PP-DocLayout-S — ONNX export
ONNX export of PaddlePaddle/PP-DocLayout-S, a lightweight (~4.7 MB) document-layout detector based on PicoDet/GFL, trained on a mixed Chinese/English corpus of papers, magazines, contracts, books, exams and research reports. 23 layout classes.
Hosted here for the RailReaderCore project — the detector intended for future web (WASM/ORT-Web) and mobile RailReader builds where the ~50 MB PP-DocLayoutV3 is too heavy.
Files
| File | Size | Notes |
|---|---|---|
pp_doclayout_s.onnx |
4.7 MB | Exported via paddle2onnx from the upstream Paddle checkpoint. |
Inference contract
The export keeps PaddleDetection's GFL-head two-input convention, not the three-input convention used by the PP-DocLayoutV3 RT-DETR export — so this is not a drop-in replacement at the I/O level.
Inputs
| Name | dtype | Shape | Notes |
|---|---|---|---|
image |
float32 |
[1, 3, 480, 480] |
Bilinear-resize the source image to exactly 480×480 without keep-ratio, scale to [0, 1], then ImageNet-normalise: mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]. |
scale_factor |
float32 |
[1, 2] |
[480 / orig_h, 480 / orig_w] — the detection head divides predicted boxes by this, so the output boxes come back in the original image's pixel space. |
Outputs
| Name | dtype | Shape | Notes |
|---|---|---|---|
| Detections | float32 |
[M, 6] |
Padded out to a fixed M. Each row is [class_id, score, x1, y1, x2, y2]. Coordinates are in the original image (un-resized) frame. |
num_dets |
int32/int64 |
scalar | Number of valid rows in the detection tensor. Trust this — the rest of the rows are padding. |
NMS is baked into the graph at score_threshold=0.3.
Class table (23)
Ordered to match the upstream inference.yml:
0 paragraph_title
1 image
2 text
3 number
4 abstract
5 content
6 figure_title
7 formula
8 table
9 table_title
10 reference
11 doc_title
12 footnote
13 header
14 algorithm
15 footer
16 seal
17 chart_title
18 chart
19 formula_number
20 header_image
21 footer_image
22 aside_text
Minimal usage (ONNX Runtime, Python)
import numpy as np
import onnxruntime as ort
from PIL import Image
MEAN = np.array([0.485, 0.456, 0.406], dtype=np.float32)
STD = np.array([0.229, 0.224, 0.225], dtype=np.float32)
sess = ort.InferenceSession("pp_doclayout_s.onnx", providers=["CPUExecutionProvider"])
img = Image.open("page.png").convert("RGB")
orig_w, orig_h = img.size
resized = img.resize((480, 480), Image.BILINEAR)
arr = (np.asarray(resized, dtype=np.float32) / 255.0 - MEAN) / STD
chw = np.transpose(arr, (2, 0, 1))[None] # [1, 3, 480, 480]
sf = np.array([[480 / orig_h, 480 / orig_w]], dtype=np.float32) # [1, 2]
dets, num_dets = sess.run(None, {"image": chw, "scale_factor": sf})
for row in dets[: int(num_dets[0])]:
cls_id, score, x1, y1, x2, y2 = row
if score < 0.3:
continue
print(int(cls_id), float(score), float(x1), float(y1), float(x2), float(y2))
License
Apache-2.0, inherited from the upstream PaddlePaddle/PP-DocLayout-S.
Source verification
sha256: 33688dbee1c23e34b81777e97cb428eb40f24b242c02b5f623484959e830aec8
size: 4917852 bytes
Model tree for stefanj0/PP-DocLayout-S-ONNX
Base model
PaddlePaddle/PP-DocLayout-S