stephenmcconnachie/0004-pdf-layout-detection
Layout detection with PP-DocLayout-L Bounding-box layout predictions for images from stephenmcconnachie/0004-pdf-pages-test, produced by PaddleOCR's PP-DocLayout-L. Processing details Source: stephenmcconnachie/0004-pdf-pages-test Model: PaddlePaddle/PP-DocLayout-L (~123M params (RT-DETR-L backbone)) Samples: 50 Processing time: 0.30 min Processing date: 2026-06-20 09:31 UTC Confidence threshold: 0.5 Layout NMS: on Output column: layout (JSON-encoded list of… See the full description on the dataset page: https://huggingface.co/datasets/stephenmcconnachie/0004-pdf-layout-detection.
Layout detection with PP-DocLayout-L
Bounding-box layout predictions for images from stephenmcconnachie/0004-pdf-pages-test, produced by PaddleOCR's PP-DocLayout-L.
Processing details
- Source: stephenmcconnachie/0004-pdf-pages-test
- Model: PaddlePaddle/PP-DocLayout-L (~123M params (RT-DETR-L backbone))
- Samples: 50
- Processing time: 0.30 min
- Processing date: 2026-06-20 09:31 UTC
- Confidence threshold: 0.5
- Layout NMS: on
- Output column:
layout(JSON-encoded list of detections)
Schema
Each row contains the original columns plus:
layout: JSON string. List of detections:
[
{"bbox": [x1, y1, x2, y2], "label": "text", "score": 0.97, "cls_id": 2},
{"bbox": [x1, y1, x2, y2], "label": "table", "score": 0.92, "cls_id": 5}
] Coordinates are in original input-image pixel space (top-left origin, [xmin, ymin, xmax, ymax]).
inference_info: JSON list tracking every model that has been applied to this dataset (appended on each run).
Usage
import json
from datasets import load_dataset
ds = load_dataset("{output_dataset_id}", split="train")
detections = json.loads(ds[0]["layout"])
for det in detections:
print(det["label"], det["score"], det["bbox"])Reproduction
hf jobs uv run --flavor l4x1 -s HF_TOKEN \
https://huggingface.co/datasets/uv-scripts/ocr/raw/main/pp-doclayout.py \
stephenmcconnachie/0004-pdf-pages-test <output> --model-name PP-DocLayout-LGenerated with UV Scripts.
