---
title: PP-LCNet_x1_0_table_cls_safetensors
canonical_url: "https://www.modelscope.cn/models/PaddlePaddle/PP-LCNet_x1_0_table_cls_safetensors"
md_url: "https://www.modelscope.cn/models/PaddlePaddle/PP-LCNet_x1_0_table_cls_safetensors.md"
repository: PaddlePaddle/PP-LCNet_x1_0_table_cls_safetensors
last_updated: 2026-04-15
license: apache-2.0
model_type:
  - pp_lcnet
parameters: 1.7M
tensor_type:
  - F32
library_name:
  - transformer
  - safetensors
language:
  - en
  - zh
downloads: 282
stars: 0
tags:
  - OCR
  - PaddlePaddle
  - PaddleOCR
  - wired_table_classification
---

# PP-LCNet_x1_0_table_cls_safetensors

> PP-LCNet_x1_0_table_cls_safetensors - PaddlePaddle 在 ModelScope 开源的模型。The Table Classification Module is a key component in computer vision systems, responsible for classifying input table images. The performance of this module directly affects the accuracy…

PaddlePaddle/PP-LCNet_x1_0_table_cls_safetensors 是 ModelScope 魔搭社区上的 1.7M 参数机器学习模型，采用 apache-2.0 许可。

- **Repository**: PaddlePaddle/PP-LCNet_x1_0_table_cls_safetensors
- **License**: apache-2.0
- **Parameters**: 1.7M
- **Tags**: OCR, PaddlePaddle, PaddleOCR, wired_table_classification
- **Downloads**: 282
- **Stars**: 0
- **Last updated**: 2026-04-15

Source: https://www.modelscope.cn/models/PaddlePaddle/PP-LCNet_x1_0_table_cls_safetensors

---

# PP-LCNet_x1_0_table_cls

## Introduction

The Table Classification Module is a key component in computer vision systems, responsible for classifying input table images. The performance of this module directly affects the accuracy and efficiency of the entire table recognition process. The Table Classification Module typically receives table images as input and, using deep learning algorithms, classifies them into predefined categories based on the characteristics and content of the images, such as wired and wireless tables. The classification results from the Table Classification Module serve as output for use in table recognition pipelines. The key metrics are as follow:

<table>
<tr>
<th>Model</th>
<th>Top1 Acc(%)</th>
<th>GPU Inference Time (ms)<br/>[Regular Mode / High-Performance Mode]</th>
<th>CPU Inference Time (ms)<br/>[Regular Mode / High-Performance Mode]</th>
<th>Model Storage Size (M)</th>
</tr>
<tr>
<td>PP-LCNet_x1_0_table_cls</td>
<td>94.2</td>
<td>2.35 / 0.47</td>
<td>4.03 / 1.35</td>
<td>6.6M</td>
</tr>
</table>

## Model Usage

```python
import requests
from PIL import Image
from transformers import AutoImageProcessor, AutoModelForImageClassification

model_path = "PaddlePaddle/PP-LCNet_x1_0_table_cls_safetensors"
model = AutoModelForImageClassification.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/table_recognition.jpg", stream=True).raw)
inputs = image_processor(images=image, return_tensors="pt").to(model.device)
outputs = model(**inputs)
predicted_label = outputs.logits.argmax(-1).item()
print(model.config.id2label[predicted_label])
```