---
title: docling-layout-egret-xlarge
canonical_url: "https://www.modelscope.cn/models/ds4sd/docling-layout-egret-xlarge"
md_url: "https://www.modelscope.cn/models/ds4sd/docling-layout-egret-xlarge.md"
repository: ds4sd/docling-layout-egret-xlarge
last_updated: 2026-08-03
license: apache-2.0
model_type:
  - d_fine
architectures:
  - DFineForObjectDetection
parameters: 62.7M
tensor_type:
  - F32
  - I64
library_name:
  - pytorch
  - transformer
  - safetensors
frameworks:
  - pytorch
downloads: 285
stars: 0
---

# docling-layout-egret-xlarge

> docling-layout-egret-xlarge - ds4sd 在 ModelScope 开源的模型。Document Layout Analysis "egret-xlarge"

ds4sd/docling-layout-egret-xlarge 是 ModelScope 魔搭社区上的 62.7M 参数机器学习模型，采用 apache-2.0 许可。

- **Repository**: ds4sd/docling-layout-egret-xlarge
- **License**: apache-2.0
- **Parameters**: 62.7M
- **Downloads**: 285
- **Stars**: 0
- **Last updated**: 2026-08-03

Source: https://www.modelscope.cn/models/ds4sd/docling-layout-egret-xlarge

---

# Document Layout Analysis "egret-xlarge"

🚀 **`egret-xlarge`** is a Document Layout Analysis Model used in the [Docling project](https://github.com/docling-project/docling).

📄 For an in-depth description of the model architecture, training datasets, and evaluation methodology, please refer to our technical report: **"Advanced Layout Analysis Models for Docling"**, Nikolaos Livathinos *et al.*, [🔗 https://arxiv.org/abs/2509.11720](https://arxiv.org/abs/2509.11720)


## Inference code example

Prerequisites:

```bash
pip install transformers Pillow torch requests
```

Prediction:

```python
import requests
from transformers import (
    DFineForObjectDetection,
    RTDetrImageProcessor,
)
import torch
from PIL import Image


classes_map = {
    0: "Caption",
    1: "Footnote",
    2: "Formula",
    3: "List-item",
    4: "Page-footer",
    5: "Page-header",
    6: "Picture",
    7: "Section-header",
    8: "Table",
    9: "Text",
    10: "Title",
    11: "Document Index",
    12: "Code",
    13: "Checkbox-Selected",
    14: "Checkbox-Unselected",
    15: "Form",
    16: "Key-Value Region",
}
image_url = "https://huggingface.co/spaces/ds4sd/SmolDocling-256M-Demo/resolve/main/example_images/annual_rep_14.png"
model_name = "ds4sd/docling-layout-egret-xlarge"
threshold = 0.6

# Download the image
image = Image.open(requests.get(image_url, stream=True).raw)
image = image.convert("RGB")


# Initialize the model
image_processor = RTDetrImageProcessor.from_pretrained(model_name)
model = DFineForObjectDetection.from_pretrained(model_name)

# Run the prediction pipeline
inputs = image_processor(images=[image], return_tensors="pt")
with torch.no_grad():
    outputs = model(**inputs)
results = image_processor.post_process_object_detection(
    outputs,
    target_sizes=torch.tensor([image.size[::-1]]),
    threshold=threshold,
)

# Get the results
for result in results:
    for score, label_id, box in zip(
        result["scores"], result["labels"], result["boxes"]
    ):
        score = round(score.item(), 2)
        label = classes_map[label_id.item()]
        box = [round(i, 2) for i in box.tolist()]
        print(f"{label}:{score} {box}")
```


## References

```
@misc{livathinos2025advancedlayoutanalysismodels,
      title={advanced layout analysis models for docling},
      author={nikolaos livathinos and christoph auer and ahmed nassar and rafael teixeira de lima and maksym lysak and brown ebouky and cesar berrospi and michele dolfi and panagiotis vagenas and matteo omenetti and kasper dinkla and yusik kim and valery weber and lucas morin and ingmar meijer and viktor kuropiatnyk and tim strohmeyer and a. said gurbuz and peter w. j. staar},
      year={2025},
      eprint={2509.11720},
      archiveprefix={arxiv},
      primaryclass={cs.cv},
      url={https://arxiv.org/abs/2509.11720},
}

@techreport{Docling,
  author = {Deep Search Team},
  month = {8},
  title = {Docling Technical Report},
  url = {https://arxiv.org/abs/2408.09869v4},
  eprint = {2408.09869},
  doi = {10.48550/arXiv.2408.09869},
  version = {1.0.0},
  year = {2024}
}
```