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*This model was released on 2025-10-16 and added to Hugging Face Transformers on 2026-02-27.*

# PP-DocLayoutV2

<div class="flex flex-wrap space-x-1">
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
</div>

## Overview

**PP-DocLayoutV2** is a dedicated lightweight model for layout analysis, focusing specifically on element detection, classification, and reading order prediction. 

## Model Architecture

PP-DocLayoutV2 is composed of two sequentially connected networks. The first is an RT-DETR-based detection model that performs layout element detection and classification. The detected bounding boxes and class labels are then passed to a subsequent pointer network, which is responsible for ordering these layout elements.

<div align="center">
<img src="https://huggingface.co/datasets/PaddlePaddle/PaddleOCR-VL_demo/resolve/main/imgs/PP-DocLayoutV2.png" width="800"/>
</div>

## Usage

### Single input inference

The example below demonstrates how to generate text with PP-DocLayoutV2 using [`Pipeline`] or the [`AutoModel`].

<hfoptions id="usage">
<hfoption id="Pipeline">

```py
import requests
from PIL import Image
from transformers import pipeline

image = Image.open(requests.get("https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/layout_demo.jpg", stream=True).raw)
layout_detector = pipeline("object-detection", model="PaddlePaddle/PP-DocLayoutV2_safetensors")
result = layout_detector(image)
print(result)
```

</hfoption>

<hfoption id="AutoModel">

```py
import requests
from PIL import Image
from transformers import AutoImageProcessor, AutoModelForObjectDetection

model_path = "PaddlePaddle/PP-DocLayoutV2_safetensors"
model = AutoModelForObjectDetection.from_pretrained(model_path)
image_processor = AutoImageProcessor.from_pretrained(model_path)
image = Image.open(requests.get("https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/layout_demo.jpg", stream=True).raw)
inputs = image_processor(images=image, return_tensors="pt")

outputs = model(**inputs)
results = image_processor.post_process_object_detection(outputs, target_sizes=[image.size[::-1]])
for result in results:
    print(result["scores"])
    print(result["labels"])
    print(result["boxes"])
    for idx, (score, label_id, box) in enumerate(zip(result["scores"], result["labels"], result["boxes"])):
        score, label = score.item(), label_id.item()
        box = [round(i, 2) for i in box.tolist()]
        print(f"Order {idx + 1}: {model.config.id2label[label]}: {score:.2f} {box}")
```

</hfoption>
</hfoptions>

### Batched inference

Here is how you can do it with PP-DocLayoutV2 using [`Pipeline`] or the [`AutoModel`]:

<hfoptions id="usage">
<hfoption id="Pipeline">

```py
import requests
from PIL import Image
from transformers import pipeline

image = Image.open(requests.get("https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/layout_demo.jpg", stream=True).raw)
layout_detector = pipeline("object-detection", model="PaddlePaddle/PP-DocLayoutV2_safetensors")
result = layout_detector([image, image])
print(result[0])
print(result[1])
```

</hfoption>

<hfoption id="AutoModel">

```py
import requests
import torch
from PIL import Image
from transformers import AutoImageProcessor, AutoModelForObjectDetection

model_path = "PaddlePaddle/PP-DocLayoutV2_safetensors"
model = AutoModelForObjectDetection.from_pretrained(model_path)
image_processor = AutoImageProcessor.from_pretrained(model_path)

image = Image.open(requests.get("https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/layout_demo.jpg", stream=True).raw)
inputs = image_processor(images=[image, image], return_tensors="pt")
target_sizes = [image.size[::-1], image.size[::-1]]

outputs = model(**inputs)
results = image_processor.post_process_object_detection(outputs, target_sizes=target_sizes)
for result in results:
    print("result:")
    for idx, (score, label_id, box) in enumerate(zip(result["scores"], result["labels"], result["boxes"])):
        score, label = score.item(), label_id.item()
        box = [round(i, 2) for i in box.tolist()]
        print(f"Order {idx + 1}: {model.config.id2label[label]}: {score:.2f} {box}")
```

</hfoption>
</hfoptions>

## PPDocLayoutV2Config

[[autodoc]] PPDocLayoutV2Config

## PPDocLayoutV2ForObjectDetection

[[autodoc]] PPDocLayoutV2ForObjectDetection

## PPDocLayoutV2Model

[[autodoc]] PPDocLayoutV2Model

## PPDocLayoutV2ReadingOrder

[[autodoc]] PPDocLayoutV2ReadingOrder

## PPDocLayoutV2ImageProcessor

[[autodoc]] PPDocLayoutV2ImageProcessor
    - preprocess
