.

Key results

Area	Result
Model format	LiteRT .tflite
Target device class	Mobile CPU
Reference device	vivo X300 (C1-Ultra, C1-Premium, C1-Pro, Android 16 / OriginOS 6)
Example environment	AWS Graviton, Ubuntu arm64
Primary performance result	p50 latency of 37.65 ms at 1 thread — 0.80x the FP32 baseline, i.e. slower (see Limitations)
Accuracy result	Top-1 accuracy of 81.12 percent (+0.03 pp vs baseline)
Size / memory result	30.20 MB (3.66x smaller) and 86.35 MB peak memory (2.85x less)

Original model

Field	Value
Original model	microsoft/swin-tiny-patch4-window7-224
Original source	Hugging Face
Original developer	Microsoft
Original model card	microsoft/swin-tiny-patch4-window7-224
Original license	Apache-2.0

Model files

File	Description
microsoft__swin-tiny-patch4-window7-224_android_litert_optimized.tflite	Arm-optimized INT8 model for deployment
example.py	Minimal inference example
pyproject.toml	Pinned runtime dependencies for example.py, resolved with uv
uv.lock	Locked dependency resolution for pyproject.toml
config.yaml	Model I/O contract used by the example
sample_input.jpg	Sample image used by the example
benchmarks/	FP32 baseline and Arm-optimized benchmark records

Performance

Performance was measured on the vivo X300 reference configuration below. The Graviton environment documented for example.py verifies portability of the inference flow but does not provide or imply equivalent Graviton performance.

Reference configuration

Field	Value
Device / platform	vivo X300
CPU / accelerator	C1-Ultra, C1-Premium, C1-Pro (8-core aarch64)
OS	android, Android 16 / OriginOS 6
Runtime	LiteRT
Backend / delegate	XNNPACK, KleidiAI
Batch size	1
Threads	1
Precision	INT8 dynamic PTQ — per-channel symmetric weights, activations quantized to INT8 dynamically at inference
Runs	100 measured runs, 20 warmup runs

Performance results

Metric	Original / baseline	Arm-optimized	Improvement
p50 latency	30.00 ms	37.65 ms	0.80x
p90 latency	31.81 ms	41.03 ms	0.78x
Model load time	166.76 ms	94.44 ms	1.77x faster
Time to first inference	37.81 ms	45.44 ms	0.83x
Frames per second	33.33 FPS	26.56 FPS	0.80x
Model size	110.39 MB	30.20 MB	3.66x smaller
Peak memory	246.31 MB	86.35 MB	2.85x less

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

Field	Value
Dataset	ImageNet-1k
Split	val
Number of samples	50000
Metric(s)	Top-1 accuracy, Top-5 accuracy
Evaluation runtime	LiteRT

Accuracy results

Metric	Original / baseline	Arm-optimized	Change
Top-1 accuracy	81.09%	81.12%	+0.03 pp
Top-5 accuracy	95.54%	95.54%	+0.00 pp

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:

Optimization area	Applied?	Notes
Model conversion	Yes	Converted to LiteRT .tflite
Quantization	Yes	Dynamic PTQ with INT8 per-channel symmetric weights; activations are quantized to INT8 dynamically at inference, so no calibration data is required
Runtime/backend selection	Yes	Selected XNNPACK and KleidiAI
Graph/runtime compatibility updates	Yes	Performed as part of the LiteRT export pipeline
Accuracy validation	Yes	Compared against the original model or published baseline
Performance validation	Yes	Measured on the reference Arm platform

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

Dependencies are declared in pyproject.toml, which ships with this repository. Resolve and install them into a local virtual environment with uv:

uv python install
uv sync --frozen

Run the example

uv run example.py

The example loads the optimized .tflite weight and sample_input.jpg from its own directory, prints the top-5 predictions, and writes predictions.json and sample_output.jpg beside itself.

Note: The Python/uv example runs on AWS Graviton (Ubuntu arm64) to confirm runtime compatibility only, and is intended as a guideline for building an equivalent run script on Mobile CPU systems.

Expected input

Property	Value
Input shape	1x3x224x224
Input type	float32
Input range	0.0 to 1.0
Preprocessing	Resize shorter edge to 232 (bilinear), center crop to 224x224, convert to tensor, normalize with ImageNet mean/std

Expected output

Property	Value
Output shape	1x1000
Output type	Raw classification logits (unnormalized), one score per ImageNet-1k class
Postprocessing	Softmax followed by top-5 selection

Intended use

This model is intended for developers evaluating image classification 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 ImageNet-1k val 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.
• The export is fixed at 224x224 input resolution, and it targets the CPU backend.
• Performance was measured on a vivo X300; other Arm devices may run slower or faster and may have different XNNPACK or KleidiAI delegation coverage.

Additional notes

• Sample input: sample_input.jpg is derived from Samoyed on Beach by Appleinfl, via Wikimedia Commons (public domain).

About this version

Original Model: microsoft/swin-tiny-patch4-window7-224 by Microsoft - 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.