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README.md

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 license: apache-2.0
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+
+# Introduction
+This repository hosts [PaddleOCR PP-DocLayoutV3](https://huggingface.co/PaddlePaddle/PP-DocLayoutV3_safetensors),
+an RT-DETR-based **document layout detector** (~33M params), for the
+[React Native ExecuTorch](https://www.npmjs.com/package/react-native-executorch) library,
+exported to `.pte` for the **ExecuTorch** runtime (XNNPACK, CoreML, Vulkan). It finds and
+classifies document regions — titles, paragraphs, tables, figures, formulas, headers/footers,
+etc. — and is a companion to
+[`react-native-executorch-paddleocr`](https://huggingface.co/software-mansion/react-native-executorch-PP-OCRv6).
+
+If you'd like to run these models in your own ExecuTorch runtime, refer to the
+[official documentation](https://pytorch.org/executorch/stable/index.html) for setup instructions.
+
+The `.pte` is a pure tensor→tensor function; all pre/post-processing (resize, normalize, score
+threshold, box convert) is the client's job.
+
+## Output contract
+
+A single static method `forward`, fixed input (no buckets):
+
+```
+in   [1, 3, 800, 800]          # RGB, ImageNet-normalized (x/255 - mean)/std
+out  logits     [1, 300, 25]   # 25 layout classes, per query (apply sigmoid)
+     pred_boxes [1, 300, 4]    # (cx, cy, w, h), normalized [0,1]
+```
+
+PP-DocLayoutV3 is a **DETR set-prediction** model → **no NMS**. Post-processing is just:
+`score = sigmoid(logits)`, keep queries above a threshold, convert `(cx,cy,w,h) → (x1,y1,x2,y2)`,
+scale to image size. Class names are in `labels.json` (index → label).
+
+### Classes (25)
+
+`abstract, algorithm, aside_text, chart, content, formula, doc_title, figure_title, footer,
+footnote, formula_number, header, image, number, paragraph_title, reference, reference_content,
+seal, table, text, vision_footnote` (some indices map to the same display label; use
+`labels.json` as the authoritative index→label map).
+
+## Backends, sizes & latency (warm)
+
+| backend | target | precision | size | latency |
+|---|---|---|---|---|
+| `xnnpack` | CPU | fp32 | 132 MB | ~2.0 s (S24) |
+| `coreml` | Apple ANE | fp16 | 91 MB | ANE fp16 |
+| `vulkan` | Android GPU | fp16 (mixed-delegate) | **66 MB** | **~0.86 s (S24)** |
+
+> **Vulkan is the recommended Android backend** — ~2.4× faster than XNNPACK and half the size.
+> It's mixed-delegate: most of RT-DETR runs fp16 on the GPU, while the box-head matmuls run on
+> XNNPACK (they delegate as `addmm`→`linear`). XNNPACK stays fp32 because RT-DETR's deformable
+> attention feeds non-contiguous tensors that int8/portable paths mis-handle.
+
+
+
+## Compatibility
+
+If you intend to use these models outside of React Native ExecuTorch, make sure your runtime is
+compatible with the **ExecuTorch** version used to export the `.pte` files. For more details, see
+the compatibility note in the
+[ExecuTorch GitHub repository](https://github.com/pytorch/executorch/blob/main/runtime/COMPATIBILITY.md).
+If you work with React Native ExecuTorch, the library constants guarantee compatibility with the
+runtime used behind the scenes.
+