2026-09-26: PP-DocLayoutV3_fp32_batchable.onnx, PP-DocLayoutV3_fp32_batchable_fp16.onnx, PP-OCRv6_medium_det.onnx, PP-OCRv6_small_det.onnx, PP-OCRv6_tiny_det.onnx, PP-OCRv6_tiny_rec_ctc.onnx, PP-OCRv6_tiny_rec_ctc_fp16.onnx, PP-OCRv6_medium_rec_ctc.onnx, PP-OCRv6_medium_rec_ctc_fp16.onnx, PP-OCRv6_small_rec_ctc.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, PP-LCNet_x1_0_table_cls.onnx, RapidTable_unet_uint8.onnx, SLANet_plus_encoder.onnx, SLANet_plus_decoder.bin

MANIFEST.txt

@@ -17,5 +17,7 @@ a021388d62aa43953b1dc7a3e0ea919b076aa8fdb98d8491830c6dbf2e5e4706  PP-FormulaNet_
 c18cf4eb94c3d16cf88faad285333a5b4b30b6e1718c2b92b4564baf984fbd7e  PP-FormulaNet_plus-S_prep.onnx
 e424f4400c921496eefc63a313b61c2167497f4a3c758cfc8dc40686ef6f5036  PP-FormulaNet_plus-S_decoder_step.onnx
 2811d82701ec97c192fa256aa2b4516929373870ae660326cc5b1dc879b95ff2  PP-FormulaNet_plus-S_tokenizer.json
+f8e4cb1b58a29bebd36852edcb238b53e0acefa75116a8a4db443f13dbc72b0b  PP-LCNet_x1_0_table_cls.onnx
+69737cded76e1106e85585547995cc952c6e98a978402cf916f5de9582e773ad  RapidTable_unet_uint8.onnx
 7e32c7e7e5b9c43dd11880749928c00a7cb22e074c378a01e1df9352c1b9f9b5  SLANet_plus_encoder.onnx
 f4b9f9b2d3fd5e85aebc839ebcd6bb5b27863077cc2b77c8e0a7ca01c6c0542d  SLANet_plus_decoder.bin

PP-LCNet_x1_0_table_cls.onnx

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+size 6777817

README.md

@@ -20,6 +20,8 @@ ONNX exports of PaddlePaddle document models, one flat file per model.
 | `PP-OCRv6_{tiny,small,medium}_det.onnx` | text detection | [`PaddlePaddle/PP-OCRv6_*_det_onnx`](https://huggingface.co/PaddlePaddle), unchanged |
 | `PP-OCRv6_{tiny,small,medium}_rec_ctc.onnx` | text recognition | [`PaddlePaddle/PP-OCRv6_*_rec_onnx`](https://huggingface.co/PaddlePaddle), plus `ctc_idx` / `ctc_prob` outputs (argmax and max over the class axis) |
 | `SLANet_plus_encoder.onnx`, `SLANet_plus_decoder.bin` | table structure | [`PaddlePaddle/SLANet_plus_onnx`](https://huggingface.co/PaddlePaddle/SLANet_plus_onnx): the encoder graph, and the GRU decoder's weights as raw float32 |
+| `PP-LCNet_x1_0_table_cls.onnx` | table classification (wired / wireless) | [`PaddlePaddle/PP-LCNet_x1_0_table_cls_onnx`](https://huggingface.co/PaddlePaddle/PP-LCNet_x1_0_table_cls_onnx), unchanged; labels `[wired_table, wireless_table]` |
+| `RapidTable_unet_uint8.onnx` | wired-table line segmentation | RapidAI [TableStructureRec](https://github.com/RapidAI/TableStructureRec)'s `unet.onnx` (Apache-2.0; byte-identical in [`opendatalab/PDF-Extract-Kit-1.0`](https://huggingface.co/opendatalab/PDF-Extract-Kit-1.0) `models/TabRec/UnetStructure/`), with a UINT8 RGB `[1,H,W,3]` input normalised in the graph and a UINT8 `[H,W]` class map output (`unet_io.py`) |
 | `PP-FormulaNet_plus-{M,S}_{encoder,prep,decoder_step}.onnx`, `_tokenizer.json` | formula recognition | [`PaddlePaddle/PP-FormulaNet_plus-M`](https://huggingface.co/PaddlePaddle/PP-FormulaNet_plus-M) / [`-S`](https://huggingface.co/PaddlePaddle/PP-FormulaNet_plus-S), split into encoder, cross-attention precompute and one greedy decoder step over a static KV cache |
 | `PP-DocLayoutV3_fp32_batchable_fp16.onnx`, `PP-OCRv6_{tiny,small,medium}_rec_ctc_fp16.onnx` | FP16 graphs for ONNX Runtime's TensorRT provider | derived from the FP32 file of this repository by `convert_fp16.py` (onnx 1.21.0, onnxconverter-common 1.16.0; I/O stay FP32) |
 

RapidTable_unet_uint8.onnx

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