The presentation discusses a project on bread identification at different junction points in a food production company. The speaker shares insights on how to approach a business problem, choose the right architecture, and automate data generation and annotation.
- Understanding the problem statement and the data generation process is crucial
- Automating boring tasks like data annotation can save time and effort
- Choosing the right augmentation techniques is important for business use cases
- Using pre-trained models can be advantageous
- The speaker shares a story on how they automated the data annotation process for bread identification
The speaker shares how they used awk transform and control logic to split images into sub-images and avoid false positives while automating the data annotation process. They also used a pre-trained model, Mask R-CNN, to segment the bread images and extract data. This reduced manual annotation by 60-70% and made the process more efficient.