Object Detection
ONNX
PaddleOCR
English
Chinese
multilingual
onnxruntime
pp_doclayout_v3
PaddlePaddle
image-segmentation
ocr
layout
layout_detection
Instructions to use ningpp/PP-DocLayoutV3-ONNX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PaddleOCR
How to use ningpp/PP-DocLayoutV3-ONNX with PaddleOCR:
# 1. See https://www.paddlepaddle.org.cn/en/install to install paddlepaddle # 2. pip install paddleocr from paddleocr import LayoutDetection model = LayoutDetection(model_name="PP-DocLayoutV3-ONNX") output = model.predict(input="path/to/image.png", batch_size=1) for res in output: res.print() res.save_to_img(save_path="./output/") res.save_to_json(save_path="./output/res.json") - Notebooks
- Google Colab
- Kaggle
| { | |
| "activation_dropout": 0.0, | |
| "activation_function": "silu", | |
| "anchor_image_size": null, | |
| "architectures": [ | |
| "PPDocLayoutV3ForObjectDetection" | |
| ], | |
| "attention_dropout": 0.0, | |
| "backbone": null, | |
| "backbone_config": { | |
| "model_type": "hgnet_v2", | |
| "arch": "L", | |
| "return_idx": [0, 1, 2, 3], | |
| "freeze_stem_only": true, | |
| "freeze_at": 0, | |
| "freeze_norm": true, | |
| "lr_mult_list": [0, 0.05, 0.05, 0.05, 0.05], | |
| "out_features": ["stage1", "stage2", "stage3", "stage4"] | |
| }, | |
| "backbone_kwargs": null, | |
| "batch_norm_eps": 1e-05, | |
| "box_noise_scale": 1.0, | |
| "d_model": 256, | |
| "decoder_activation_function": "relu", | |
| "decoder_attention_heads": 8, | |
| "decoder_ffn_dim": 1024, | |
| "decoder_in_channels": [ | |
| 256, | |
| 256, | |
| 256 | |
| ], | |
| "decoder_layers": 6, | |
| "decoder_n_points": 4, | |
| "disable_custom_kernels": true, | |
| "dropout": 0.0, | |
| "encode_proj_layers": [ | |
| 2 | |
| ], | |
| "encoder_activation_function": "gelu", | |
| "encoder_attention_heads": 8, | |
| "encoder_ffn_dim": 1024, | |
| "encoder_hidden_dim": 256, | |
| "encoder_in_channels": [ | |
| 512, | |
| 1024, | |
| 2048 | |
| ], | |
| "encoder_layers": 1, | |
| "eos_coefficient": 0.0001, | |
| "eval_size": null, | |
| "feature_strides": [ | |
| 8, | |
| 16, | |
| 32 | |
| ], | |
| "hidden_expansion": 1.0, | |
| "id2label": { | |
| "0": "abstract", | |
| "1": "algorithm", | |
| "2": "aside_text", | |
| "3": "chart", | |
| "4": "content", | |
| "5": "formula", | |
| "6": "doc_title", | |
| "7": "figure_title", | |
| "8": "footer", | |
| "9": "footer", | |
| "10": "footnote", | |
| "11": "formula_number", | |
| "12": "header", | |
| "13": "header", | |
| "14": "image", | |
| "15": "formula", | |
| "16": "number", | |
| "17": "paragraph_title", | |
| "18": "reference", | |
| "19": "reference_content", | |
| "20": "seal", | |
| "21": "table", | |
| "22": "text", | |
| "23": "text", | |
| "24": "vision_footnote" | |
| }, | |
| "initializer_range": 0.01, | |
| "is_encoder_decoder": true, | |
| "label2id": {}, | |
| "label_noise_ratio": 0.5, | |
| "layer_norm_eps": 1e-05, | |
| "learn_initial_query": false, | |
| "matcher_alpha": 0.25, | |
| "matcher_bbox_cost": 5.0, | |
| "matcher_class_cost": 2.0, | |
| "matcher_gamma": 2.0, | |
| "matcher_giou_cost": 2.0, | |
| "model_type": "pp_doclayout_v3", | |
| "normalize_before": false, | |
| "num_denoising": 100, | |
| "num_feature_levels": 3, | |
| "num_queries": 300, | |
| "positional_encoding_temperature": 10000, | |
| "torch_dtype": "float32", | |
| "use_pretrained_backbone": false, | |
| "use_timm_backbone": false, | |
| "global_pointer_head_size": 64, | |
| "mask_feature_channels": [64, 64], | |
| "x4_feat_dim": 128 | |
| } | |