Summary of Towards Sim-to-real Industrial Parts Classification with Synthetic Dataset, by Xiaomeng Zhu et al.
Towards Sim-to-Real Industrial Parts Classification with Synthetic Dataset
by Xiaomeng Zhu, Talha Bilal, Pär Mårtensson, Lars Hanson, Mårten Björkman, Atsuto Maki
First submitted to arxiv on: 12 Apr 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 This paper explores the use of synthetic data to train deep neural networks for classifying industrial parts. The authors introduce a new synthetic dataset, SIP-17, which contains 17 objects with varying levels of complexity and realism. They benchmark five state-of-the-art models, both supervised and self-supervised, trained only on the synthetic data against real-world images. The results show that while there is room for improvement, synthetic data can be effective in industrial parts classification, especially when domain randomization is applied. This study highlights the importance of understanding the limitations and challenges of using synthetic data for this task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at a new way to teach computers to recognize industrial parts, like gears or screws. Instead of using real pictures, they create fake ones that are similar but not exactly the same. This helps them learn how to classify real images more accurately. The authors tested five different computer programs on this synthetic data and found that it can be pretty good at recognizing parts. They also discovered some challenges with using fake data, but think it’s a useful tool for learning. |
Keywords
* Artificial intelligence * Classification * Self supervised * Supervised * Synthetic data
