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license: apache-2.0
library_name: PaddleOCR
language:
- en
- zh
pipeline_tag: image-to-text
tags:
- OCR
- PaddlePaddle
- PaddleOCR
- wired_table_classification
---
# 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])
``` |