Digital Screen | Television Dataset

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Digital Screen | Television Dataset

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Digital Screen | Television Dataset

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Digital Screen | Television Dataset

Description

Explore the comprehensive TV image dataset from DataCluster Labs, featuring over 1000 HD images captured across 400+ cities in India. Perfect for smart home systems, retail analytics, and security monitoring, with annotations in COCO, YOLO, PASCAL-VOC, and Tf-Record formats.

Description:

The dataset comprises over 1000+ original Television/TV images, captured and crowdsourced from over 400+ urban and rural areas. Each image has been manually reviewed and verified by computer vision professionals at DataCluster Labs, ensuring high quality and accuracy. This dataset presents a significant challenge for machine learning models due to its diverse conditions and settings.

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Dataset Features

Size: 1000+ images
Contributors: Over 1000+ crowdsource contributors
Resolution: 98% of the images are HD and above (1920×1080 and higher)
Geographical Diversity: Captured across 400+ cities throughout India
Lighting Conditions: Includes various lighting scenarios such as day and night, and different distances and viewpoints
Device Used: Captured using mobile phones during 2022-2023
Usage: Ideal for enhancing real-world applications such as smart home systems, retail analytics, and security monitoring

Available Annotation Formats

COCO
YOLO
PASCAL-VOC
Tf-Record

Ownership and Licensing

DataCluster Labs exclusively owns the images in this dataset, which were not sourced from the internet. A license can be purchased to access a larger portion of the training dataset for research and commercial purposes.

Applications and Use Cases

Smart Home Systems: Improve the accuracy of TV detection and interaction in smart home environments.
Retail Analytics: Utilize the dataset to analyze consumer behavior and optimize in-store experiences.
Security Monitoring: Enhance security systems by improving the detection and recognition of TVs in various settings.
Computer Vision Research: Provide a challenging and diverse dataset for developing advanced computer vision algorithms.