Scraping Engine – Image and Data Scraping for AI Training
The project aimed to collect a comprehensive dataset of images and associated data from
various websites. The primary focus is on obtaining high-quality images for AI training
purposes, with a secondary emphasis on capturing relevant metadata related to the images.
Scope
Geographical focus: Primarily United States and Canada, with international sources as a
secondary priority
Types of images: Various categories, including but not limited to portraits, landscapes,
and object transformations
Data sources: Websites featuring image galleries, relevant online resources, and image
repositories
Deliverables Summary:
Web Scraper Development:
Developed a web scraper to efficiently extract images and data from targeted websites,
with search functionality for identifying relevant pages based on specific categories and
keywords.
Image Collection:
Gathered high-quality images, focusing on diverse transformations within each category,
and organized them for AI training, preserving their original quality.
Data Extraction:
Extracted and structured metadata linked to the images, using AI techniques for
normalization and creating mappings between images and their metadata.
Data Cleaning and Organization:
Conducted basic data cleaning, organized the data for AI training, prioritizing important
images and complete metadata.
Documentation and Reporting: Documented the web scraping process and challenges,
and provided a report detailing the dataset, including statistics on image count, category
distribution, and metadata quality.