---
license: mit
tags:
- executorch
- xnnpack
- pte
- on-device
- zero-shot-image-classification
base_model:
- openai/clip-vit-base-patch32
---
# clip_vit_b32_image — ExecuTorch

- **Source**: openai/clip-vit-base-patch32
- **License**: MIT
- **Input**: [[1, 3, 224, 224]] — RGB, CLIP norm (mean .481/.458/.408, std .269/.261/.276), 224x224
- **Output**: image embedding [1,512] (unnormalized; L2-normalize before cosine)

## Variants

All variants take and return fp32 tensors — swap the `.pte` file, keep your app code.

| build | file | size (MB) | parity vs fp32 eager (worst corr) | Mac median (ms)* |
|-----------|------|-----------|------------------------------------|------------------|
| fp32 | `clip_vit_b32_image_xnnpack_fp32.pte` | 351.6 | 1.000000 | 19.0 |
| fp16 | `clip_vit_b32_image_xnnpack_fp16.pte` | 180.7 | 0.999996 | 26.1 |
| int8 (dynamic) | `clip_vit_b32_image_xnnpack_int8.pte` | 95.9 | 0.995739 | 18.4 |
| Core ML (fp16, iOS) | `clip_vit_b32_image_coreml_all.pte` | 176.2 | 0.999998 | 3.5 |


The Core ML build is the same graph lowered to Apple's Neural Engine instead of
XNNPACK, which is CPU-only. Measured on an iPhone 17 Pro across seven models, it
runs **3.5x to 13.9x faster (median 12x)** at roughly half the file size — for
example Depth-Anything-V2-Small at 500.8 ms against 42.7 ms, and MODNet at 81.7 ms
against 5.9 ms. It computes in fp16 and is iOS-only; the XNNPACK files stay the
portable option and are what runs on Android.

\*Mac arm64, single process, median of 10 — a reference point for relative cost
only, not a device number (torch eager fp32 on the same machine: 18.5 ms).

### Checked in the task's own units

Correlation is a first filter. These are the numbers that decide:

- **int8 (dynamic)** — measured in the units that matter for this model — cosine similarity of the image embeddings: median 0.9988 over 10 real images, worst 0.9957.

## Verification (executorch 1.4.0, torch 2.13.0)

Parity is measured against the fp32 eager model on real image input; `corr` is
the correlation over all elements of each output tensor.

| output | shape | max_abs_diff | corr |
|--------|-------|--------------|------|
| 0 | [1, 512] | 9.179e-06 | 1.000000 |

XNNPACK delegate coverage (fp32): 69.3% (390/563 ops); ops left on the portable kernels: `aten.expand_copy.default` x49, `aten.native_layer_norm.default` x26, `aten.mul.Scalar` x24, `aten.logical_not.default` x24, `aten.eq.Scalar` x12, `aten.full_like.default` x12, `aten.any.dim` x12, `aten.where.self` x12, `aten.embedding.default` x1, `aten.select_copy.int` x1

## Conversion

torch.export -> to_edge_transform_and_lower(partitioner) -> .pte
(conversion scripts: [executorch-models](https://github.com/john-rocky/executorch-models))

This repo holds **both towers**: `clip_vit_b32_image_xnnpack_fp32.pte` (image) and
`clip_vit_b32_text_xnnpack_fp32.pte` (text, fixed len 77 + attention mask).
L2-normalize both embeddings, then cosine-match.

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---

**More models in this format:** [ExecuTorch Model Zoo](https://huggingface.co/collections/mlboydaisuke/executorch-model-zoo-6a7ff328390b63075ffeae5e) — 31 models, each with the recipe that produced it.

**Want a different model on-device?** [Open a request](https://github.com/john-rocky/on-device-requests) — free, open weights only; the export and its measured numbers get published publicly.

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