|  | |
| 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): | |
| ```python | |
| 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```. | |
| ```bash | |
| python format_docsynth300k.py | |
| ``` | |
| To perform DocSynth300K pre-training, use this [command](assets/script.sh#L2). 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 |