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LICENSE

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+SAM License
+Last Updated: November 19, 2025
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README.md

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+---
+license: other
+library_name: sam3
+pipeline_tag: image-segmentation
+base_model: facebook/sam3.1
+datasets:
+  - adopd/adopd2026
+tags:
+  - document-ai
+  - sam3.1
+  - entity-segmentation
+---
+
+# SAM3.1 Segmentation ADOPD
+
+[Thinking with Anchors Project](https://sichenzhu.github.io/thinking-with-anchors/) | **ADOPD 2026 Paper:** *Thinking with Anchors: Grounded and Efficient Document Reasoning* | [ADOPD 2024 Paper](https://openreview.net/forum?id=x1ptaXpOYa) | [Dataset](https://huggingface.co/datasets/adopd/adopd2026) | [Code](https://github.com/SichenZhu/ADOPD2026)
+
+## Model Overview
+
+- **Model developer:** Thinking with Anchors project contributors
+- **Base architecture:** [SAM3](https://github.com/facebookresearch/sam3), initialized from the SAM3.1 checkpoint
+- **Task:** text-prompted document entity segmentation
+- **Fine-tuning dataset:** [adopd/adopd2026](https://huggingface.co/datasets/adopd/adopd2026)
+- **Input:** one RGB document image and the text prompt `entity`
+- **Output:** entity boxes, confidence scores, and instance masks
+
+## Description
+
+SAM3.1 Segmentation ADOPD uses the SAM3 image architecture initialized from the
+SAM3.1 checkpoint and fine-tuned to segment visual entities in document pages.
+The companion inference interface uses the fixed text prompt `entity` and
+returns class-agnostic instance predictions.
+
+## Training Data
+
+The checkpoint was fine-tuned on the ADOPD Doc2Mask task. Public supervision is
+stored in:
+
+```text
+human_annotated_masks[].polygons
+```
+
+The released adapter rasterizes all valid polygon components into
+full-resolution binary masks and creates native SAM3 training datapoints.
+
+## Checkpoint Format
+
+`model.pt` contains the official `detector.*` image-model state used for
+inference and weight initialization. It does not include optimizer, scheduler,
+scaler, or trainer state and cannot exactly resume an interrupted training run.
+
+## Quick Start
+
+```bash
+git clone https://github.com/SichenZhu/ADOPD2026.git
+cd ADOPD2026/release_code
+
+git clone https://github.com/facebookresearch/sam3.git upstream/sam3
+git -C upstream/sam3 checkout 5dd401d1c5c1d5c3eedff06d41b77af824517619
+
+python -m pip install -e 'upstream/sam3[train]'
+python -m pip install -e model_zoo/common
+python -m pip install -e model_zoo/sam3_1
+
+hf download adopd/SAM3.1-segmentation-ADOPD \
+  --local-dir checkpoints/sam3.1
+
+adopd-sam31-infer \
+  --checkpoint checkpoints/sam3.1/model.pt \
+  --image document.jpg \
+  --threshold 0.5 \
+  --output prediction.json
+```
+
+`prediction.json` contains pixel-space boxes, confidence scores, and
+uncompressed COCO-style RLE masks.
+
+## Fine-Tuning And Evaluation
+
+Use the data adapter, one-node DDP trainer, and sharded evaluation commands in
+[`sam3_1`](https://github.com/SichenZhu/ADOPD2026/tree/main/release_code/model_zoo/sam3_1).
+
+## Limitations
+
+The model predicts a single document-entity class and relies on the fixed
+prompt `entity`. Confidence and mask thresholds may require calibration for new
+document domains. Performance can vary with page resolution, language, scan
+quality, and visual style.
+
+## License
+
+Use is governed by the SAM license included in this repository and the licenses
+of the upstream SAM3 source and its dependencies.
+
+## 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}
+}
+```

model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7ad4f53bdeb3a773a991a3ef62f63651c9032939ca423a62af9d446f7ccb9784
+size 3371876017

model_config.json

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+{
+  "architecture": "sam3_image",
+  "release_variant": "sam3.1_finetune",
+  "task": "entity_segmentation",
+  "prompt": "entity",
+  "checkpoint_format": "sam3_official_detector_model_state",
+  "dataset": "adopd/adopd2026"
+}