PP-DocBlockLayout
Introduction
A layout block localization model trained on a self-built dataset containing Chinese and English papers, PPT, multi-layout magazines, contracts, books, exams, ancient books and research reports using RT-DETR-L. The layout detection model includes 1 category: Region.
| Model | mAP(0.5) (%) |
|---|---|
| PP-DocBlockLayout | 95.9 |
Note: the evaluation set of the above precision indicators is the self built version sub area detection data set, including Chinese and English papers, magazines, newspapers, research reports PPT、 1000 document type pictures such as test papers and textbooks.
Model Usage
import requests
from PIL import Image
from transformers import AutoImageProcessor, AutoModelForObjectDetection
model_path = "PaddlePaddle/PP-DocBlockLayout_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.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:
for score, label_id, box in 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"{model.config.id2label[label]}: {score:.2f} {box}")