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
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
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