*This model was contributed to Hugging Face Transformers on 2026-02-27.*
# PP-DocLayoutV2
## 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.
## Usage
### Single input inference
The example below demonstrates how to generate text with PP-DocLayoutV2 using [`Pipeline`] or the [`AutoModel`].
```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-DocLayoutV2_safetensors")
result = layout_detector(image)
print(result)
```
```python
import requests
from PIL import Image
from transformers import AutoImageProcessor, AutoModelForObjectDetection
model_path = "PaddlePaddle/PP-DocLayoutV2_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:
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}")
```
### Batched inference
Here is how you can do it with PP-DocLayoutV2 using [`Pipeline`] or the [`AutoModel`]:
```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-DocLayoutV2_safetensors")
result = layout_detector([image, image])
print(result[0])
print(result[1])
```
```python
import requests
from PIL import Image
from transformers import AutoImageProcessor, AutoModelForObjectDetection
model_path = "PaddlePaddle/PP-DocLayoutV2_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) 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}")
```
## PPDocLayoutV2Config
[[autodoc]] PPDocLayoutV2Config
## PPDocLayoutV2ForObjectDetection
[[autodoc]] PPDocLayoutV2ForObjectDetection
## PPDocLayoutV2Model
[[autodoc]] PPDocLayoutV2Model
## PPDocLayoutV2ReadingOrder
[[autodoc]] PPDocLayoutV2ReadingOrder
## PPDocLayoutV2ImageProcessor
[[autodoc]] PPDocLayoutV2ImageProcessor
- preprocess