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README.md

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+---
+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))