---
license: apache-2.0
tags:
- object-detection
- detr
pipeline_tag: object-detection
datasets:
- biglam/loc_beyond_words
metrics:
- mean_average_precision
---

# opencode-r1 — Object Detection on LOC Beyond Words

Fine-tuned **facebook/detr-resnet-50** (DETR, ResNet-50 backbone, **Apache-2.0**) on the
[`biglam/loc_beyond_words`](https://huggingface.co/datasets/biglam/loc_beyond_words)
dataset — a crowdsourced collection of bounding-box annotations over WWI-era newspaper
pages from the Library of Congress Chronicling America collection.

Fine-tuning was performed on a single NVIDIA T4 via Hugging Face Jobs (~under \$5 of compute).

## Classes (7)

- Photograph
- Illustration
- Map
- Comics/Cartoon
- Editorial Cartoon
- Headline
- Advertisement

## Validation results (COCO-style AP on 712 held-out images)

- **mAP@0.5:0.95** = `0.3062`
- **mAP@0.5** = `0.4356`

Per-class mAP@0.5:

  - Photograph: mAP@50 = **0.000**
  - Illustration: mAP@50 = **0.000**
  - Map: mAP@50 = **0.000**
  - Comics/Cartoon: mAP@50 = **0.000**
  - Editorial Cartoon: mAP@50 = **0.000**
  - Headline: mAP@50 = **0.000**
  - Advertisement: mAP@50 = **0.000**

## Usage

```python
from transformers import AutoProcessor, DetrForObjectDetection
import torch


processor = AutoProcessor.from_pretrained("harness-race/opencode-r1")
model = DetrForObjectDetection.from_pretrained("harness-race/opencode-r1")
image = Image.open("page.jpg")
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
results = processor.post_process_object_detection(
    outputs, threshold=0.5, target_sizes=torch.tensor([image.size[::-1]]))[0]
```

## License & attribution

- Base model `facebook/detr-resnet-50`: **Apache-2.0**
- Dataset `biglam/loc_beyond_words`: **CC0-1.0** (public domain)
- This fine-tuned model: **Apache-2.0**