| --- |
| 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)) |
|
|
