PaddlePaddle/RT-DETR-L_wired_table_cell_det_safetensors

RT-DETR-L_wired_table_cell_det

Introduction

The Table Cell Detection Module is a key component of the table recognition task, responsible for locating and marking each cell region in table images. The performance of this module directly affects the accuracy and efficiency of the entire table recognition process. The Table Cell Detection Module typically outputs bounding boxes for each cell region, which are then passed as input to the table recognition pipeline for further processing.

Model	Top1 Acc(%)	GPU Inference Time (ms) [Regular Mode / High-Performance Mode]	CPU Inference Time (ms) [Regular Mode / High-Performance Mode]	Model Storage Size (M)
RT-DETR-L_wired_table_cell_det	82.7	35.00 / 10.45	495.51 / 495.51	124M

Note: The accuracy of RT-DETR-L_wired_table_cell_det comes from the results of joint testing with RT-DETR-L_wireless_table_cell_det.

Model Usage

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

model_path = "PaddlePaddle/RT-DETR-L_wired_table_cell_det_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/table_recognition.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}")