huggingface--transformers/docs/source/en/model_doc/pp_doclayout_v2.md

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].

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)

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]:

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])

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