AutomatosX/AXDocLayout-M-v1.4-CUDA-TF32

AX-DocLayout-M v1.4 - H5 CUDA / cuDNN TF32

This is the owner-selected H5 final EMA model, fine-tuned from Docling Heron. It detects page layout from decoded RGB rasters and maps17native classes to AX PDF SDK IDs1-17, taxonomy ax-document-unified-25/1. This repository is a separate Heron-derived model identity. It is not D6, not the old11-class v1.0 model, and not the ax-doclayout Python package v1.4.0.

The owner selected H5 after qualitative comparison with D5 and D6 using a150-page HKEX-oriented review packet. The number of pages actually inspected and structured ratings were not recorded. This selection does not mean H5 wins every benchmark.

Exact artifact and runtime

• Parent: docling-project/docling-layout-heron at 8f39ad3c0b4c58e9c2d2c84a38465abf757272d8.
• H1:2048updates; H5:1024updates from finalH1EMA.42,761,907parameters.
• Weight SHA256: a00ac115f20a9b00ababec445c526771e0d6dcf8ad41cad30049bcaa40fc0ec8.
• FP32 safetensors and activations; no quantization, autocast or FP16 conversion.
• Native 640x640 direct resize, original pinned image processor. Do not apply D5's1024letterbox preprocessing to this model.
• Qualified CUDA settings: cuDNN TF32 allowed, CUDA matmul TF32 disabled. The repository suffix does not mean all matrix multiplications use TF32. TF32 is an allowed arithmetic mode, not a different weight file format.
• Existing GPU evidence: PyTorch2.12.1+cu129, CUDA12.9, cuDNN92000, Transformers4.53.3, RTX PRO6000Blackwell. See runtime-profile.json.
• The standalone example is newly packaged and CPU-tested; it was not freshly rerun on CUDA for this publication. The underlying exact H5 checkpoint was evaluated on CUDA and its exported tensors were verified exactly after reload.

The independent controls for matmul and cuDNN are documented by PyTorch2.12. An all-matmul-TF32, BF16, FP16, ONNX or TensorRT variant is not qualified here.

Classes

Transformers returns native IDs0-16. Map them using sdk-taxonomy.json -> native_index_to_sdk_id, not an11-class consumer enum.

SDK ID	Name
1	caption
2	footnote
3	formula
4	list_item
5	page_footer
6	page_header
7	picture
8	section_header
9	table
10	text
11	title
12	document_index
13	code
14	checkbox_selected
15	checkbox_unselected
16	form
17	key_value_region

SDK0isreserved. SDK18-25 are unsupported by this head, although the SDK catalog contains their definitions. document_index detects a TOC/index region; it does not imply entry parsing, reading order or document hierarchy.

Fine-tuning targets cover SDK1-11. Loss masking protected unknown classes from negative supervision, but accuracy retention of classes12-17 is unmeasured.

Evaluation

All values below are percentages. AP averages IoU0.50:0.95; AP50/AP70/AP90 use the named IoU threshold. AR100 is COCOmaximumrecall, not recall at confidence0.30. These are previously exposed development panels, not independent final tests.

Panel	Pages	H5 AP	D5 AP	H5 AP50	H5 AP70	H5 AP90	H5 AR100
DocLayNet gold validation	6489	70.6572	76.1917	88.4057	78.1385	50.4501	82.8326
Financial validation subset	1745	61.5113	59.6151	74.8695	68.4569	43.3226	83.2427
FinTabNet validation	256	91.5856	91.2829	96.4231	95.6897	89.0473	97.1835
Exposed DocLayNet test	4999	73.3731	74.5767	87.7283	81.9561	54.2318	84.6841
Exposed synthetic test	100	32.1854	35.6326	41.6041	34.7108	23.4697	52.6072
Exposed FinTabNet test	255	87.8283	88.1351	92.4317	91.9150	85.4955	97.4497

The financial validation panel is the financial_reports category within gold validation, not the independently annotated HKEX corpus. No HKEX paired final-bot label accuracy is claimed. The table-only panels score SDK9; synthetic scores SDK5,7,8,9,10; the general panels score the shared11classes (absent GTclasses are excluded). H5 uses640resize versus D5's1024letterbox and a different encoder expansion; this is a practical comparison, not a pure pretraining ablation.

Known limitation: document Title. Gold-validation Title AP is35.9637 forH5 versus80.7431 forD5. H5 passes its improvement gate versusH0 but fails the prior numeric D5 replacement gate. The owner nevertheless selected H5 for this release on qualitative visual suitability. Historical gate results remain unchanged. Title improvements will be separate candidates, not silent replacement of these weights. See evaluation.json for complete metrics and per-classAP.

Quick start

Use an immutable40-character Hub commit for AX_DOCLAYOUT_REVISION; never load main or latest in a production consumer. The final publication receipt gives the exact revision. A private repository requires your own Hugging Face access. Do not embed tokens in source or command-line arguments.

Use Python3.12 or3.13. Install the matching CUDA12.9 PyTorch runtime using the official PyTorch distribution, then the pinned requirements.txt from this snapshot. Example:

python -m pip install torch==2.12.1 torchvision==0.27.1 --index-url https://download.pytorch.org/whl/cu129
python -m pip install transformers==4.53.3 Pillow==12.3.0 numpy==2.5.2 safetensors==0.8.0 huggingface-hub==0.36.2 tokenizers==0.21.4

Download and verify the bundle at an exact revision:

import hashlib, json, os, re
from pathlib import Path
from huggingface_hub import snapshot_download

revision = os.environ["AX_DOCLAYOUT_REVISION"]
assert re.fullmatch(r"[0-9a-f]{40}", revision)
folder = Path(snapshot_download(
    "AutomatosX/AXDocLayout-M-v1.4-CUDA-TF32",
    revision=revision,
))
manifest = json.loads((folder / "bundle-manifest.json").read_text())
for name, expected in manifest["files"].items():
    with (folder / name).open("rb") as stream:
        assert hashlib.file_digest(stream, "sha256").hexdigest() == expected["sha256"]
print(folder)

Run the included example using that verified local folder:

python /path/to/verified/snapshot/predict_image.py \
  --model-dir /path/to/verified/snapshot \
  --image page.png --output detections.json --device cuda --threshold 0.30

The example accepts raster images, not PDF files. It verifies the four core artifact hashes and exact SDK mapping, refuses CUDA-to-CPU fallback, and returns original-raster xyxy boxes. Native top300 postprocessing is retained. Threshold0.30 is a display convention, not calibrated probability. There is no addedNMS, ensemble, PDF geometry replacement or bot curation. Explicit --device cpu is available for inspection; it is not a CUDA performance claim.

This model is not loadable by the old11-class AXDocLayout.from_pretrained pin. Consumers must explicitly adopt the17-class native model and SDK mapping.

Training and attribution

H1/H5 continue the pinned Heron model with human DocLayNet gold replay and finance pools (FinTabNet.c, IIIT-AR-13K inH1, and financial-layout-analysis synthetic data). Training inputs and source documents are not redistributed. No new HKEX pseudo-label fine-tuning was used in this H5 run. Complete and partial-label supervision masks were preserved. The training corpus is not licensed by this weights repository.

Apache-2.0. See LICENSE and NOTICE. Upstream model: Docling Heron. Technical report: Livathinos et al.,2025.