juliozhao/DocSynth300K

DocSynth300K is a large-scale and diverse document layout analysis pre-training dataset, which can largely boost model performance.

Data Download

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)

Data Formatting & Pre-training

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.

https://huggingface.co/papers/2410.12628