README.md

---
license: apache-2.0
tags:
- executorch
- xnnpack
- pte
- on-device
- object-detection
- text-detection
---
# docTR DB-MobileNetV3-Large — ExecuTorch

Text **detection**: where the words are. The other half of the pair is
[CRNN-MobileNetV3-Small](https://huggingface.co/mlboydaisuke/docTR-CRNN-MobileNetV3-Small-ExecuTorch),
which reads them.

- **Source**: [mindee/doctr](https://github.com/mindee/doctr) `db_mobilenet_v3_large`,
  4.2M parameters
- **License**: Apache-2.0
- **Input**: `[1, 3, 1024, 1024]`, normalised with docTR's own mean (0.798, 0.785, 0.772)
  and std (0.264, 0.2749, 0.287) — not ImageNet's
- **Output**: `[1, 1, 1024, 1024]`, one probability per pixel of belonging to a word

Differentiable Binarization returns a *shrunk* region per word; turning the map into boxes
is docTR's `postprocessor`, which unshrinks the polygons. That stays on the caller, the way
the shelf's other detectors leave NMS outside.

## Verification (Mac arm64, 2026-08-23)

| build | size | latency | worst corr |
|---|---|---|---|
| XNNPACK fp32 | 16.1 MB | 46.9 ms | 1.000000 |
| Core ML fp32 | 8.6 MB | **13.1 ms** | 0.999714 |

Eager fp32 for the same input is 309 ms. Delegation is 100% (224/224 ops, one subgraph).

**Read end to end**, both `.pte` files through docTR's own post-processing, on a London
street photograph (`convert/check_doctr.py`):

```
boxes: 10
read: TIUZABXPRESS | Chrisiophers | Place | STREE! | LAG | BAR!
```

The signs in that photograph say PIZZA EXPRESS and Christopher's Place. Approximate, and
that is the point of quoting it: correlation against eager says the tensors match, and says
nothing about whether the map thresholds to any boxes at all.

## Not shipped, and why

- **int8**: worst corr **0.627**. It is 4.3 MB against 16.1, which is the trade this shelf
  would normally take, but not at that number.
- **fp16**: corr 0.999879 and it passes, but the file is the same 16.1 MB — the weights are
  convolutions XNNPACK already runs in fp32, so there is nothing to buy.

## The mistake worth repeating back

The harness gates on correlation against eager for **random** input. This file scored
1.000000 there and then produced a map that thresholded to zero boxes on a real photograph.
The cause was not the export:

```
                corr     pte max   pixels > 0.3
as built      0.024711    0.2385         0
.contiguous() 1.000000    0.5639      1816
```

`np.transpose` leaves the array in HWC order with permuted strides, and ExecuTorch reads a
tensor in memory order rather than by its strides. Random and all-zero inputs are
contiguous, so the gate never saw it. Anything built with `transpose` needs
`np.ascontiguousarray` before it reaches a `.pte`.

## Conversion

```bash
python convert/export_doctr.py detect
python convert/check_doctr.py <image>
```

docTR's `forward` runs its post-processing in numpy whether or not it was asked to, which
`torch.export` refuses (`.numpy() is not supported for tensor subclasses`). The model
carries an `exportable` flag that skips it and returns raw logits; the wrapper sets that and
applies the sigmoid itself.

(conversion scripts: [executorch-models](https://github.com/john-rocky/executorch-models))