ML-TN-003 — AI at the edge: visual inspection of assembled PCBs for defect detection — Part 3
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Contents
History[edit | edit source]
| Version | Date | Notes |
|---|---|---|
| 1.0.0 | March 2021 | First public release |
Introduction[edit | edit source]
Building the dataset[edit | edit source]
Defects generation and acquisition[edit | edit source]
Class subdivision and labelling[edit | edit source]
Soldering regions extraction[edit | edit source]
Data augmentation with image synthesis[edit | edit source]
Generative adversarial networks[edit | edit source]
Progressive GAN implementation[edit | edit source]
Results validation[edit | edit source]
| Class | Synth image | Resolution (pixel) |
Google Colab (min) |
AWS SageMaker (min) |
|---|---|---|---|---|
missing |
full | 512 × 512 | ~480 | ~410 |
| upper/lower | 256 × 256 | ~435 | ~310 | |
tombstoning |
full | 512 × 512 | ~460 | ~390 |
| upper/lower | 256 × 256 | ~420 | ~300 |
Useful links[edit | edit source]
- Tero Karras, Timo Aila, Samuli Laine, Jaakko Lehtinen, Progressive Growing of GANs for Improved Quality, Stability, and Variation, February 2018.
- Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, Aaron Courville, Improved Training of Wasserstein GANs, December 2017.
