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Official PyTorch implementation of DocLayout-YOLO.
We present DocLayout-YOLO, a real-time and robust layout detection model for diverse documents, based on YOLO-v10. This model is enriched with diversified document pre-training and structural optimization tailored for layout detection. In the pre-training phase, we introduce Mesh-candidate BestFit, viewing document synthesis as a two-dimensional bin packing problem, and create a large-scale diverse synthetic document dataset, DocSynth-300K. In terms of model structural optimization, we propose a module with Global-to-Local Controllability for precise detection of document elements across varying scales.
2024.10.25 🎉🎉 Mesh-candidate Bestfit code is released. Mesh-candidate Bestfit is a automatic pipeline which can synthesize large-scale, high-quality, and visually appealing document layout detection dataset. Tutorial and example data are available in here.
2024.10.23 🎉🎉 DocSynth300K dataset is released on 🤗Huggingface, DocSynth300K is a large-scale and diverse document layout analysis pre-training dataset, which can largely boost model performance.
2024.10.21 🎉🎉 Online demo available on 🤗Huggingface.
2024.10.18 🎉🎉 DocLayout-YOLO is implemented in PDF-Extract-Kit for document context extraction.
2024.10.16 🎉🎉 Paper now available on ArXiv.
Online Demo is now available. For local development, follow steps below:
Follow these steps to set up your environment:
conda create -n doclayout_yolo python=3.10
conda activate doclayout_yolo
pip install -e .
Note: If you only need the package for inference, you can simply install it via pip:
pip install doclayout-yolo
You can make predictions using either a script or the SDK:
Script
Run the following command to make a prediction using the script:
python demo.py --model path/to/model --image-path path/to/image
SDK
Here is an example of how to use the SDK for prediction:
import cv2
from doclayout_yolo import YOLOv10
# Load the pre-trained model
model = YOLOv10("path/to/provided/model")
# Perform prediction
det_res = model.predict(
"path/to/image", # Image to predict
imgsz=1024, # Prediction image size
conf=0.2, # Confidence threshold
device="cuda:0" # Device to use (e.g., 'cuda:0' or 'cpu')
)
# Annotate and save the result
annotated_frame = det_res[0].plot(pil=True, line_width=5, font_size=20)
cv2.imwrite("result.jpg", annotated_frame)
We provide model fine-tuned on DocStructBench for prediction, which is capable of handing various document types. Model can be downloaded from here and example images can be found under assets/example
.
Note: For PDF content extraction, please refer to PDF-Extract-Kit and MinerU.
Note: Thanks to NielsRogge, DocLayout-YOLO now supports implementation directly from 🤗Huggingface, you can load model as follows:
filepath = hf_hub_download(repo_id="juliozhao/DocLayout-YOLO-DocStructBench", filename="doclayout_yolo_docstructbench_imgsz1024.pt")
model = YOLOv10(filepath)
or directly load using from_pretrained
:
model = YOLOv10.from_pretrained("juliozhao/DocLayout-YOLO-DocStructBench")
more details can be found at this PR.
Note: Thanks to luciaganlulu, DocLayout-YOLO can perform batch inference and prediction. Instead of passing single image into model.predict
in demo.py
, pass a list of image path. Besides, due to batch inference is not implemented before YOLOv11
, you should manually change batch_size
in here.
Use following command to download dataset(about 113G):
from huggingface_hub import snapshot_download
# Download DocSynth300K
snapshot_download(repo_id="juliozhao/DocSynth300K", local_dir="./docsynth300k-hf", repo_type="dataset")
# If the download was disrupted and the file is not complete, you can resume the download
snapshot_download(repo_id="juliozhao/DocSynth300K", local_dir="./docsynth300k-hf", repo_type="dataset", resume_download=True)
If you want to perform DocSynth300K pretraining, using format_docsynth300k.py
to convert original .parquet
format into YOLO
format. The converted data will be stored at ./layout_data/docsynth300k
.
python format_docsynth300k.py
To perform DocSynth300K pre-training, use this command. We default use 8GPUs to perform pretraining. To reach optimal performance, you can adjust hyper-parameters such as imgsz
, lr
according to your downstream fine-tuning data distribution or setting.
Note: Due to memory leakage in YOLO original data loading code, the pretraining on large-scale dataset may be interrupted unexpectedly, use --pretrain last_checkpoint.pt --resume
to resume the pretraining process.
Find your ultralytics config file (for Linux user in $HOME/.config/Ultralytics/settings.yaml)
and change datasets_dir
to project root path.
./layout_data
:Dataset | Download |
---|---|
D4LA | link |
DocLayNet | link |
the file structure is as follows:
./layout_data
├── D4LA
│ ├── images
│ ├── labels
│ ├── test.txt
│ └── train.txt
└── doclaynet
├── images
├── labels
├── val.txt
└── train.txt
Training is conducted on 8 GPUs with a global batch size of 64 (8 images per device). The detailed settings and checkpoints are as follows:
Dataset | Model | DocSynth300K Pretrained? | imgsz | Learning rate | Finetune | Evaluation | AP50 | mAP | Checkpoint |
---|---|---|---|---|---|---|---|---|---|
D4LA | DocLayout-YOLO | ✗ | 1600 | 0.04 | command | command | 81.7 | 69.8 | checkpoint |
D4LA | DocLayout-YOLO | ✓ | 1600 | 0.04 | command | command | 82.4 | 70.3 | checkpoint |
DocLayNet | DocLayout-YOLO | ✗ | 1120 | 0.02 | command | command | 93.0 | 77.7 | checkpoint |
DocLayNet | DocLayout-YOLO | ✓ | 1120 | 0.02 | command | command | 93.4 | 79.7 | checkpoint |
The DocSynth300K pretrained model can be downloaded from here. Change checkpoint.pt
to the path of model to be evaluated during evaluation.
The code base is built with ultralytics and YOLO-v10.
Thanks for their great work!
If you find our project useful, please add a "star" to the repo. It's exciting to us when we see your interest, which keep us motivated to continue investing in the project!
@misc{zhao2024doclayoutyoloenhancingdocumentlayout,
title={DocLayout-YOLO: Enhancing Document Layout Analysis through Diverse Synthetic Data and Global-to-Local Adaptive Perception},
author={Zhiyuan Zhao and Hengrui Kang and Bin Wang and Conghui He},
year={2024},
eprint={2410.12628},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2410.12628},
}
@article{wang2024mineru,
title={MinerU: An Open-Source Solution for Precise Document Content Extraction},
author={Wang, Bin and Xu, Chao and Zhao, Xiaomeng and Ouyang, Linke and Wu, Fan and Zhao, Zhiyuan and Xu, Rui and Liu, Kaiwen and Qu, Yuan and Shang, Fukai and others},
journal={arXiv preprint arXiv:2409.18839},
year={2024}
}
FAQs
DocLayout-YOLO: an effecient and robust document layout analysis method.
We found that doclayout-yolo demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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