Static INT8 ONNX quantization of docling-project/docling-layout-heron-onnx (Docling Heron, RT-DETRv2, 17-class document-layout detection at 640×640). Produced for RailReaderCore.

What this is

QDQ static quantization of only the CNN backbone Conv ops; the RT-DETR transformer decoder is left in FP32.

Why backbone-only

Quantizing the decoder drives two quantization scales (model.decoder.anchors_scale, add_2355_scale) to Inf, collapsing all detection scores → zero detections. Leaving the decoder FP32 avoids this, and the backbone is where essentially all of the convolutional compute (and the speedup) lives. Quantizing any MatMul (encoder or decoder) was measured to either break the model or reduce recall with no speed gain.

Validation

Versus the FP32 base on 88 held-out real academic pages (detection agreement, FP32 as reference):

metric	value
recall	0.990
precision	0.988
mean IoU (matched)	0.984
pages with <90% recall	1 / 88
inference speedup (AVX-VNNI CPU, i7-12700H)	~2.6–3×

Visually faithful on dense and math-heavy pages.

Caveat: agreement is measured against the FP32 Heron model, not labelled ground truth — i.e. this is near-lossless versus the model you would otherwise ship, not an independent mAP claim.

I/O contract (identical to the base export)

Inputs: images uint8 [1,3,640,640] NCHW; orig_target_sizes int64 [1,2] = [W, H] (note: [W, H], not [H, W]).
Outputs: labels [1,300], boxes [1,300,4] (xyxy in pixel space), scores [1,300].

17 classes: caption, footnote, formula, list_item, page_footer, page_header, picture, section_header, table, text, title, document_index, code, checkbox_selected, checkbox_unselected, form, key_value_region.

Recipe

python

from onnxruntime.quantization import quantize_static, QuantFormat, QuantType, CalibrationMethod
quantize_static(
    pre_processed_fp32, out, calibration_reader,
    quant_format=QuantFormat.QDQ, per_channel=True,
    activation_type=QuantType.QUInt8, weight_type=QuantType.QInt8,
    calibrate_method=CalibrationMethod.MinMax,
    op_types_to_quantize=["Conv"],
)

Calibration: real rasterized academic pages. Full reproducible tooling: tools/quant-probe/ in RailReaderCore.

License

Inherits the license of the base model docling-project/docling-layout-heron-onnx.

stefanj0/docling-layout-heron-int8-onnx

Docling Heron Layout — backbone-only INT8 (ONNX)