Control-R1: Doc-layout object detection (fine-tuned DETR)
This model is a fine-tuned DETR (ResNet-50) object-detection model for document /
newspaper page-layout analysis. It detects 7 region types in scanned historical
newspaper pages and was trained on the biglam/loc_beyond_words dataset.
Base model & license
- Base model:
facebook/detr-resnet-50 - License: Apache-2.0 (open license — free to share, modify and use commercially)
- The fine-tuned weights in this repository inherit the Apache-2.0 license.
Classes (7)
Photograph, Illustration, Map, Comics/Cartoon, Editorial Cartoon, Headline, Advertisement
Training data
- Dataset: biglam/loc_beyond_words
- Train: 2846 images
- Validation: 712 images
- COCO-style annotations (xywh bounding boxes).
Training details
- Model:
facebook/detr-resnet-50, all parameters fine-tuned (backbone unfrozen) - Epochs: 4
- Batch size: 4 (gradient accumulation = 2)
- Optimizer: AdamW (lr = 0.0001, weight decay on non-bias/LN params)
- LR schedule: linear warmup + cosine decay
- Image preprocessing: resize to shortest edge = 800px, longest = 1333px (aspect preserved), ImageNet normalization
- Mixed precision: bf16/fp16 autocast (GPU)
- Hardware: Hugging Face Jobs GPU
Validation results (COCO eval on the 712-image validation split)
| Metric | Value |
|---|---|
| mAP (IoU 0.50:0.95) | 0.1658 |
| AP @ IoU 0.50 | 0.2761 |
| AP @ IoU 0.75 | 0.1802 |
| AR (max 100 dets) | 0.2908 |
Per-class AP (IoU 0.50:0.95):
- Photograph: 0.3484
- Illustration: 0.0000
- Map: 0.0000
- Comics/Cartoon: 0.0385
- Editorial Cartoon: 0.0000
- Headline: 0.4418
- Advertisement: 0.3321
Quick usage
from transformers import AutoModelForObjectDetection, AutoImageProcessor
from PIL import Image
model = AutoModelForObjectDetection.from_pretrained("harness-race/control-r1")
processor = AutoImageProcessor.from_pretrained("harness-race/control-r1")
image = Image.open("page.png").convert("RGB")
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
results = processor.post_process_object_detection(outputs, target_sizes=[(image.height, image.width)], threshold=0.4)
for r in results[0]:
print(model.config.id2label[int(r['labels'])], round(r['scores'].item(),3) if hasattr(r['scores'],'item') else r['scores'], [round(c,1) for c in r['boxes'].tolist()])
Intended use & limitations
Trained for research on historical newspaper layout analysis. Best on page layouts similar
to the loc_beyond_words training distribution; large format/styled pages not seen in
training may be missed. Evaluation was done on the dataset's official 712-image validation
split; run at 2026-08-07 (UTC) on HF Jobs.
Control-R1 — a layout-model entry. Trained on Hugging Face Jobs (< $5 budget).