Summary of Training a Computer Vision Model For Commercial Bakeries with Primarily Synthetic Images, by Thomas H. Schmitt et al.
Training a Computer Vision Model for Commercial Bakeries with Primarily Synthetic Images
by Thomas H. Schmitt, Maximilian Bundscherer, Tobias Bocklet
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a machine learning application that automates the tracking of returned baked goods in the food industry, building upon previous work by [SBB23]. To improve model robustness, the authors create an expanded dataset containing 2432 images and use generative models like pix2pix and CycleGAN to generate synthetic images. The authors then train state-of-the-art object detection models YOLOv9 and YOLOv8 on this task, achieving an average precision of 90.3% on the test set. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps make food production more efficient by using AI to track returned baked goods like bread buns. The authors created a big dataset with lots of images and used special computer programs to generate fake ones that look like real pictures. They trained two important machine learning models, YOLOv9 and YOLOv8, on this task and got very good results. |
Keywords
» Artificial intelligence » Machine learning » Object detection » Precision » Tracking
