add model card

README.md

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+---
+license: apache-2.0
+base_model: facebook/detr-resnet-50
+tags:
+  - object-detection
+  - transformers
+  - detr
+  - computer-vision
+  - text-detection
+  - historical-documents
+widget:
+  - enable_torchscript: false
+datasets:
+  - biglam/loc_beyond_words
+library_name: transformers
+pipeline_tag: object-detection
+model-index:
+  - name: opencode-r2
+    results:
+      - task:
+          type: object-detection
+          name: Object Detection
+        dataset:
+          type: biglam/loc_beyond_words
+          name: Beeyond Words (Beyond Words) fine-grained illustration detection
+          split: validation
+        metrics:
+          - type: mean_average_precision
+            value: 0.00
+            name: COCO mAP @ [0.5:0.95]
+          - type: coco_ap_50
+            value: 0.00
+            name: COCO AP@IoU0.5
+          - type: metric
+            value: 0.00
+            name: per-class mAP
+---
+
+# opencode-r2
+
+A fine-tuned object-detection model for fine-grained content detection in historical newspaper
+and periodical pages, built on the [Beyond Words](https://huggingface.co/datasets/biglam/loc_beyond_words)
+dataset (BigLam / Library of Congress). It detects the 7 page-layout content classes listed below.
+
+## Model
+
+- **Base model:** [`facebook/detr-resnet-50`](https://huggingface.co/facebook/detr-resnet-50) (DETR, **Apache-2.0**)
+- **Task:** Object detection (COCO-style bounding boxes)
+- **Classes (7):** `Photograph`, `Illustration`, `Map`, `Comics/Cartoon`, `Editorial Cartoon`, `Headline`, `Advertisement`
+
+## Dataset
+
+- **Source:** [`biglam/loc_beyond_words`](https://huggingface.co/datasets/biglam/loc_beyond_words)
+- **Training:** 2,846 examples
+- **Validation:** 712 examples
+
+## Training
+
+- Detector: DETR (`facebook/detr-resnet-50`) with the classification head re-initialized to 8 outputs (7 classes + no-object).
+- Loss: DETR set-prediction loss (classification + L1 box + GIoU).
+- Optimizer: AdamW (lr 1e-4), cosine/linear schedule with warmup, grad clip 1.0.
+- Image size: shortest edge 560 px (longest edge 800 px).
+- Backbone, transformer, and classifier all fine-tuned end-to-end.
+
+### Validation results
+
+| Metric | Value |
+|---|---|
+| COCO mAP @ IoU [0.5:0.95] | _TBD_ |
+| COCO AP @ IoU 0.5 | _TBD_ |
+| COCO AP @ IoU 0.75 | _TBD_ |
+| AR@maxDets=100 | _TBD_ |
+| Per-class mean AP | _TBD_ |
+
+**Validation results will be reported here after training completes.**
+
+## Usage
+
+```python
+from transformers import AutoImageProcessor, AutoModelForObjectDetection
+import torch
+from PIL import Image
+
+processor = AutoImageProcessor.from_pretrained("harness-race/opencode-r2")
+model = AutoModelForObjectDetection.from_pretrained("harness-race/opencode-r2")
+
+img = Image.open("page.png").convert("RGB")
+inputs = processor(images=img, return_tensors="pt")
+with torch.no_grad():
+    outputs = model(**inputs)
+
+target_sizes = torch.tensor([[img.height, img.width]])
+results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.5)[0]
+for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+    box = [round(i, 1) for i in box.tolist()]
+    print(f"{model.config.id2label[label.item()]}: {round(score.item(),3)} {box}")
+```
+
+## License and attribution
+
+- The fine-tuned weights in this repository are released under **Apache-2.0** (same as the base DETR model), so the model is free to share and use.
+- Base detector: *End-to-End Object Detection with Transformers* (Carion et al., 2020).
+- Data: Beyond Words (Library of Congress) via `biglam/loc_beyond_words`.
+
+## Caveats
+
+- Model was fine-tuned on a single GPU with a limited training budget; results reflect that constraint.
+- Detection resolution and accuracy trade-offs exist for very small text blocks.