How Do We see?

Tuesday 13th February 

Computer vision, a field of artificial intelligence, empowers machines with the ability to interpret and understand visual information. In 'The Bin Project,' computer vision is harnessed to analyze images of food waste, providing a comprehensive understanding of the types and quantities of discarded items. This technology goes beyond traditional methods, enabling automated and real-time analysis, which is crucial for large-scale waste management.Imagine you have a big jigsaw puzzle with lots of pieces, and you want a computer to figure out how the pieces fit together without telling it what the final picture looks like. Traditional methods, like using Convolutional Neural Networks (CNNs), are like looking at small sections of the puzzle at a time.

Now, think of Vision Transformer (ViT) systems as a smarter way to solve this puzzle. Instead of focusing on just small parts, ViTs are like having the ability to look at the entire puzzle at once and understand how each piece relates to the others. They do this using something called "self-attention mechanisms," which helps them see the big picture and how all the parts work together.

This is super helpful in tasks like unsupervised classification, where the computer needs to figure out patterns and features in the data without someone telling it what each piece is. ViTs are like puzzle masters—they can understand the relationships between different elements of the puzzle, even if they've never seen that exact puzzle before. So, in 'The Bin Project,' using ViTs helps the computer quickly and smartly identify and categorize different types of food waste without needing a lot of training or specific examples beforehand.

The 'The Bin Project' faces the challenge of dealing with diverse and ever-changing types of food waste, making unsupervised classification a key requirement. Unsupervised learning allows the system to identify and categorize waste items without prior labeled examples, adapting dynamically to new waste patterns. Vision Transformer systems excel in unsupervised scenarios, as their self-attention mechanisms facilitate learning intricate relationships within the data.

See more information at our site at https://thebinproject.org/