google/vit-base-patch16-224 optimized for Arm-based Edge Linux systems
An INT8-quantized version of google/vit-base-patch16-224 for image classification, exported to ExecuTorch (.pte) and optimized for Arm-based Edge Linux systems.
Summary
This repository contains an Arm-optimized version of google/vit-base-patch16-224 for image classification, quantized to INT8 via dynamic post-training quantization — per-channel symmetric weights, with activation scales computed dynamically at inference (no calibration data required). The model is provided in ExecuTorch (.pte) format, targeting Edge Linux 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 ImageNet-1k and measured performance on a representative evaluation target.
Key results
| Area | Result |
|---|---|
| Model format | ExecuTorch (.pte) |
| Target device class | Edge Linux |
| Reference device | Raspberry Pi 5 (Cortex-A76, Raspberry Pi OS 64-bit, based on Debian 13 "Trixie") |
| Primary performance result | p50 latency 378.139 ms (2.91x faster than baseline); 2.64 fps |
| Accuracy result | Top-1 81.50%, Top-5 96.06% |
| Size / memory result | 83.926 MB (3.94x smaller), peak memory 110.05 MB |
Original model
| Field | Value |
|---|---|
| Original model | google/vit-base-patch16-224 |
| Original source | Hugging Face |
| Original developer | Google Research |
| Original model card | google/vit-base-patch16-224 |
| Original license | Apache-2.0 |
Model files
| File | Description |
|---|---|
google__vit-base-patch16-224_raspberry_executorch_optimized.pte | 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 |
benchmarks/ | FP32 baseline and Arm-optimized benchmark records |
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
| Field | Value |
|---|---|
| Device / platform | Raspberry Pi 5 |
| CPU | Cortex-A76, 4 cores @ 2.4 GHz |
| System memory | 8 GB |
| OS | Raspberry Pi OS 64-bit, based on Debian 13 "Trixie" |
| Runtime | ExecuTorch 1.1.0 |
| Execution backend | CPU (XNNPACK, KleidiAI) |
| Precision | INT8, dynamic PTQ — per-channel symmetric weights; activations quantized dynamically at inference |
| Batch size | 1 |
| Input resolution | 224x224 |
| Runs / warmup | 10 / 10 |
Performance results
| Metric | Original / baseline | Arm-optimized | Improvement |
|---|---|---|---|
| Model size (MB) | 330.378 | 83.926 | 3.94x smaller |
| End-to-end latency p50 (ms) | 1101.028 | 378.139 | 2.91x faster |
| End-to-end latency p90 (ms) | 1102.087 | 379.857 | 2.90x faster |
| End-to-end latency p99 (ms) | 1104.819 | 388.308 | 2.84x faster |
| Model load time (ms) | 269.401 | 244.45 | 1.1x faster |
| Time to first inference (ms) | 1128.776 | 378.773 | 2.98x faster |
| Peak memory (MB) | 394.343 | 110.05 | 3.58x less |
| Average memory (MB) | 394.281 | 107.984 | 3.65x less |
| Frames per second | 0.908 | 2.64 | 2.90x |
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 | ExecuTorch 1.1.0 (CPU, XNNPACK, KleidiAI) |
Accuracy results
| Metric | Original / baseline | Arm-optimized | Change |
|---|---|---|---|
| Top-1 accuracy (%) | 81.52 | 81.50 | -0.02 pp |
| Top-5 accuracy (%) | 96.06 | 96.06 | 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 ExecuTorch .pte |
| Quantization | Yes | INT8 dynamic PTQ — per-channel symmetric weights; activation scales computed dynamically at inference, no calibration data required |
| Runtime/backend selection | Yes | XNNPACK + KleidiAI (CPU) |
| Graph/runtime compatibility updates | Yes | Performed as part of the ExecuTorch 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
Expected input
| Property | Value |
|---|---|
| Input shape | [1, 3, 224, 224] |
| Input type | float32 |
| Input range | [0.0, 1.0] |
| Preprocessing | resize shorter edge to 232 (bilinear), center-crop to 224x224, convert to tensor, normalize (mean [0.5, 0.5, 0.5], std [0.5, 0.5, 0.5]) |
Expected output
| Property | Value |
|---|---|
| Output shape | [1, 1000] |
| Output type | Raw class logits (unnormalized) |
| Postprocessing | softmax, top-5 |
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 50,000 images from the ImageNet-1k validation split and may not generalize to all domains.
- The model expects a fixed 224x224 input resolution — no dynamic input size support.
- This repository is not a replacement for the original model documentation.
Additional notes
- The export pipeline captures the FP32 model via torch.export, inserts dynamic-quantization observers, converts them to Q/DQ pairs, and exports the result to ExecuTorch (.pte) targeting the XNNPACK backend. All encoder Linear layers are quantized uniformly; none are kept in FP32.
- Sample input:
sample_input.jpgis derived from Samoyed on Beach by Appleinfl, via Wikimedia Commons (public domain).
About this version
Original Model: google/vit-base-patch16-224 by Google 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.