You can deploy this model and start earning money today!

adopd/
RF-DETR-Seg-2XL-segmentation-ADOPD

Name: RF-DETR-Seg-2XL-segmentation-ADOPD
Availability: InStock

Views

No views yet

RF-DETR-Seg 2XL Segmentation ADOPD

Thinking with Anchors Project | ADOPD 2026 Paper: Thinking with Anchors: Grounded and Efficient Document Reasoning | ADOPD 2024 Paper | Dataset | Code

Use Restrictions

Non-commercial research use only. The ADOPD fine-tuned checkpoint weights in this repository are provided solely for non-commercial research. Commercial use of these checkpoint weights is not permitted. Users must also comply with every applicable upstream license and acceptable-use term; see USE_RESTRICTIONS.md.

Model Overview

Model developer: Thinking with Anchors project contributors
Base architecture: RF-DETR
Architecture variant: RF-DETR-Seg 2XL
Task: class-agnostic document entity segmentation
Fine-tuning dataset: adopd/adopd2026
Input: one RGB document image
Output: entity boxes, confidence scores, and instance masks

Description

RF-DETR-Seg 2XL Segmentation ADOPD is an RF-DETR instance-segmentation model fine-tuned to identify visual entities in document pages. It predicts a single entity class and retains every polygon component associated with each source mask during conversion to COCO supervision.

Training Data

The checkpoint was fine-tuned on the ADOPD Doc2Mask task. Public supervision is stored in:

human_annotated_masks[].polygons

The released data adapter converts these polygons to RF-DETR's COCO training layout without using the independent legacy annotation layers.

Checkpoint Format

model.ckpt is a PyTorch Lightning checkpoint for the seg-2xlarge architecture. It is not an RFDETR.from_checkpoint() export. The architecture must therefore be supplied when loading the model through the companion code.

Quick Start

bash

git clone https://github.com/SichenZhu/ADOPD2026.git
cd ADOPD2026/release_code

git clone https://github.com/roboflow/rf-detr.git upstream/rf-detr
git -C upstream/rf-detr checkout 7f2490d4ece5a894b6bfe69e876a1d5d9936e2e1

python -m pip install -e model_zoo/common
python -m pip install -e 'upstream/rf-detr[train,loggers]'
python -m pip install -e model_zoo/rf_detr

hf download adopd/RF-DETR-Seg-2XL-segmentation-ADOPD \
  --local-dir checkpoints/rfdetr-seg-2xl

adopd-rfdetr-infer \
  --checkpoint checkpoints/rfdetr-seg-2xl/model.ckpt \
  --architecture seg-2xlarge \
  --image document.jpg \
  --threshold 0.5 \
  --output prediction.json

prediction.json contains pixel-space boxes, confidence scores, and masks.

Fine-Tuning And Evaluation

Prepare ADOPD2026 with adopd-rfdetr-prepare --task segment, then follow the training and evaluation commands in rf_detr.

Limitations

The model predicts a single document-entity class rather than semantic entity types. Confidence and mask thresholds may require calibration when document styles, languages, resolutions, or scan quality differ from the fine-tuning data.

License

The ADOPD fine-tuned checkpoint weights are subject to the non-commercial, research-only restriction above. The included Apache License 2.0 documents the terms applicable to upstream RF-DETR software components. The RF-DETR base model and installed dependencies retain their own terms. Use is permitted only when all applicable terms are satisfied.

Citation

Please cite the ADOPD 2026 and ADOPD 2024 papers.

bibtex

@misc{zhu2026thinkingwithanchors,
  title={Thinking with Anchors: Grounded and Efficient Document Reasoning},
  author={Sichen Zhu and Yuchen Zhu and Wenzhuo Xu and Jason Kuen and Wanrong Zhu and Jing Shi and Xuan Shen and Quanyi Wang and Yiwei Wang and Yujun Cai and Bing Shuai and Qin Zhang and Yongxin Chen and Shilong Liu and Molei Tao and Jiuxiang Gu},
  year={2026}
}

bibtex

@inproceedings{gu2024adopd,
  title={{ADOPD}: A Large-Scale Document Page Decomposition Dataset},
  author={Jiuxiang Gu and Xiangxi Shi and Jason Kuen and Lu Qi and Ruiyi Zhang and Anqi Liu and Ani Nenkova and Tong Sun},
  booktitle={The Twelfth International Conference on Learning Representations},
  year={2024},
  url={https://openreview.net/forum?id=x1ptaXpOYa}
}