This week I worked on:
- Software Integration of Visualizer
- Integrated all components of the visualizer API. It was running very slowly as there’s a fixed cost every time we use the ML model for classification. To fix this, I created batch classification methods. They increased the runtime per image from 4000 ms to 10 ms.
- Webscraper integration required an algorithm to determine how well two given labels (one for user’s clothes and one for online clothes) matched. I created my own algorithm that took a weighted average with more weight given to more confident labels. It also uses L2 error for color.
- Validation Testing for Visualizer
- Validation testing on the image and labels storage made me realize 1. storage hits were extremely unlikely because the keys was based on specific labels and color. and 2. the storage was too large to fit in memory.
- 1. color keys is now rounded to closest color our of 125. Only most confident label is used for storage.
- 2. Instead of an in-memory storage, it will be on-disk with memory-caching using DiskCache.
- Core functionality of Visualizer is quite good, as seen in interim demo.
- Validation testing on the image and labels storage made me realize 1. storage hits were extremely unlikely because the keys was based on specific labels and color. and 2. the storage was too large to fit in memory.
- Final Presentation
- started working on the final presentation.
For next week:
- Complete software integration with matching api, user preference model, and user interface.
- Complete software validation.
- Keep working on Final Presentation
I am on schedule.