ONNX export of PaddlePaddle/PP-DocLayoutV3_safetensors,
a DETR-style document layout detection model. Given a document page image, it
predicts per-region bounding boxes, layout class, reading order, and
(optionally) segmentation polygons for 25 layout element types
(title, text, table, figure, formula, header/footer, reference, seal, ...).
This repo ships the traced ONNX graph only — inference needs ONNX Runtime +
NumPy + OpenCV, no PyTorch or transformers required at serve time. The
export script and reference pre/post-processing code (pp_doclayout_v3_onnx.py)
are included in this repo for convenience — see below.
Files
File
Description
pp_doclayoutv3.onnx
Full graph, includes the mask head (out_masks) for polygon output
pp_doclayoutv3_nomask.onnx
Same graph without out_masks — smaller, no ~48 MB/image mask tensor; polygons degrade to axis-aligned boxes
pp_doclayoutv3_fp16.onnx
Half-precision copy of the graph above it (normalization/mask ops kept in fp32)
labels.json
id2label mapping used to decode logits
Only the variants actually present in this repo were exported — see the
file list on the repo page for what's available.
Model I/O
Input
Name
Shape
Notes
pixel_values
(B, 3, 800, 800) float32
RGB, resized to a fixed 800×800 square (bicubic), scaled to [0, 1]. No mean/std normalization (mean=0, std=1). Batch dim is dynamic.
Output
Name
Shape
Notes
logits
(B, 300, 25)
Per-query class scores (sigmoid, not softmax)
pred_boxes
(B, 300, 4)
cxcywh, normalized to [0, 1]
order_logits
(B, 300, 300)
Reading-order pointer matrix
out_masks(optional)
(B, 300, 200, 200)
Mask logits at stride 4 (input_size / 4)
300 object queries, no NMS — box selection is done by top-k over the
flattened (query, class) score grid and thresholding, matching the
original PaddlePaddle/HF post-processing.
Usage
python
1import numpy as np
2import onnxruntime as ort
34session = ort.InferenceSession("pp_doclayoutv3.onnx", providers=["CPUExecutionProvider"])5pixel_values = np.random.rand(1,3,800,800).astype(np.float32)# preprocess your image to this6logits, pred_boxes, order_logits, out_masks = session.run(None,{"pixel_values": pixel_values})
Decoding raw outputs into boxes/labels/reading-order/polygons requires the
post-processing logic ported from PPDocLayoutV3ImageProcessor (sigmoid
scoring, top-k selection, cxcywh→xyxy rescaling, reading-order pointer
resolution, mask→polygon extraction). The reference implementation is
pp_doclayout_v3_onnx.py in the source repo — a self-contained
PPDocLayoutV3ONNX class with no torch/transformers dependency:
python
1from pp_doclayout_v3_onnx import PPDocLayoutV3ONNX
23det = PPDocLayoutV3ONNX("pp_doclayoutv3.onnx", device="cpu")# or "cuda" / "tensorrt"4for r in det.predict("page.jpg"):5print(r["order"], r["label"], r["score"], r["box"])
Examples
Served with serve_pp_doclayout_v3.py (TensorRT/CUDA EP, threshold=0.4, masks on)
against dense scientific-article pages from the CDLA-Permissive-1.0-licensed
creative-graphic-design/PubLayNet
dataset (PubMed Central open-access articles), selected for high layout-element
count out of a scan of the train split — see fetch_example_images.py. Boxes
below are colored by predicted label, tagged {reading_order}:{label} {score}.
Full detections (all 25 classes, boxes, polygons, reading order) are in the
linked JSON.
Input → detections
Elements
Labels detected
JSON
PMC5883225_00001
38
chart, figure_title, formula, header, number, paragraph_title, text
disable_custom_kernels=True — the custom CUDA deformable-attention kernel
has no ONNX symbolic, so export uses the pure-PyTorch (grid_sample) path instead.
The upstream 2D sin/cos position embedding is computed in float64 upstream;
ONNX Runtime's CPU EP has no double kernel for Cos, so it's patched to
float32 during tracing (diff ~1e-6, otherwise the exported graph fails to load).
Verified against the PyTorch reference with a parity check
(max|diff| < 1e-3 per output tensor) using the real pretrained weights.
Intended for document layout analysis in document-AI / IDP pipelines
(reading-order extraction, region cropping, downstream OCR routing).
Inherits the training data, biases, and limitations of the base
PaddlePaddle/PP-DocLayoutV3_safetensors checkpoint — this repo changes
only the runtime format, not the weights or decision boundary.
Fixed 800×800 input: very small text regions or extreme aspect-ratio pages
may lose detail relative to their original resolution.
Not evaluated here beyond output-tensor parity with the PyTorch model —
refer to the base model card for accuracy/benchmark numbers.
License
Apache 2.0, inherited from the base model. Verify current license terms on
the base model card
before redistribution.
Citation
Please cite the original PP-DocLayoutV3 / PaddleOCR work if you use this model:
