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---
license: other
license_name: embedl-models-community-licence-1.0
license_link: https://github.com/embedl/embedl-models/blob/main/LICENSE
base_model:
  - apple/mobilevit-small
quantized_from:
  - apple/mobilevit-small
tags:
  - image-classification
  - quantization
  - onnx
  - tensorrt
  - edge
  - embedl
gated: true
extra_gated_heading: "Access Embedl Mobilevit Small"
extra_gated_description: "To access this model, please review and accept the terms below. Your contact information is collected solely to manage access and, with your explicit consent, to notify you about updated or new optimized models from Embedl."
extra_gated_button_content: "Agree and request access"
extra_gated_prompt: "By requesting access you agree to the Embedl Models Community Licence and the upstream Mobilevit Small License"
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# Embedl Mobilevit Small (Quantized for TensorRT)

Deployable INT8-quantized version of [`apple/mobilevit-small`](https://huggingface.co/apple/mobilevit-small),
optimized with [embedl-deploy](https://github.com/embedl/embedl-deploy)
for low-latency NVIDIA TensorRT inference on edge GPUs.

## Upstream Model

<a href="https://hfviewer.com/apple/mobilevit-small?utm_source=huggingface&amp;utm_medium=embedded_model_card&amp;utm_campaign=apple__mobilevit-small_card" target="_blank" rel="noopener">
  <img
    src="https://hfviewer.com/api/card.svg?source=apple%2Fmobilevit-small&amp;v=20260501clipcard"
    alt="Open apple/mobilevit-small in hfviewer"
    width="100%"
  />
</a>

## Highlights

- **Mixed-precision INT8/FP16 quantization** with hardware-aware
  optimizations from [embedl-deploy](https://github.com/embedl/embedl-deploy).
- **Drop-in replacement** for `apple/mobilevit-small` in TensorRT pipelines —
  same input shape (256×256), same output
  semantics.
- **Validated accuracy** within 3.30 pp of the FP32
  baseline on ImageNet (see Accuracy table below).
- **Quantization-aware training (QAT)** further recovers accuracy
  lost in INT8 conversion by fine-tuning the model with simulated
  quantization in the forward pass.
- **Matches the latency of `trtexec --best`** on supported NVIDIA
  hardware while preserving INT8 accuracy (see Performance table
  below).
- Includes both **ONNX** (for TensorRT) and **PT2**
  (`torch.export`-loadable) artifacts plus runnable inference scripts.

## Quick Start

```bash
pip install huggingface_hub onnxruntime-gpu pillow numpy
python -c "from huggingface_hub import snapshot_download; snapshot_download('embedl/mobilevit-small-quantized', local_dir='.')"
python infer_trt.py --image path/to/image.jpg   # TensorRT
# or
python infer_pt2.py --image path/to/image.jpg   # pure PyTorch via torch.export
```

## Files

| File | Purpose |
|---|---|
| `embedl_mobilevit_small_int8.onnx` | INT8-quantized ONNX with Q/DQ nodes — feed to TensorRT. |
| `embedl_mobilevit_small_int8.pt2` | INT8-quantized `torch.export` ExportedProgram. |
| `infer_trt.py` | Build a TRT engine from the ONNX and run sample inference. |
| `infer_pt2.py` | Load the `.pt2` with `torch.export.load` and run sample inference. |

## Performance

Latency measured with TensorRT + `trtexec`, GPU compute time only
(`--noDataTransfers`), CUDA Graph + Spin Wait enabled, clocks locked
(`nvpmodel -m 0 && jetson_clocks` on Jetson).

<img src="https://huggingface.co/datasets/embedl/documentation-images/resolve/main/mobilevit-small-quantized/mobilevit-small-quantized__orin-mountain-view.svg" alt="MobileViT-Small benchmark on NVIDIA Jetson AGX Orin">

### NVIDIA Jetson AGX Orin

| Configuration | Mean Latency | Speedup vs FP16 |
|---|---|---|
| TensorRT FP16 | 1.28 ms | 1.00x |
| TensorRT --best (unconstrained) | 1.09 ms | 1.17x |
| **Embedl Deploy INT8** | **1.09 ms** | **1.17x** |


## Accuracy

Evaluated on the ImageNet validation split. The quantized model
retains nearly all of the FP32 accuracy with a small tolerance.

| Model | Top-1 | Top-5 |
|---|---|---|
| `apple/mobilevit-small` FP32 (ours) | 78.14% | 94.08% |
| **Embedl Mobilevit Small INT8** | **74.83%** | **92.28%** |

## Creating Your Own Optimized Models

This artifact was produced with
[embedl-deploy](https://github.com/embedl/embedl-deploy),
Embedl's open-source PyTorch → TensorRT deployment library. You can
apply the same workflow to your own models — see
[the documentation](https://github.com/embedl/embedl-deploy#readme)
for installation and usage.

## License

| Component | License |
|---|---|
| Optimized model artifacts (this repo) | [Embedl Models Community Licence v1.0](https://github.com/embedl/embedl-models/blob/main/LICENSE) — no redistribution as a hosted service |
| Upstream architecture and weights | [Mobilevit Small License](https://huggingface.co/apple/mobilevit-small) |

## Contact

We offer engineering support for on-prem/edge deployments and partner
co-marketing opportunities. Reach out at
[contact@embedl.com](mailto:contact@embedl.com), or open an issue on
[GitHub](https://github.com/embedl/embedl-deploy).

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