Can I use Transfer Learning to build a high-performing Image Classifier with only 500 images?

All answers to this question.

Transfer Learning is exactly what you need here. Even though ResNet was trained on dogs and cars, the early layers of the network learned to detect "edges" and "textures," which are universal across all images. You just need to "Freeze" those early layers and only train the final few layers on your specific defect images. In 2024, we used this for a medical imaging project with only 300 samples and achieved over 90% accuracy. The key is "Data Augmentation"—flip, rotate, and zoom into your 500 images to artificially create a larger dataset for the model to study.

   Answered 2025-03-21 by Alice Vance

Don't forget to check your "Class Balance." If 450 images are "Good" and only 50 are "Defective," the model will just learn to call everything "Good."

   Answered 2025-03-25 by Janet Peterson

• Great point, Janet. With such a small set, even a slight imbalance can completely derail the training. Oversampling the "Defect" class is a must here.

     Commented 2025-03-26 by Joyce Gardner

Are you planning to use a "Learning Rate Finder" to ensure you don't overwrite the pre-trained weights too quickly during the fine-tuning phase?

   Answered 2025-05-23 by Philip Duncan

• Using a very small learning rate is vital for when doing transfer learning. If the rate is too high, you’ll suffer from "Catastrophic Forgetting," where the model loses all the useful generic features it learned from the big dataset. I’d also suggest looking into "Discriminative Fine-Tuning," where you use different learning rates for different layers. This allows the final layers to adapt quickly to your manufacturing parts while the foundational layers stay stable, preserving the essential geometric knowledge the model already possesses.

     Commented 2025-03-24 by Roy Chapman