The problem Cleaner Unkle solves

In layman's terms, this smart sorting system cuts through the hassle of figuring out what goes where. Here are a few ways to describe the problem it solves, depending on the tone you want:

Casual: Tired of sorting trash into the right bin? This system does the brainwork for you!
Pain Point: Spending ages sorting stuff is a drag. This system makes it a breeze.
Efficiency focus: Wanna streamline your sorting process? This is your secret weapon.

Challenges we ran into

Challenges Encountered During Development

Developing this smart sorting system using CNN and computer vision presented a number of technical hurdles that required innovative solutions. One of the primary challenges was achieving robust object recognition. Training the CNN model to accurately identify a wide range of objects from various viewpoints and under different lighting conditions demanded a significant amount of diverse training data.

Additionally, integrating the computer vision system with the physical sorting mechanism necessitated careful consideration of real-world factors. Potential variations in object presentation, such as occlusions or unconventional positions, had to be addressed to ensure accurate sorting functionality.

Finally, balancing the computational efficiency of the CNN model with its classification accuracy was crucial. Optimizing the model for real-time deployment while maintaining a high degree of accuracy presented a challenge that required careful parameter selection and potentially hardware considerations.