foduucom/table-detection-yolo

Dataset Labels

['bordered', 'borderless']

Number of Images

{'test': 34, 'train': 238, 'valid': 70}

How to Use

• Install datasets:

pip install datasets

• Load the dataset:

from datasets import load_dataset
ds = load_dataset("foduucom/table-detection-yolo", name="full")
example = ds['train'][0]

Dataset Summary

Certainly! Here's a dataset summary for your dataset of images containing tables that are categorized as border and borderless, provided in YOLO format:

Dataset Summary

The Table Detection Dataset is a curated collection of images, each depicting tables that are classified as either 'bordered' or 'borderless'. The dataset is provided in YOLO format, featuring annotations for accurate object detection and classification. It serves as a valuable resource for researchers, developers, and practitioners working on table detection tasks, with a specific focus on distinguishing between tables with distinct visual characteristics.

Key Features:

• Image Variety: The dataset encompasses a diverse range of images, capturing tables from various real-world scenarios and environments.
• Annotation Precision: Each image is meticulously annotated with bounding box coordinates and class labels, indicating whether the table is 'bordered' or 'borderless'.
• YOLO Format: Annotations follow the YOLO format, making it suitable for training and evaluating object detection models.
• Research and Development: The dataset is designed to facilitate advancements in table detection algorithms and technologies, enabling the development of models capable of accurately identifying and classifying different types of tables.

Whether you are working on document analysis, data extraction, or image-based content recognition, the Table Detection Dataset provides an essential foundation for enhancing the capabilities of object detection models in identifying tables with varying visual attributes. By offering a comprehensive collection of border and borderless tables, this dataset empowers the AI community to tackle challenges in table detection across a wide range of applications.

For more details and access to the dataset, please refer to [email protected] .