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*This model was contributed to Hugging Face Transformers on 2026-01-29.*

# PP-DocLayoutV3


## Overview

**PP-DocLayoutV3** is a unified and high-efficiency model designed for comprehensive layout analysis. It addresses the challenges of complex physical distortions—such as skewing, curving, and adverse lighting—by integrating instance segmentation and reading order prediction into a single, end-to-end framework.

## Model Architecture

PP-DocLayoutV3 evolves from a traditional detection-based approach to a robust instance segmentation architecture built upon the RT-DETR framework. Instead of simple bounding boxes, it utilizes a mask-based detection head to predict pixel-accurate segments for layout elements. 

Unlike its predecessor, PP-DocLayoutV3 eliminates decoupled stages by embedding a Global Pointer Mechanism directly within the Transformer decoder layers. This allows the model to concurrently output classification labels, precise masks, and logical reading orders in a single forward pass, significantly reducing latency while enhancing parsing precision on complex document layouts.

## Usage

### Single input inference

The example below demonstrates how to generate text with PP-DocLayoutV3 using [`Pipeline`] or the [`AutoModel`].

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

```python
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-DocLayoutV3_safetensors")
results = layout_detector(image)
for idx, res in enumerate(results):
    print(f"Order {idx + 1}: {res}")
```

</hfoption>

<hfoption id="AutoModel">

```python
import requests
from PIL import Image

from transformers import AutoImageProcessor, AutoModelForObjectDetection


model_path = "PaddlePaddle/PP-DocLayoutV3_safetensors"
model = AutoModelForObjectDetection.from_pretrained(model_path, device_map="auto")
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").to(model.device)

outputs = model(**inputs)
results = image_processor.post_process_object_detection(outputs, target_sizes=[image.size[::-1]])
for result in results:
    for idx, (score, label_id, box, polygon_points) in enumerate(zip(result["scores"], result["labels"], result["boxes"], result["polygon_points"])):
        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: {score:.2f}, box: {box}, polygon_points: {polygon_points}")
```

</hfoption>
</hfoptions>

### Batched inference

PP-DocLayoutV3 also supports batched inference. Here is how you can do it with PP-DocLayoutV3 using [`Pipeline`] or the [`AutoModel`]:

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

```python
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-DocLayoutV3_safetensors")
results = layout_detector([image, image])
for result in results:
    print("result:")
    for idx, res in enumerate(result):
        print(f"Order {idx + 1}: {res}")
```

</hfoption>

<hfoption id="AutoModel">

```python
import requests
from PIL import Image

from transformers import AutoImageProcessor, AutoModelForObjectDetection


model_path = "PaddlePaddle/PP-DocLayoutV3_safetensors"
model = AutoModelForObjectDetection.from_pretrained(model_path, device_map="auto")
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").to(model.device)
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, polygon_points) in enumerate(zip(result["scores"], result["labels"], result["boxes"], result["polygon_points"])):
        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: {score:.2f}, box: {box}, polygon_points: {polygon_points}")
```

</hfoption>
</hfoptions>

## PPDocLayoutV3ForObjectDetection

[[autodoc]] PPDocLayoutV3ForObjectDetection
    - forward

## PPDocLayoutV3Model

[[autodoc]] PPDocLayoutV3Model
    
## PPDocLayoutV3Config

[[autodoc]] PPDocLayoutV3Config

## PPDocLayoutV3ImageProcessor

[[autodoc]] PPDocLayoutV3ImageProcessor
    - preprocess
