---
library_name: transformers
license: apache-2.0
pipeline_tag: object-detection
base_model: docling-project/docling-layout-heron
base_model_relation: finetune
tags:
- document-layout-analysis
- finance
- rtdetr-v2
- cuda
- tf32
---

# AX-DocLayout-M v1.5 - H5.1 CUDA / cuDNN TF32

This is the owner-selected **H51-title final-1024 EMA** model. It continues
H5 finance fine-tuning with a paired Title-sampling study. It is a separate
Heron-derived model identity, not D5/D6/H7, not the public eleven-class v1.0
model, and not an ax-doclayout Python package release. The original v1.4 weights
and public v1.0 loader pin remain unchanged.

Decoded RGB page raster in; layout boxes out. Native classes0-16 map to AX PDF
SDK IDs1-17 via `sdk-taxonomy.json` (ax-document-unified-25/1). SDK18-25 are
unsupported. This model does not open PDFs, perform OCR or infer reading order.
Accuracy retention of extra SDK classes12-17 is unmeasured.

## Exact artifact and qualified runtime

- FP32 safetensors SHA256:
  `d0949704220ee50d94359cb6417e3481ac4ef5078a9f63795989e9f5d76e9b7e`.
- H51-title final1024 checkpoint SHA256:
  `44b75b2ca14d7d4756ca51d402d470d069f17ee99145717e1994549b32ef87d2`.
- Upstream: docling-project/docling-layout-heron at immutable revision
  `8f39ad3c0b4c58e9c2d2c84a38465abf757272d8`, Apache-2.0.
- Native **640x640 direct resize**, original hash-pinned image processor.
  Do not substitute D5's1024letterbox or H7's1696letterbox preprocessing.
- Tested CUDA: PyTorch2.12.1+cu129, CUDA12.9, Transformers4.53.3,
  RTX PRO6000Blackwell. FP32 storage/activations, no autocast or quantization.
- **cuDNN TF32 allowed; CUDA matmul TF32 disabled.** The suffix means cuDNN
  permission, not all-matmul TF32. No ONNX or TensorRT artifact is included.
- The exact selected checkpoint has existing three-panel CUDA evidence and
  exact exported-tensor reload evidence. The packaged example is CPU-qualified;
  it was not freshly rerun on CUDA for this publication. No GPU was restarted.

## Measured results and accepted tradeoffs

All AP values are percentages on previously exposed development panels.

| Panel | H5 | Matched control | Selected H5.1 / v1.5 |
|---|---:|---:|---:|
| General AP (6489pages) |70.6572|71.1214|71.5327|
| Financial AP (1745pages) |61.5113|61.5445|61.7331|
| FinTab AP (256pages) |91.5856|91.7371|91.5774|
| General Title AP |35.9637|37.0977|38.7453|
| General Title precision at0.30/IoU0.50 |38.9333|39.5833|36.2069|
| General Title recall at0.30/IoU0.50 |48.8294|50.8361|56.1873|
| FinTab AP90 |89.0473|89.0155|88.4481|

Title AP treatment-control improves1.6476pp, with a paired family-bootstrap95%
interval[0.4851,2.8212]. Compared with H5 at the fixed threshold,22additional
true Titles accompany67additional false Titles. **The frozen numerical screen
failed**, including the2pp Title AP target, Title precision retention and table
AP90 retention. The owner accepted these tradeoffs and explicitly selected
publication; the original screen has not been reclassified as passed.

Financial is a subset of General, not independent HKEX evidence. Family
independence, independent final-test quality, second-seed reproducibility and
extra-class accuracy are not established. This is not a claim of overall
superiority to D5, D6 or H7; their differently defined panels cannot be ranked
by subtracting historical scores. See evaluation.json and the frozen screen.

## Installation and inference

Use Python3.12/3.13. Install CUDA PyTorch separately for the recorded runtime:

```bash
python -m pip install torch==2.12.1 --index-url https://download.pytorch.org/whl/cu129
python -m pip install -r requirements.txt
```

Download this private repository with an authorized Hugging Face account. Pin
the immutable commit from the release receipt, or resolve tag `v1.5` to that
commit and use `hf download --revision <COMMIT>`. The example fails closed on
core hash or runtime mismatches and never falls back silently from CUDA.

```bash
python predict_image.py --model-dir . --image page.png --output layout.json --device cuda
```

Use `--device cpu` explicitly for CPU. Boxes use original raster pixel xyxy
coordinates and SDK class IDs1-17. The default score threshold is0.30; it is an
operating point, not COCO AP/AR100. Keep all17classes when interpreting output.
Do not load this artifact through the eleven-class v1.0 receipt-pinned loader.

The bundle contains inference files, aggregate numeric evidence, attribution
and hash receipts only. No training documents, pixels, per-page predictions,
optimizer states, credentials or private training implementation are included.