I’m building a defect detection system for a manufacturing line. I don’t have 50,000 labeled images to train a model from scratch. Can I use Transfer Learning with a model like YOLO or EfficientNet? How much "domain-specific" data do I actually need to reach 95%+ accuracy if I start with a pre-trained backbone?
3 answers
Transfer Learning is a game-changer for industrial applications. By using a backbone trained on ImageNet, the model already knows how to detect edges, textures, and basic shapes. You only need to train the "head" of the network on your specific defect types. For 95% accuracy, you could potentially succeed with as few as 500 to 1,000 high-quality labeled images per class. The trick is to "freeze" the early layers and only train the final dense layers first. Once the loss stabilizes, you can "unfreeze" and fine-tune the entire network with a very low learning rate to adapt the features to your specific manufacturing environment.
Are the lighting conditions on your manufacturing line consistent? Transfer learning handles feature extraction well, but dramatic shifts in lighting or camera angles between your training set and production can ruin your accuracy.
YOLOv8 is currently the best for real-time speed. If you use the "small" or "nano" versions, the transfer learning process is incredibly fast, often finishing in just a few hours on a single GPU.
I agree with Jennifer. YOLOv8 is a beast for speed. I used it for a similar project and the trade-off between speed and accuracy was nearly non-existent after proper fine-tuning.
Richard, that is a great point. We have overhead LED lighting that can flicker. I’m thinking of adding "Random Brightness" and "Contrast" to my data augmentation pipeline to make the model more resilient. Do you think that’s enough, or should I consider a more complex preprocessing step to normalize every frame before it hits the model?