---
license: agpl-3.0
library_name: ax-doclayout
pipeline_tag: object-detection
tags:
- document-layout
- object-detection
- onnx
- safetensors
- cuda
- tf32
---

# AXDocLayout-M v1.3 CUDA TF32

AX DocLayout detects layout regions in a decoded RGB page image and returns
class labels, confidence scores and boxes in the original raster coordinates.
This model updates the weights while retaining the v1.2 CUDA TF32 inference
standard. The predecessor remains available at
[AXDocLayout-M v1.2 CUDA TF32](https://huggingface.co/AutomatosX/AXDocLayout-M-v1.2-CUDA-TF32).

## Model and runtime

Architecture: RT-DETRv2 `rtdetrv2_r50vd_m`. Stored weights and ONNX inputs are
FP32. CUDA TF32 is the execution policy, not a BF16 weight format. The bundle
contains FP32 safetensors, a standard-operator ONNX graph (opset 16) and the
complete inference source. Integer quantization is not used.

The ONNX backend requires Linux x86-64, Python 3.12 or 3.13 and an NVIDIA
TF32-capable Ampere or newer device with a compatible CUDA 12.8 driver. Install
the pinned ONNX Runtime GPU 1.23.2, CUDA 12.8 and cuDNN 9 dependencies through
the `onnx` extra. CUDAExecutionProvider is required, TF32 is explicitly enabled,
and startup checks verify recognized compute placement. Shape/control nodes
may use the CPU. An unavailable CUDA backend raises an error.

A separate explicit PyTorch CPU FP32 reference is available with the `torch`
extra and `backend="pytorch"`. Vectorized postprocessing follows v1.2.

## Input and output

Input is one HWC uint8 RGB raster. Preserve aspect ratio, resize the long edge
to 1024, center-pad to 1024 x 1024 with white, and convert to NCHW float32 in
[0, 1]. Contract: `rgb-letterbox-1024/1`.

Direct ONNX inputs are `images` float32 [N, 3, 1024, 1024] and
`orig_target_sizes` int64 [N, 2], always [1024, 1024] per image. Outputs are `labels`,
`boxes`, `scores`. Raw boxes are letterbox-space xyxy; the Python API undoes
padding/scaling and clips boxes to the page. Default score floor is 0.30;
invalid or reserved labels are removed.

The JSON schema is `ax-doclayout.detection/1`; taxonomy is `ax-doclayout-11/1`.
Boxes use half-open pixel edges, origin top-left, x rightward, y downward.

| ID | Label |
|---|---|
| 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` |

## Qualification and limitations

The following measurements use internal development cohorts on one NVIDIA
RTX PRO 6000 Blackwell Server Edition. They are not independent acceptance
or a universal benchmark superiority claim. The predecessor and this release
were measured on the same host with the same CUDA TF32 runtime. AP uses
score floor 0.001, COCO IoU 0.50:0.95, area all and maxDets 100.

| Development cohort | Model | AP | AP50 | AP70 | AP90 |
|---|---|---:|---:|---:|---:|
|General, 6489 pages, 11 classes|v1.2|76.2397|90.9894|86.8779|55.9050|
|General, 6489 pages, 11 classes|v1.3|76.1623|90.9487|86.7953|55.8282|
|Financial layout, 100 pages, table|v1.2|66.7463|78.4563|71.8082|54.7579|
|Financial layout, 100 pages, table|v1.3|69.4385|80.4662|74.8691|58.1819|
|Financial tables, 256 pages|v1.2|90.8539|95.5602|94.5320|89.6775|
|Financial tables, 256 pages|v1.3|90.9530|95.3348|94.2652|89.3608|

These cohorts show mixed tradeoffs: financial-layout table AP improves, while
some general and high-IoU table metrics decrease. Validate on representative
inputs before deployment. Dataset definitions differ from other comparisons;
their numbers should not be mixed.

Public API latency uses the same 100 decoded RGB pages, three counterbalanced
rounds per model, 10 untimed initial calls per session and the default score floor 0.30.
The timed region ends at PageStructure and excludes raster decoding, JSON
serialization and worker transport. Only the production session is retained.

| Same-host CUDA TF32 API | Median ms | p95 ms | Mean ms |
|---|---:|---:|---:|
|v1.2|17.959|18.268|17.998|
|v1.3|17.945|18.563|18.013|

The ONNX operator structure and numerical inference code follow v1.2; model
weights and immutable artifact bindings are updated. Conversion checks passed
against native FP32 reference weights. The CUDA-only inference environment
contains no PyTorch. A 128-page check verified preprocessing and API equivalence
using captured same-invocation outputs. GPU execution can vary slightly across
invocations; no bitwise repeatability guarantee is made.

The detector accepts RGB page rasters. It does not open PDFs, perform OCR,
extract text, predict reading order or hierarchy, or reconstruct table cells.

## License

The model weights and modified inference source are **AGPL-3.0-only**.
Commercial use is permitted subject to the license terms, including applicable
source-disclosure obligations. See LICENSE and NOTICE. Preserved Apache-2.0
component notices and rights remain in NOTICE. The complete inference source
is provided in `inference/`.

See the root README for immutable source installation and model revisions.