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DocSynth300K

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YAML Metadata Warning: empty or missing yaml metadata in repo card (https://huggingface.co/docs/hub/datasets-cards)

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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

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Size of the auto-converted Parquet files (First 5GB):
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Estimated number of rows:
229,033

Models trained or fine-tuned on juliozhao/DocSynth300K