Upload PP-DocLayoutV3 ONNX for layout-service (ORT CPU)
Browse files- PP-DocLayoutV3.onnx +3 -0
- README.md +130 -0
- config.json +107 -0
- inference.yml +100 -0
- preprocessor_config.json +36 -0
PP-DocLayoutV3.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:b0deee066f8b71e6f8ae3a645c242f1985c4b66e6e3332e7d72cae774a7f70ac
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size 142486972
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README.md
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---
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license: apache-2.0
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pipeline_tag: object-detection
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tags:
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- PaddleOCR
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- PaddlePaddle
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- image-segmentation
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- ocr
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- layout
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- layout_detection
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language:
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- en
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- zh
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- multilingual
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library_name: transformers
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base_model:
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- PaddlePaddle/PP-DocLayoutV3
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---
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<div align="center">
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<h1 align="center">
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Unified Layout Module for PaddleOCR-VL 1.5/1.6 & GLM-OCR
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</h1>
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[](https://github.com/PaddlePaddle/PaddleOCR)
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[](https://huggingface.co/PaddlePaddle/PP-DocLayoutV3)
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[](https://modelscope.cn/models/PaddlePaddle/PP-DocLayoutV3)
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[](https://huggingface.co/spaces/PaddlePaddle/PaddleOCR-VL-1.5_Online_Demo)
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[](https://modelscope.cn/studios/PaddlePaddle/PaddleOCR-VL-1.5_Online_Demo/summary)
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[](https://discord.gg/JPmZXDsEEK)
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[](https://x.com/PaddlePaddle)
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[](./LICENSE)
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**🔥 [Official Website](https://www.paddleocr.com)** |
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**📝 [Technical Report](https://arxiv.org/abs/2606.23344)**
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</div>
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## Introduction
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This is the model weights for PP-DocLayoutv3 in safetensors format. Get PaddlePaddle weights at [PP-DocLayoutV3](https://huggingface.co/PaddlePaddle/PP-DocLayoutV3)
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**PP-DocLayoutV3 is specifically engineered to handle non-planar document images. It can directly predict multi-point bounding boxes for layout elements—as opposed to standard two-point boxes—and determine logical reading orders for skewed and curved surfaces within a single forward pass, significantly reducing cascading errors.** This model is an essential component of PaddleOCR-VL-1.5, providing crucial layout analysis for the high-precision parsing of various real-world documents in PaddleOCR-VL.
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This work has been accepted to ECCV 2026! 🎉
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### **Model Architecture**
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<div align="center">
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<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/PP-DocLayoutV3.png" width="800"/>
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</div>
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## Model Usage
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```python
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import requests
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from PIL import Image
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from transformers import AutoImageProcessor, AutoModelForObjectDetection
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model_path = "PaddlePaddle/PP-DocLayoutV3_safetensors"
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model = AutoModelForObjectDetection.from_pretrained(model_path)
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image_processor = AutoImageProcessor.from_pretrained(model_path)
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image = Image.open(requests.get("https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/layout_demo.jpg", stream=True).raw)
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inputs = image_processor(images=image, return_tensors="pt")
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outputs = model(**inputs)
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results = image_processor.post_process_object_detection(outputs, target_sizes=[image.size[::-1]])
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for result in results:
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for idx, (score, label_id, box, polygon_points) in enumerate(zip(result["scores"], result["labels"], result["boxes"], result["polygon_points"])):
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score, label = score.item(), label_id.item()
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box = [round(i, 2) for i in box.tolist()]
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print(f"Order {idx + 1}: {model.config.id2label[label]}, score: {score:.2f}, box: {box}, polygon_points: {polygon_points}")
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```
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## Visualization
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### Light Variation
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<div align="center">
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<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/layout_lighting.jpg" width="800"/>
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</div>
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### Skewing
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<div align="center">
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<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/layout_skew.jpg" width="800"/>
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</div>
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### Screen-photo
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<div align="center">
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<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/layout_screen.jpg" width="800"/>
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</div>
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### Curving
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<div align="center">
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<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/layout_curv.jpg" width="800"/>
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</div>
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## Citation
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If you find PP-DocLayoutV3 helpful, feel free to give us a star and citation.
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```bibtex
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@misc{cui2026rtdoclayoutrealtimeendtoenddocument,
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title={RT-DocLayout: Real-Time End-to-End Document Layout Analysis with Reading Order in the Wild},
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author={Cheng Cui and Tingquan Gao and Xueqing Wang and Changda Zhou and Hongen Liu and Ting Sun and Yubo Zhang and Zelun Zhang and Jiaxuan Liu and Manhui Lin and Yue Zhang and Suyin Liang and Yiqing Xiang and Yi Liu},
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year={2026},
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eprint={2606.23344},
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archivePrefix={arXiv},
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/2606.23344},
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}
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}
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```
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config.json
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{
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"activation_dropout": 0.0,
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"activation_function": "silu",
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"anchor_image_size": null,
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"architectures": [
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"PPDocLayoutV3ForObjectDetection"
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],
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"attention_dropout": 0.0,
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"backbone": null,
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"backbone_config": {
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"model_type": "hgnet_v2",
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"arch": "L",
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"return_idx": [0, 1, 2, 3],
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| 14 |
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"freeze_stem_only": true,
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"freeze_at": 0,
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"freeze_norm": true,
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| 17 |
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"lr_mult_list": [0, 0.05, 0.05, 0.05, 0.05],
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"out_features": ["stage1", "stage2", "stage3", "stage4"]
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},
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"backbone_kwargs": null,
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| 21 |
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"batch_norm_eps": 1e-05,
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"box_noise_scale": 1.0,
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"d_model": 256,
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"decoder_activation_function": "relu",
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| 25 |
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"decoder_attention_heads": 8,
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"decoder_ffn_dim": 1024,
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"decoder_in_channels": [
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256,
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256,
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256
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],
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"decoder_layers": 6,
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"decoder_n_points": 4,
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"disable_custom_kernels": true,
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"dropout": 0.0,
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"encode_proj_layers": [
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2
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],
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| 39 |
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"encoder_activation_function": "gelu",
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"encoder_attention_heads": 8,
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"encoder_ffn_dim": 1024,
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| 42 |
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"encoder_hidden_dim": 256,
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| 43 |
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"encoder_in_channels": [
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512,
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1024,
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2048
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],
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| 48 |
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"encoder_layers": 1,
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"eos_coefficient": 0.0001,
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"eval_size": null,
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"feature_strides": [
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8,
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16,
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32
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],
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| 56 |
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"hidden_expansion": 1.0,
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| 57 |
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"id2label": {
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"0": "abstract",
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"1": "algorithm",
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"2": "aside_text",
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"3": "chart",
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"4": "content",
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"5": "formula",
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"6": "doc_title",
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"7": "figure_title",
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"8": "footer",
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| 67 |
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"9": "footer",
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"10": "footnote",
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"11": "formula_number",
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| 70 |
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"12": "header",
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| 71 |
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"13": "header",
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"14": "image",
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| 73 |
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"15": "formula",
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| 74 |
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"16": "number",
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| 75 |
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"17": "paragraph_title",
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| 76 |
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"18": "reference",
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| 77 |
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"19": "reference_content",
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| 78 |
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"20": "seal",
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| 79 |
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"21": "table",
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| 80 |
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"22": "text",
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| 81 |
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"23": "text",
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| 82 |
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"24": "vision_footnote"
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| 83 |
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},
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| 84 |
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"initializer_range": 0.01,
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| 85 |
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"is_encoder_decoder": true,
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| 86 |
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"label2id": {},
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| 87 |
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"label_noise_ratio": 0.5,
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| 88 |
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"layer_norm_eps": 1e-05,
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| 89 |
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"learn_initial_query": false,
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| 90 |
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"matcher_alpha": 0.25,
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| 91 |
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"matcher_bbox_cost": 5.0,
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| 92 |
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"matcher_class_cost": 2.0,
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| 93 |
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"matcher_gamma": 2.0,
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| 94 |
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"matcher_giou_cost": 2.0,
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| 95 |
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"model_type": "pp_doclayout_v3",
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| 96 |
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"normalize_before": false,
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| 97 |
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"num_denoising": 100,
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| 98 |
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"num_feature_levels": 3,
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| 99 |
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"num_queries": 300,
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| 100 |
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"positional_encoding_temperature": 10000,
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| 101 |
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"torch_dtype": "float32",
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| 102 |
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"use_pretrained_backbone": false,
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| 103 |
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"use_timm_backbone": false,
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| 104 |
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"global_pointer_head_size": 64,
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| 105 |
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"mask_feature_channels": [64, 64],
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"x4_feat_dim": 128
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}
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inference.yml
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|
| 1 |
+
mode: paddle
|
| 2 |
+
draw_threshold: 0.5
|
| 3 |
+
metric: COCO
|
| 4 |
+
use_dynamic_shape: false
|
| 5 |
+
Global:
|
| 6 |
+
model_name: PP-DocLayoutV3
|
| 7 |
+
arch: DETR
|
| 8 |
+
min_subgraph_size: 3
|
| 9 |
+
Preprocess:
|
| 10 |
+
- interp: 2
|
| 11 |
+
keep_ratio: false
|
| 12 |
+
target_size:
|
| 13 |
+
- 800
|
| 14 |
+
- 800
|
| 15 |
+
type: Resize
|
| 16 |
+
- mean:
|
| 17 |
+
- 0.0
|
| 18 |
+
- 0.0
|
| 19 |
+
- 0.0
|
| 20 |
+
norm_type: none
|
| 21 |
+
std:
|
| 22 |
+
- 1.0
|
| 23 |
+
- 1.0
|
| 24 |
+
- 1.0
|
| 25 |
+
type: NormalizeImage
|
| 26 |
+
- type: Permute
|
| 27 |
+
label_list:
|
| 28 |
+
- abstract
|
| 29 |
+
- algorithm
|
| 30 |
+
- aside_text
|
| 31 |
+
- chart
|
| 32 |
+
- content
|
| 33 |
+
- display_formula
|
| 34 |
+
- doc_title
|
| 35 |
+
- figure_title
|
| 36 |
+
- footer
|
| 37 |
+
- footer_image
|
| 38 |
+
- footnote
|
| 39 |
+
- formula_number
|
| 40 |
+
- header
|
| 41 |
+
- header_image
|
| 42 |
+
- image
|
| 43 |
+
- inline_formula
|
| 44 |
+
- number
|
| 45 |
+
- paragraph_title
|
| 46 |
+
- reference
|
| 47 |
+
- reference_content
|
| 48 |
+
- seal
|
| 49 |
+
- table
|
| 50 |
+
- text
|
| 51 |
+
- vertical_text
|
| 52 |
+
- vision_footnote
|
| 53 |
+
Hpi:
|
| 54 |
+
backend_configs:
|
| 55 |
+
paddle_infer:
|
| 56 |
+
trt_dynamic_shapes: &id001
|
| 57 |
+
image:
|
| 58 |
+
- - 1
|
| 59 |
+
- 3
|
| 60 |
+
- 800
|
| 61 |
+
- 800
|
| 62 |
+
- - 1
|
| 63 |
+
- 3
|
| 64 |
+
- 800
|
| 65 |
+
- 800
|
| 66 |
+
- - 8
|
| 67 |
+
- 3
|
| 68 |
+
- 800
|
| 69 |
+
- 800
|
| 70 |
+
scale_factor:
|
| 71 |
+
- - 1
|
| 72 |
+
- 2
|
| 73 |
+
- - 1
|
| 74 |
+
- 2
|
| 75 |
+
- - 8
|
| 76 |
+
- 2
|
| 77 |
+
trt_dynamic_shape_input_data:
|
| 78 |
+
scale_factor:
|
| 79 |
+
- - 2
|
| 80 |
+
- 2
|
| 81 |
+
- - 1
|
| 82 |
+
- 1
|
| 83 |
+
- - 0.67
|
| 84 |
+
- 0.67
|
| 85 |
+
- 0.67
|
| 86 |
+
- 0.67
|
| 87 |
+
- 0.67
|
| 88 |
+
- 0.67
|
| 89 |
+
- 0.67
|
| 90 |
+
- 0.67
|
| 91 |
+
- 0.67
|
| 92 |
+
- 0.67
|
| 93 |
+
- 0.67
|
| 94 |
+
- 0.67
|
| 95 |
+
- 0.67
|
| 96 |
+
- 0.67
|
| 97 |
+
- 0.67
|
| 98 |
+
- 0.67
|
| 99 |
+
tensorrt:
|
| 100 |
+
dynamic_shapes: *id001
|
preprocessor_config.json
ADDED
|
@@ -0,0 +1,36 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_valid_processor_keys": [
|
| 3 |
+
"images",
|
| 4 |
+
"do_resize",
|
| 5 |
+
"size",
|
| 6 |
+
"resample",
|
| 7 |
+
"do_rescale",
|
| 8 |
+
"rescale_factor",
|
| 9 |
+
"do_normalize",
|
| 10 |
+
"image_mean",
|
| 11 |
+
"image_std",
|
| 12 |
+
"return_tensors",
|
| 13 |
+
"data_format",
|
| 14 |
+
"input_data_format"
|
| 15 |
+
],
|
| 16 |
+
"do_normalize": true,
|
| 17 |
+
"do_rescale": true,
|
| 18 |
+
"do_resize": true,
|
| 19 |
+
"image_mean": [
|
| 20 |
+
0,
|
| 21 |
+
0,
|
| 22 |
+
0
|
| 23 |
+
],
|
| 24 |
+
"image_processor_type": "PPDocLayoutV3ImageProcessor",
|
| 25 |
+
"image_std": [
|
| 26 |
+
1,
|
| 27 |
+
1,
|
| 28 |
+
1
|
| 29 |
+
],
|
| 30 |
+
"resample": 3,
|
| 31 |
+
"rescale_factor": 0.00392156862745098,
|
| 32 |
+
"size": {
|
| 33 |
+
"height": 800,
|
| 34 |
+
"width": 800
|
| 35 |
+
}
|
| 36 |
+
}
|