Publish owner-selected H5.1 as v1.5 with qualified CUDA profile and preserved tradeoffs

LICENSE

@@ -0,0 +1,202 @@
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NOTICE

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+AX-DocLayout-M v1.5 (H5.1), owner-selected release, 2026.
+Derived from docling-project/docling-layout-heron revision
+8f39ad3c0b4c58e9c2d2c84a38465abf757272d8 under Apache-2.0.
+Changes: H1/H5 finance tuning, paired H5.1 Title sampling and AX PDF SDK binding.
+No training documents or rendered pages are included.

README.md

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+---
+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.

bundle-manifest.json

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+}

config.json

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+{
+  "activation_dropout": 0.0,
+  "activation_function": "silu",
+  "anchor_image_size": null,
+  "architectures": [
+    "RTDetrV2ForObjectDetection"
+  ],
+  "attention_dropout": 0.0,
+  "auxiliary_loss": true,
+  "backbone": null,
+  "backbone_config": {
+    "depths": [
+      3,
+      4,
+      6,
+      3
+    ],
+    "downsample_in_bottleneck": false,
+    "downsample_in_first_stage": false,
+    "embedding_size": 64,
+    "hidden_act": "relu",
+    "hidden_sizes": [
+      256,
+      512,
+      1024,
+      2048
+    ],
+    "layer_type": "bottleneck",
+    "model_type": "rt_detr_resnet",
+    "num_channels": 3,
+    "out_features": [
+      "stage2",
+      "stage3",
+      "stage4"
+    ],
+    "out_indices": [
+      2,
+      3,
+      4
+    ],
+    "stage_names": [
+      "stem",
+      "stage1",
+      "stage2",
+      "stage3",
+      "stage4"
+    ]
+  },
+  "backbone_kwargs": null,
+  "batch_norm_eps": 1e-05,
+  "box_noise_scale": 1.0,
+  "d_model": 256,
+  "decoder_activation_function": "relu",
+  "decoder_attention_heads": 8,
+  "decoder_ffn_dim": 1024,
+  "decoder_in_channels": [
+    256,
+    256,
+    256
+  ],
+  "decoder_layers": 6,
+  "decoder_method": "default",
+  "decoder_n_levels": 3,
+  "decoder_n_points": 4,
+  "decoder_offset_scale": 0.5,
+  "dropout": 0.0,
+  "encode_proj_layers": [
+    2
+  ],
+  "encoder_activation_function": "gelu",
+  "encoder_attention_heads": 8,
+  "encoder_ffn_dim": 1024,
+  "encoder_hidden_dim": 256,
+  "encoder_in_channels": [
+    512,
+    1024,
+    2048
+  ],
+  "encoder_layers": 1,
+  "eos_coefficient": 0.0001,
+  "eval_size": null,
+  "feat_strides": [
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+    16,
+    32
+  ],
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+  "focal_loss_gamma": 2.0,
+  "freeze_backbone_batch_norms": true,
+  "hidden_expansion": 1.0,
+  "id2label": {
+    "0": "caption",
+    "1": "footnote",
+    "2": "formula",
+    "3": "list_item",
+    "4": "page_footer",
+    "5": "page_header",
+    "6": "picture",
+    "7": "section_header",
+    "8": "table",
+    "9": "text",
+    "10": "title",
+    "11": "document_index",
+    "12": "code",
+    "13": "checkbox_selected",
+    "14": "checkbox_unselected",
+    "15": "form",
+    "16": "key_value_region"
+  },
+  "initializer_bias_prior_prob": null,
+  "initializer_range": 0.01,
+  "is_encoder_decoder": true,
+  "label2id": {
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+    "checkbox_selected": 13,
+    "checkbox_unselected": 14,
+    "code": 12,
+    "document_index": 11,
+    "footnote": 1,
+    "form": 15,
+    "formula": 2,
+    "key_value_region": 16,
+    "list_item": 3,
+    "page_footer": 4,
+    "page_header": 5,
+    "picture": 6,
+    "section_header": 7,
+    "table": 8,
+    "text": 9,
+    "title": 10
+  },
+  "label_noise_ratio": 0.5,
+  "layer_norm_eps": 1e-05,
+  "learn_initial_query": false,
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+  "matcher_gamma": 2.0,
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+  "model_type": "rt_detr_v2",
+  "normalize_before": false,
+  "num_denoising": 100,
+  "num_feature_levels": 3,
+  "num_queries": 300,
+  "positional_encoding_temperature": 10000,
+  "torch_dtype": "float32",
+  "transformers_version": "4.53.3",
+  "use_focal_loss": true,
+  "use_pretrained_backbone": false,
+  "use_timm_backbone": false,
+  "weight_loss_bbox": 5.0,
+  "weight_loss_giou": 2.0,
+  "weight_loss_vfl": 1.0,
+  "with_box_refine": true
+}

cpu-parity.json

@@ -0,0 +1,13 @@
+{
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+  "pages": 150,
+  "raw_detections": 45000,
+  "boxes_at_030": 2405,
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+  "example_sha256": "afe45b076c2b2688e1b58307a923c5c001333a8440dd303cf19a2e1cf21b929b",
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+  "seconds": 39.956092542008264
+}

evaluation-and-pr.json

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evaluation.json

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+{
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+}

export-receipt.json

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frozen-numerical-screen.json

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model-selection.json

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+{
+  "completed_updates": 1024,
+  "decision": "take it; owner considers improvement good",
+  "frozen_numerical_screen_passed": false,
+  "model_identity": "AutomatosX/AXDocLayout-M-v1.5-CUDA-TF32",
+  "model_version": "1.5",
+  "numeric_gate_reclassified_as_passed": false,
+  "public_loader_pin_changed": false,
+  "publication_authority": "owner explicitly requested this exact Hub repository",
+  "published": true,
+  "published_v14_changed": false,
+  "release_selected": true,
+  "schema": "ax-doclayout.owner-model-selection/1",
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+  "selected": true,
+  "selected_training_stage": "H51-title",
+  "selection_authority": "owner directive after reported Title AP, precision, recall and table AP90 tradeoffs",
+  "source_checkpoint_sha256": "44b75b2ca14d7d4756ca51d402d470d069f17ee99145717e1994549b32ef87d2",
+  "source_git_revision": "adce6c2ddfc1977956fc180463ab221f780c8271",
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+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d0949704220ee50d94359cb6417e3481ac4ef5078a9f63795989e9f5d76e9b7e
+size 171658996

original-cpu-parity.json

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+{"all_predictions_exact":true,"boxes_at_030":2405,"device":"cpu","elapsed_seconds":41.683596957998816,"example_sha256":"e7d60ca02f40ea2eb973c46ae6aa452f0d820bf8cdd54a153465f9437f26260d","new_example_cuda_rerun":false,"pages":150,"raw_detections":45000,"review_manifest_sha256":"8083a57308277239d5e5de7acd7a0fec4635bc23c1b3238bb2fd47d35b1b2e72","schema":"ax-doclayout.selected-bundle-cpu-parity/1","torch":"2.12.1","transformers":"4.53.3","weights_sha256":"d0949704220ee50d94359cb6417e3481ac4ef5078a9f63795989e9f5d76e9b7e"}

predict_image.py

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+"""Pinned owner-selected H5.1 raster inference example; native model outputs map to AX PDF SDK IDs."""
+from __future__ import annotations
+
+import argparse
+import hashlib
+import importlib.metadata
+import json
+import math
+from pathlib import Path
+
+import torch
+from PIL import Image
+from transformers import RTDetrImageProcessor, RTDetrV2ForObjectDetection
+
+REPO_ID = 'AutomatosX/AXDocLayout-M-v1.5-CUDA-TF32'
+CORE_HASHES = {'config.json': '7691e69a4e4cb70416c2e12bbefea3a90671790226b2c0d22ede944c48e11f05', 'model.safetensors': 'd0949704220ee50d94359cb6417e3481ac4ef5078a9f63795989e9f5d76e9b7e', 'preprocessor_config.json': 'cd38cd59999e7a95d68e487fbe5132df3d4e5c32a0836add57e6126ba0c4eaf1', 'sdk-taxonomy.json': '81e7908ee7c8bbb6e00905a98ed43c988128faa38c0b1d55d20948f78e40e351'}
+
+
+def digest(path: Path) -> str:
+    with path.open('rb') as stream:
+        return hashlib.file_digest(stream, 'sha256').hexdigest()
+
+
+def validate_threshold(value: float) -> float:
+    if isinstance(value, bool) or not isinstance(value, (float, int)):
+        raise TypeError('score_threshold_invalid')
+    if not math.isfinite(value) or not 0 <= value <= 1:
+        raise ValueError('score_threshold_invalid')
+    return float(value)
+
+
+def load_bundle(*, model_dir: Path, device: str = 'cuda') -> tuple:
+    """Load exact local files with an explicit device; never fall back from CUDA."""
+    if device not in ('cuda', 'cpu'):
+        raise ValueError('device_invalid')
+    if importlib.metadata.version('transformers') != '4.53.3':
+        raise RuntimeError('transformers_version_mismatch')
+    if torch.__version__.split('+')[0] != '2.12.1':
+        raise RuntimeError('torch_version_mismatch')
+    for name, expected in CORE_HASHES.items():
+        path = model_dir / name
+        if not path.is_file() or digest(path) != expected:
+            raise RuntimeError('model_bundle_hash_mismatch')
+    if device == 'cuda':
+        if not torch.cuda.is_available():
+            raise RuntimeError('cuda_required_no_cpu_fallback')
+        if torch.cuda.get_device_capability()[0] < 8:
+            raise RuntimeError('ampere_or_newer_required_for_tf32_profile')
+        # Preserve the actual three-panel CUDA evaluation configuration.
+        torch.backends.cuda.matmul.allow_tf32 = False
+        torch.backends.cudnn.allow_tf32 = True
+    model, loading = RTDetrV2ForObjectDetection.from_pretrained(
+        model_dir, local_files_only=True, output_loading_info=True,
+    )
+    if any(loading[key] for key in ('missing_keys', 'unexpected_keys', 'mismatched_keys', 'error_msgs')):
+        raise RuntimeError('model_state_mismatch')
+    binding = json.loads((model_dir / 'sdk-taxonomy.json').read_text())
+    if binding['taxonomy'] != 'ax-document-unified-25/1' or binding['model_sdk_class_ids'] != list(range(1, 18)):
+        raise RuntimeError('sdk_binding_mismatch')
+    definitions = {row['id']: row for row in binding['classes']}
+    for native, sdk in binding['native_index_to_sdk_id'].items():
+        if model.config.id2label[int(native)] != definitions[sdk]['name']:
+            raise RuntimeError('sdk_name_mismatch')
+    processor = RTDetrImageProcessor.from_pretrained(model_dir, local_files_only=True)
+    return model.float().to(device).eval(), processor, binding
+
+
+def predict_rgb(*, rgb: Image.Image, model: object, processor: object,
+                binding: dict, threshold: float = .30) -> list[dict]:
+    """Return original-raster xyxy boxes with SDK IDs1-17 and finite scores."""
+    threshold = validate_threshold(threshold)
+    if not isinstance(rgb, Image.Image) or rgb.mode != 'RGB' or min(rgb.size) < 1:
+        raise ValueError('decoded_rgb_image_required')
+    device = next(model.parameters()).device
+    inputs = processor(images=rgb, return_tensors='pt')['pixel_values']
+    if tuple(inputs.shape) != (1, 3, 640, 640):
+        raise RuntimeError('preprocessor_shape_mismatch')
+    with torch.inference_mode():
+        outputs = model(pixel_values=inputs.to(device))
+        predictions = processor.post_process_object_detection(
+            outputs, threshold=0.,
+            target_sizes=torch.tensor([[rgb.height, rgb.width]], device=device),
+        )[0]
+    definitions = {row['id']: row['name'] for row in binding['classes']}
+    result = []
+    for native, score, box in zip(predictions['labels'].tolist(), predictions['scores'].tolist(),
+                                  predictions['boxes'].tolist(), strict=True):
+        if not all(math.isfinite(v) for v in [score, *box]) or not 0 <= score <= 1:
+            raise RuntimeError('nonfinite_or_invalid_prediction')
+        if score < threshold:
+            continue
+        sdk = binding['native_index_to_sdk_id'][str(native)]
+        clipped = [min(max(box[0], 0.), rgb.width), min(max(box[1], 0.), rgb.height),
+                   min(max(box[2], 0.), rgb.width), min(max(box[3], 0.), rgb.height)]
+        if clipped[2] <= clipped[0] or clipped[3] <= clipped[1]:
+            continue
+        result.append({'class_id': sdk, 'class_name': definitions[sdk],
+                       'score': score, 'bbox_xyxy': clipped})
+    return result
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument('--model-dir', type=Path, required=True)
+    parser.add_argument('--image', type=Path, required=True)
+    parser.add_argument('--output', type=Path, required=True)
+    parser.add_argument('--device', choices=('cuda', 'cpu'), default='cuda')
+    parser.add_argument('--threshold', type=float, default=.30)
+    args = parser.parse_args()
+    validate_threshold(args.threshold)
+    if args.output.exists():
+        raise RuntimeError('output_already_exists')
+    try:
+        with Image.open(args.image) as image:
+            if image.format not in ('PNG', 'JPEG', 'TIFF', 'WEBP', 'BMP'):
+                raise ValueError('raster_format_required')
+            rgb = image.convert('RGB')
+    except (OSError, ValueError):
+        raise RuntimeError('raster_image_read_failed') from None
+    model, processor, binding = load_bundle(model_dir=args.model_dir, device=args.device)
+    detections = predict_rgb(rgb=rgb, model=model, processor=processor,
+                             binding=binding, threshold=args.threshold)
+    result = {'model_id': REPO_ID, 'model_sha256': CORE_HASHES['model.safetensors'],
+              'taxonomy': binding['taxonomy'], 'width': rgb.width, 'height': rgb.height,
+              'coordinate_space': 'original-raster-pixel-xyxy', 'device': args.device,
+              'score_threshold': args.threshold, 'detections': detections}
+    args.output.write_text(json.dumps(result, sort_keys=True, allow_nan=False) + '\n')
+
+
+if __name__ == '__main__':
+    main()

preprocessor_config.json

@@ -0,0 +1,26 @@
+{
+  "do_convert_annotations": true,
+  "do_normalize": false,
+  "do_pad": false,
+  "do_rescale": true,
+  "do_resize": true,
+  "format": "coco_detection",
+  "image_mean": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "image_processor_type": "RTDetrImageProcessor",
+  "image_std": [
+    0.229,
+    0.224,
+    0.225
+  ],
+  "pad_size": null,
+  "resample": 2,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 640,
+    "width": 640
+  }
+}

qualification.json

@@ -0,0 +1,21 @@
+{
+  "boundary_tests_passed": 14,
+  "cpu_example": {
+    "all_predictions_exact": true,
+    "boxes_at_030": 2405,
+    "device": "cpu",
+    "example_sha256": "afe45b076c2b2688e1b58307a923c5c001333a8440dd303cf19a2e1cf21b929b",
+    "new_example_cuda_rerun": false,
+    "pages": 150,
+    "raw_detections": 45000,
+    "review_manifest_sha256": "8083a57308277239d5e5de7acd7a0fec4635bc23c1b3238bb2fd47d35b1b2e72",
+    "schema": "ax-doclayout.h51-v15-cpu-parity/1",
+    "seconds": 39.956092542008264,
+    "weights_sha256": "d0949704220ee50d94359cb6417e3481ac4ef5078a9f63795989e9f5d76e9b7e"
+  },
+  "existing_cuda_evidence": "exact H51-title final1024 three-panel evaluation and exact exported tensor reload",
+  "new_example_cuda_rerun": false,
+  "owner_selected_despite_frozen_screen_failure": true,
+  "ruff_passed": true,
+  "schema": "ax-doclayout.h51-v15-qualification/1"
+}

requirements.txt

@@ -0,0 +1,8 @@
+# Match the recorded CUDA runtime. Install CUDA PyTorch wheels separately as shown in README.
+torch==2.12.1
+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

retention-drift.json

@@ -0,0 +1 @@
+{"accuracy_claim":false,"analysis_source_sha256":"a0b3cdf2c18b79ef8b861bb8954ad6ab58f7009d47e958e0f835ee35aad02015","counts":{"H5":{"1":16,"10":1005,"11":4,"12":1,"13":0,"14":0,"15":1,"16":0,"17":26,"2":20,"3":0,"4":415,"5":178,"6":134,"7":22,"8":523,"9":113},"H51-control":{"1":16,"10":994,"11":4,"12":1,"13":0,"14":0,"15":0,"16":0,"17":24,"2":20,"3":0,"4":411,"5":177,"6":131,"7":23,"8":520,"9":115},"H51-title":{"1":14,"10":977,"11":7,"12":1,"13":0,"14":0,"15":0,"16":0,"17":16,"2":20,"3":0,"4":406,"5":175,"6":134,"7":23,"8":519,"9":113}},"extra_class_quality_measured":false,"page_count_changes_from_h5":{"H51-control":[{"delta":{"4":-1},"id":"006"},{"delta":{"10":-3,"9":1},"id":"010"},{"delta":{"10":1},"id":"029"},{"delta":{"10":-1,"9":1},"id":"033"},{"delta":{"10":1},"id":"034"},{"delta":{"8":2},"id":"038"},{"delta":{"8":-1,"9":1},"id":"040"},{"delta":{"10":-1,"6":-1},"id":"041"},{"delta":{"4":1},"id":"043"},{"delta":{"5":-1},"id":"047"},{"delta":{"10":-1,"8":-1},"id":"049"},{"delta":{"10":1,"8":1},"id":"050"},{"delta":{"10":1,"4":-1,"8":-1},"id":"055"},{"delta":{"10":-2,"17":-1,"4":-1},"id":"060"},{"delta":{"8":1},"id":"061"},{"delta":{"10":1},"id":"063"},{"delta":{"10":-1},"id":"068"},{"delta":{"10":-1,"4":-1},"id":"069"},{"delta":{"10":-2,"15":-1},"id":"070"},{"delta":{"8":-1},"id":"077"},{"delta":{"6":-1},"id":"079"},{"delta":{"7":1},"id":"084"},{"delta":{"4":-1,"9":-1},"id":"085"},{"delta":{"10":-1},"id":"087"},{"delta":{"10":-1},"id":"088"},{"delta":{"10":-1},"id":"089"},{"delta":{"8":-1},"id":"091"},{"delta":{"5":-1,"6":-1,"8":-1},"id":"094"},{"delta":{"5":1},"id":"098"},{"delta":{"4":1},"id":"101"},{"delta":{"10":-1,"8":1},"id":"102"},{"delta":{"4":-1},"id":"103"},{"delta":{"10":1},"id":"106"},{"delta":{"10":-1},"id":"107"},{"delta":{"8":-1},"id":"118"},{"delta":{"10":1},"id":"125"},{"delta":{"10":-1,"17":-1},"id":"130"},{"delta":{"4":1},"id":"134"},{"delta":{"4":-1},"id":"137"},{"delta":{"8":-1},"id":"144"}],"H51-title":[{"delta":{"4":-1},"id":"006"},{"delta":{"10":1},"id":"007"},{"delta":{"10":-3,"17":-1},"id":"010"},{"delta":{"1":-1},"id":"018"},{"delta":{"11":1},"id":"020"},{"delta":{"4":-1},"id":"024"},{"delta":{"10":1},"id":"029"},{"delta":{"10":-1,"11":1},"id":"031"},{"delta":{"10":-1,"9":1},"id":"033"},{"delta":{"9":1},"id":"034"},{"delta":{"8":1},"id":"038"},{"delta":{"8":-1},"id":"040"},{"delta":{"10":-1,"17":-1},"id":"041"},{"delta":{"6":1},"id":"042"},{"delta":{"4":-1,"5":-1},"id":"043"},{"delta":{"5":-1},"id":"047"},{"delta":{"10":-2,"8":-1},"id":"049"},{"delta":{"8":1},"id":"050"},{"delta":{"10":1,"4":-1,"8":-1},"id":"055"},{"delta":{"10":-3,"17":-1},"id":"060"},{"delta":{"10":-1,"8":1},"id":"061"},{"delta":{"6":1},"id":"067"},{"delta":{"10":-1,"4":-1,"8":-1},"id":"069"},{"delta":{"10":-15,"15":-1,"17":-3,"8":1},"id":"070"},{"delta":{"10":2},"id":"071"},{"delta":{"8":-1},"id":"077"},{"delta":{"6":-1},"id":"079"},{"delta":{"7":1},"id":"084"},{"delta":{"4":-2,"8":2,"9":-2},"id":"085"},{"delta":{"5":-1},"id":"086"},{"delta":{"10":-1},"id":"087"},{"delta":{"10":-1},"id":"089"},{"delta":{"8":-1},"id":"091"},{"delta":{"5":-1,"6":-1,"8":-1},"id":"094"},{"delta":{"1":-1},"id":"096"},{"delta":{"5":1},"id":"098"},{"delta":{"10":-1,"5":-1},"id":"102"},{"delta":{"4":-1,"8":-1},"id":"103"},{"delta":{"10":1},"id":"106"},{"delta":{"8":1},"id":"112"},{"delta":{"8":-1},"id":"118"},{"delta":{"17":-1},"id":"123"},{"delta":{"10":-2},"id":"125"},{"delta":{"10":-1,"17":-1,"9":-1},"id":"130"},{"delta":{"11":1},"id":"132"},{"delta":{"17":-1},"id":"136"},{"delta":{"8":-1},"id":"137"},{"delta":{"4":-1,"9":1},"id":"138"},{"delta":{"5":1,"8":-1},"id":"144"},{"delta":{"17":-1},"id":"149"}]},"pages":150,"production_promotion_allowed":false,"schema":"ax-doclayout.h51-retention-count-drift/1","score_threshold":0.3}

runtime-profile.json

@@ -0,0 +1 @@
+{"activation_dtype":"float32","all_matmul_tf32_qualified":false,"autocast":false,"cuda_matmul_allow_tf32":false,"cudnn_allow_tf32":true,"device":"cuda","id":"cuda-fp32-cudnn-tf32/1","input":"RGB640directresize","new_packaged_example_cuda_rerun":false,"onnx_artifact_included":false,"preprocessor_sha256":"cd38cd59999e7a95d68e487fbe5132df3d4e5c32a0836add57e6126ba0c4eaf1","qualification_source":"exact H51-title final1024 three-panel CUDA evaluation; reloaded export tensor parity","schema":"ax-doclayout.runtime-profile/1","storage_dtype":"float32","tensorrt_qualified":false,"tested_cuda":"12.9","tested_cudnn":92000,"tested_gpu":"NVIDIA RTX PRO 6000 Blackwell Server Edition","tested_torch":"2.12.1+cu129","tested_transformers":"4.53.3","tf32_scope":"cuDNN operations only; GEMM matmul TF32 is disabled"}

sdk-taxonomy.json

@@ -0,0 +1 @@
+{"additional_class_retention_verified":false,"catalog_implies_detection_support":false,"catalog_sha256":"f0c332ec6ff5871f911b20b4405573b4db088497480f337f65b6b8950e15cc50","classes":[{"description":"Reserved slot; never a public detection class.","id":0,"name":"reserved","task":"reserved"},{"description":"Caption describing a figure, table or illustration.","id":1,"name":"caption","task":"layout"},{"description":"Supplementary note linked to main text.","id":2,"name":"footnote","task":"layout"},{"description":"Standalone mathematical expression or equation.","id":3,"name":"formula","task":"layout"},{"description":"Individual bulleted or numbered list item.","id":4,"name":"list_item","task":"layout"},{"description":"Footer content such as page numbers or recurring text.","id":5,"name":"page_footer","task":"layout"},{"description":"Recurring content at the top of a page.","id":6,"name":"page_header","task":"layout"},{"description":"Figure, photograph, diagram or illustration.","id":7,"name":"picture","task":"layout"},{"description":"Heading introducing a section or subsection.","id":8,"name":"section_header","task":"layout"},{"description":"Information arranged in rows and columns.","id":9,"name":"table","task":"layout"},{"description":"General body text or paragraph.","id":10,"name":"text","task":"layout"},{"description":"Main document title.","id":11,"name":"title","task":"layout"},{"description":"Document index or table of contents region.","id":12,"name":"document_index","task":"layout"},{"description":"Source code or programming listing.","id":13,"name":"code","task":"layout"},{"description":"Checked or selected checkbox.","id":14,"name":"checkbox_selected","task":"layout"},{"description":"Empty or unselected checkbox.","id":15,"name":"checkbox_unselected","task":"layout"},{"description":"Form region containing fields or controls.","id":16,"name":"form","task":"layout"},{"description":"Related label and value information.","id":17,"name":"key_value_region","task":"layout"},{"description":"Comment or review annotation.","id":18,"name":"comment","task":"markup"},{"description":"Highlighted content.","id":19,"name":"highlighted","task":"markup"},{"description":"Handwritten annotation or content.","id":20,"name":"handwritten","task":"markup"},{"description":"Content marked with a strikeout line.","id":21,"name":"strikethrough","task":"markup"},{"description":"Caption specifically describing a table.","id":22,"name":"table_caption","task":"layout"},{"description":"Supplementary note associated with a table.","id":23,"name":"table_footnote","task":"layout"},{"description":"Caption or explanatory label associated with a formula.","id":24,"name":"formula_caption","task":"layout"},{"description":"Other region; no automatic mapping from source abandon labels.","id":25,"name":"other","task":"layout"}],"fine_tuning_positive_class_ids":[1,2,3,4,5,6,7,8,9,10,11],"model_sdk_class_ids":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17],"native_index_to_sdk_id":{"0":1,"1":2,"10":11,"11":12,"12":13,"13":14,"14":15,"15":16,"16":17,"2":3,"3":4,"4":5,"5":6,"6":7,"7":8,"8":9,"9":10},"taxonomy":"ax-document-unified-25/1","unsupported_sdk_class_ids":[18,19,20,21,22,23,24,25]}