2026-10-27: PP-DocLayoutV3_fp32_batchable.onnx, PP-OCRv6_medium_det.onnx, PP-OCRv6_small_det.onnx, PP-OCRv6_tiny_det.onnx, PP-OCRv6_tiny_rec_ctc_fp16.onnx, PP-OCRv6_medium_rec_ctc_fp16.onnx, PP-OCRv6_small_rec_ctc_fp16.onnx, PP-FormulaNet_plus-M_encoder.onnx, PP-FormulaNet_plus-M_prep.onnx, PP-FormulaNet_plus-M_decoder_step.onnx, PP-FormulaNet_plus-M_tokenizer.json, PP-FormulaNet_plus-S_encoder.onnx, PP-FormulaNet_plus-S_prep.onnx, PP-FormulaNet_plus-S_decoder_step.onnx, PP-FormulaNet_plus-S_tokenizer.json, SLANet_plus_encoder.onnx, SLANet_plus_decoder.bin

833ebc4 verified 15 days ago

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metadata

license: apache-2.0
tags:
  - onnx
  - document-parsing
  - layout-analysis
  - ocr
  - table-structure-recognition
  - formula-recognition

docparser-models

ONNX exports of PaddlePaddle document models, one flat file per model. MANIFEST.txt lists the sha256 of every file. All files are Apache-2.0.

File	Model	Source
`PP-DocLayoutV3_fp32_batchable.onnx`	layout detection	`PaddlePaddle/PP-DocLayoutV3_onnx`, output reshaped to `[B,300,7]`
`PP-OCRv6_{tiny,small,medium}_det.onnx`	text detection	`PaddlePaddle/PP-OCRv6_*_det_onnx`, unchanged
`PP-OCRv6_{tiny,small,medium}_rec_ctc_fp16.onnx`	text recognition	`PaddlePaddle/PP-OCRv6_*_rec_onnx`, plus `ctc_idx` / `ctc_prob` outputs (argmax and max over the class axis), FP16 weights with FP32 inputs and outputs
`SLANet_plus_encoder.onnx`, `SLANet_plus_decoder.bin`	table structure	`PaddlePaddle/SLANet_plus_onnx`: the encoder graph, and the GRU decoder's weights as raw float32 (run by a Python model, `table_decode`)
`PP-FormulaNet_plus-{M,S}_{encoder,prep,decoder_step}.onnx`, `_tokenizer.json`	formula recognition	`PaddlePaddle/PP-FormulaNet_plus-M` / `-S`, split into encoder, cross-attention precompute and one greedy decoder step (FP16, new-slice) whose KV axis is dynamic: one TensorRT engine per tier with a profile per KV length (plus-S 256 / 512 / 1024, plus-M 256 / 512 / 1536)

Pre- and post-processing follow each source model's inference.yml.