---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- pytorch
- transformers
- object-detection
- vision
- detr
- document-layout
- newspaper
pipeline_tag: object-detection
base_model: facebook/detr-resnet-50
datasets:
- biglam/loc_beyond_words
model-index:
- name: control-r3
  results:
  - task:
      type: object-detection
      name: Object Detection
    dataset:
      name: biglam/loc_beyond_words (validation)
      type: biglam/loc_beyond_words
      split: validation
      num_examples: 712
    metrics:
      - type: Average Precision (COCO)
        value: 24.65
        name: mAP
      - type: Average Precision @ IoU=0.5
        value: 34.76
        name: mAP@50
      - type: Average Precision @ IoU=0.75
        value: 28.39
        name: mAP@75
      - type: Average Recall max 100 detections
        value: 37.94
        name: mAR@100
---

# control-r3 — DETR object detection for news-page layout regions

This model is a **fine-tuned `DetrForObjectDetection`** (DETR with a ResNet-50 backbone,
starting from [`facebook/detr-resnet-50`](https://huggingface.co/facebook/detr-resnet-50))
tuned to localize **layout regions in historical and modern newspaper pages**.
It predicts bounding boxes for the 7 classes of the
[BigLam *“Locating Objects Beyond Words”*](https://huggingface.co/datasets/biglam/loc_beyond_words) dataset:

| class id | label |
|----------|-------|
| 0 | Photograph |
| 1 | Illustration |
| 2 | Map |
| 3 | Comics/Cartoon |
| 4 | Editorial Cartoon |
| 5 | Headline |
| 6 | Advertisement |

The base model [`facebook/detr-resnet-50`](https://huggingface.co/facebook/detr-resnet-50) is
released under the **Apache 2.0** license, so this fine-tune can be freely shared and used.

## Model Details

- **Model type:** DETR (DEtection TRansformer), encoder-decoder Transformer with a ResNet-50 backbone.
- **Backbone:** ResNet-50 (provided by `timm`), pretrained on ImageNet.
- **Number of queries:** 100.
- **Input:** RGB image resized/padded to a square of `600 × 600` after a *smallest-max-size* resize.
- **Output:** per-image box predictions with class logits for the 7 layout classes (plus the implicit `no object` class).
- **Optimized by:** fine-tuned on the training split (2,846 images) of `biglam/loc_beyond_words`.
- **Base model license:** Apache 2.0.

## Intended Use

The model is intended for **document / newspaper-page layout analysis**: given a scan or a page image,
it detects coarse layout regions such as headlines, photographs/illustrations, advertisements, maps and comics.
It is designed as a layout-region detector and is not meant for fine-grained text recognition or OCR
(use OCR/HTR systems for reading text).

Example inference:

```python
from transformers import pipeline

detector = pipeline("object-detection", model="harness-race/control-r3")
results = detector("path/to/newspaper_page.png")
# results: list of {label, score, box: {xmin, ymin, xmax, ymax}}
```

## Training Data

- **Dataset:** [`biglam/loc_beyond_words`](https://huggingface.co/datasets/biglam/loc_beyond_words)
  (BigLam “Locating Objects Beyond Words”, a Library-of-Congress-derived newspaper layout dataset).
- **Training split:** 2,846 images.
- **Validation split:** 712 images (reported results are on this held-out split).
- **Format:** COCO-style bounding boxes `[x, y, width, height]` in pixel coordinates.
- The data was repackaged (only renaming annotation fields for compatibility) into
  [`harness-race/loc_beyond_words_coco`](https://huggingface.co/datasets/harness-race/loc_beyond_words_coco);
  no annotations were modified.

## Training Procedure

The model was fine-tuned end-to-end (all weights trainable) with the Hugging Face `Trainer`-style loop
on a single NVIDIA T4 GPU (fp16 AMP), with the RGB images resized and padded to `600 × 600` and light
augmentation (horizontal flip, random brightness/contrast, hue/saturation, random crop with box clipping).

### Hyperparameters

- optimizer: AdamW (betas 0.9/0.999, eps 1e-8)
- learning rate: 1e-4 with linear schedule + 10% warmup
- weight decay: 1e-4
- train batch size: 4
- eval batch size: 8
- epochs: 8
- mixed precision: fp16 (native AMP)
- image size: 600 × 600 (resize + pad)
- gradient clipping: 1.0
- seed: 42

Evaluation is run at the end of every epoch and the checkpoint with the best validation `mAP` is kept.

## Evaluation Results

Reported on the **validation split (712 images)**, using COCO-style metrics
(`torchmetrics.MeanAveragePrecision`, `box_format=xyxy`). Metrics are in %:

| Metric | Value |
|--------|-------|
| mAP (IoU .5:.95) | **24.65** |
| mAP @ IoU 0.50 | 34.76 |
| mAP @ IoU 0.75 | 28.39 |
| mAR@100 | 37.94 |

**Per-class mAP (IoU .5:.95):**

| Class | mAP |
|-------|------|
| Photograph | 39.02 |
| Illustration | 1.17 |
| Map | 0.03 |
| Comics/Cartoon | 13.21 |
| Editorial Cartoon | 0.00 |
| Headline | 59.51 |
| Advertisement | 59.64 |

The model detects **Headline** and **Advertisement** regions very well (>59 mAP) and detects
**Photograph** and **Comics/Cartoon** reasonably. The rare classes (**Illustration**, **Map**,
**Editorial Cartoon**) show very low mAP, which is largely a consequence of heavy class imbalance in the
dataset (e.g. only ~215 Map and ~293 Editorial Cartoon object instances across the whole dataset vs
~27.9k Headline instances). More data or class-balancing/oversampling for those classes would improve them.

The raw per-epoch metrics are stored in [`val_metrics.json`](val_metrics.json) in this repository.

## Bias, Risks, and Limitations

- Performance is strongly skewed by class imbalance; rare layout classes are unreliable.
- Detector quality on page layouts/engravings/styles outside the training distribution may degrade.
- Predictions should be reviewed when used in archival/curation workflows (layout regions may span
  multiple semantic zones; bounding boxes are approximate).
- This is a layout-region detector, not an OCR/HTR model.

## Technical Notes

- Architecture: `DetrForObjectDetection`, 100 queries, 6 encoder + 6 decoder layers, d_model=256.
- Repository files: `config.json`, `model.safetensors`, `preprocessor_config.json`
  (`DetrImageProcessorFast`, `size=600`), `val_metrics.json`, `train_detr.py` (training script).

## Acknowledgements

Based on the DETR model ([Carion et al., 2020](https://arxiv.org/abs/2005.12872)) and the
Transformers library. Dataset from *BigLam* and the Library of Congress newspaper collections.