--- title: PicoDet_layout_1x canonical_url: "https://www.modelscope.cn/models/PaddlePaddle/PicoDet_layout_1x" md_url: "https://www.modelscope.cn/models/PaddlePaddle/PicoDet_layout_1x.md" repository: PaddlePaddle/PicoDet_layout_1x last_updated: 2026-06-12 license: apache-2.0 language: - en - zh downloads: 108 stars: 0 tags: - OCR - PaddlePaddle - PaddleOCR - layout_detection --- # PicoDet_layout_1x > PicoDet_layout_1x - PaddlePaddle 在 ModelScope 开源的模型。A high-efficiency layout area localization model trained on a self-built dataset using PicoDet-1x, capable of detecting 5-Class english document area, including Text, Title, Table, Figure, and List. The key… PaddlePaddle/PicoDet_layout_1x 是 ModelScope 魔搭社区上的机器学习模型，采用 apache-2.0 许可。 - **Repository**: PaddlePaddle/PicoDet_layout_1x - **License**: apache-2.0 - **Tags**: OCR, PaddlePaddle, PaddleOCR, layout_detection - **Downloads**: 108 - **Stars**: 0 - **Last updated**: 2026-06-12 Source: https://www.modelscope.cn/models/PaddlePaddle/PicoDet_layout_1x --- # PicoDet_layout_1x ## Introduction A high-efficiency layout area localization model trained on a self-built dataset using PicoDet-1x, capable of detecting 5-Class english document area, including Text, Title, Table, Figure, and List. The key metrics are as follow: | Model| mAP(0.5) (%) | | --- | --- | |PicoDet_layout_1x | 97.8 | ## Quick Start ### Installation 1. PaddlePaddle Please refer to the following commands to install PaddlePaddle using pip: ```bash # for CUDA11.8 python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/ # for CUDA12.6 python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/ # for CPU python -m pip install paddlepaddle==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/ ``` For details about PaddlePaddle installation, please refer to the [PaddlePaddle official website](https://www.paddlepaddle.org.cn/en/install/quick). 2. PaddleOCR Install the latest version of the PaddleOCR inference package from PyPI: ```bash python -m pip install paddleocr ``` ### Model Usage You can quickly experience the functionality with a single command: ```bash paddleocr layout_detection \ --model_name PicoDet_layout_1x \ -i https://cdn-uploads.huggingface.co/production/uploads/63d7b8ee07cd1aa3c49a2026/-oU2IpNLcA0gTMJ34wDBR.png ``` You can also integrate the model inference of the layout detection module into your project. Before running the following code, please download the sample image to your local machine. ```python from paddleocr import LayoutDetection model = LayoutDetection(model_name="PicoDet_layout_1x") output = model.predict("-oU2IpNLcA0gTMJ34wDBR.png", batch_size=1, layout_nms=True) for res in output: res.print() res.save_to_img(save_path="./output/") res.save_to_json(save_path="./output/res.json") ``` After running, the obtained result is as follows: ```json {'res': {'input_path': '-oU2IpNLcA0gTMJ34wDBR.png', 'page_index': None, 'boxes': [{'cls_id': 0, 'label': 'Text', 'score': 0.9922339916229248, 'coordinate': [84.18926, 1248.3407, 583.2205, 1582.3639]}, {'cls_id': 0, 'label': 'Text', 'score': 0.991148829460144, 'coordinate': [606.0175, 1209.0277, 1105.669, 1538.559]}, {'cls_id': 0, 'label': 'Text', 'score': 0.989486813545227, 'coordinate': [84.11199, 710.821, 582.4992, 1208.4802]}, {'cls_id': 0, 'label': 'Text', 'score': 0.9823781847953796, 'coordinate': [605.9509, 951.964, 1105.8907, 1089.925]}, {'cls_id': 3, 'label': 'Table', 'score': 0.98123699426651, 'coordinate': [608.91614, 301.1981, 1111.2635, 805.45233]}, {'cls_id': 4, 'label': 'Figure', 'score': 0.971710741519928, 'coordinate': [84.329, 200.35394, 577.5106, 681.766]}, {'cls_id': 0, 'label': 'Text', 'score': 0.950641930103302, 'coordinate': [607.27435, 826.2146, 1107.0017, 911.43726]}, {'cls_id': 0, 'label': 'Text', 'score': 0.9468774795532227, 'coordinate': [605.6758, 197.66481, 1105.3107, 265.3606]}, {'cls_id': 1, 'label': 'Title', 'score': 0.86313396692276, 'coordinate': [606.6188, 1170.0265, 827.1196, 1192.0656]}, {'cls_id': 1, 'label': 'Title', 'score': 0.8427881002426147, 'coordinate': [605.2791, 1131.709, 823.0802, 1152.363]}, {'cls_id': 0, 'label': 'Text', 'score': 0.5990690588951111, 'coordinate': [625.3928, 1558.7576, 849.12946, 1583.5391]}]}} ``` The visualized image is as follows: ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/63d7b8ee07cd1aa3c49a2026/syzCGwEeqaZ6nqM_u8Qs5.png) For details about usage command and descriptions of parameters, please refer to the [Document](https://paddlepaddle.github.io/PaddleOCR/latest/en/version3.x/module_usage/layout_detection.html#iii-quick-integration). ### Pipeline Usage The ability of a single model is limited. But the pipeline consists of several models can provide more capacity to resolve difficult problems in real-world scenJust a few lines of code can experience the inference of the pipelinearios. #### PP-TableMagic (table_recognition_v2) The General Table Recognition v2 pipeline (PP-TableMagic) is designed to tackle table recognition tasks, identifying tables in images and outputting them in HTML format. PP-TableMagic includes the following 8 modules: * Table Structure Recognition Module * Table Classification Module * Table Cell Detection Module * Text Detection Module * Text Recognition Module * Layout Region Detection Module (optional) * Document Image Orientation Classification Module (optional) * Text Image Unwarping Module (optional) You can quickly experience the PP-TableMagic pipeline with a single command. ```bash paddleocr table_recognition_v2 -i https://cdn-uploads.huggingface.co/production/uploads/63d7b8ee07cd1aa3c49a2026/-oU2IpNLcA0gTMJ34wDBR.png \ --layout_detection_model_name PicoDet_layout_1x \ --use_doc_orientation_classify False \ --use_doc_unwarping False \ --save_path ./output \ --device gpu:0 ``` You can also integrate the PP-TableMagic pipeline into your project. Before running the following code, please download the sample image to your local machine. ```python from paddleocr import TableRecognitionPipelineV2 pipeline = TableRecognitionPipelineV2( layout_detection_model_name=PicoDet_layout_1x, use_doc_orientation_classify=False, # Use use_doc_orientation_classify to enable/disable document orientation classification model use_doc_unwarping=False, # Use use_doc_unwarping to enable/disable document unwarping module device="gpu:0", # Use device to specify GPU for model inference ) output = pipeline.predict("-oU2IpNLcA0gTMJ34wDBR.png") for res in output: res.print() ## Print the predicted structured output res.save_to_img("./output/") res.save_to_xlsx("./output/") res.save_to_html("./output/") res.save_to_json("./output/") ``` The default model used in pipeline is `PP-DocLayout-L`, so it is needed that specifing to `PicoDet_layout_1x` by argument `layout_detection_model_name`. And you can also use the local model file by argument `layout_detection_model_dir`. For details about usage command and descriptions of parameters, please refer to the [Document](https://paddlepaddle.github.io/PaddleOCR/main/en/version3.x/pipeline_usage/table_recognition_v2.html#2-quick-start). ## Links [PaddleOCR Repo](https://github.com/paddlepaddle/paddleocr) [PaddleOCR Documentation](https://paddlepaddle.github.io/PaddleOCR/latest/en/index.html)