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MinerU-4_models_onnx
opendatalab/MinerU-4_models_onnx
MinerU-4_models_onnx is a machine learning model from opendatalab. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
CPU ONNX Runtime models for MinerU's light stack. Requires onnxruntime = 1.20.1 (ONNX IR 10 support).
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Public
Updated Sep 10, 2026
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.onnx856 MB · 100%
At a glance
- Access
- Public
- Created
- Sep 10, 2026
- Updated
- Sep 10, 2026
- SHA
- 358310b4
Model card
From the Hugging Face model README
MinerU 4 ONNX models
CPU ONNX Runtime models for MinerU's light stack. Requires onnxruntime >= 1.20.1 (ONNX IR 10 support).
- Layout: PP-DocLayoutV2
- OCR: PP-OCRv6 Tiny Det + Small Rec
- Seal: PP-OCRv4 mobile seal detector + shared Small Rec
- Formula: PP-FormulaNet plus-M
- Table: identical to MinerU-4_models_torch
See manifest.json for source repositories, exact revisions, SHA-256 checksums, ONNX signatures and export validation. Original model licenses and attribution remain applicable.
OCR uses the single TXT dictionary distributed with MinerU. Prepare these assets with python -m scripts.prepare_onnx_models prepare.
Formula model update (2026-09-11)
PP-FormulaNet plus-M now uses an ONNX graph exported from the matching MinerU-4_models_torch PTH checkpoint. CPU FP32, opset 17, IR 8, dynamic batch and a graph-internal autoregressive Loop. MinerU CPU inference uses batches of up to 8, sorted by original crop area.
The 153-image validation matched the previous ONNX in token sequences and LaTeX, including batches 1/2/4/8/16. The export preserves Torch generation behavior: forced EOS at generated token 1536; the previous ONNX allowed up to 2560 tokens without forced EOS. Validation samples did not reach this boundary. See manifest.json for provenance and hashes.
- Created
- Sep 10, 2026
- Updated
- Sep 10, 2026