When should I use transfer learning instead of training a model from scratch?

All answers to this question.

With only 500 images, transfer learning is almost certainly the better choice. Training a deep neural network from scratch requires thousands, if not millions, of data points to learn basic features like edges and textures. By using a model pre-trained on ImageNet (like ResNet or EfficientNet), you inherit those low-level features and only need to fine-tune the final layers to your specific classes. This significantly reduces training time and computational cost. The only drawback is if your data is extremely different from the pre-training set (e.g., medical X-rays vs. standard photos).

   Answered 2025-02-07 by Martha Bradley

Do you plan on freezing all the initial layers, or will you allow some of them to be updated during the fine-tuning process?

   Answered 2025-02-09 by Kevin Malone

• That’s a crucial question, Kevin. For such a small dataset, Cynthia should definitely keep the base layers frozen to prevent "catastrophic forgetting." Only the newly added dense layers at the top should be trained initially. Once the top layers have stabilized, she could potentially unfreeze the later convolutional blocks and use a very small learning rate to fine-tune the entire stack without destroying the pre-learned features.

     Commented 2025-02-10 by Jason Perry

Transfer learning is a lifesaver for startups. It allows you to get state-of-the-art accuracy with very little data. I rarely train from scratch these days unless I'm doing research.

   Answered 2025-02-11 by Gary Simmons

• Exactly, Gary. It's the most efficient way to build a proof of concept. Plus, frameworks like Keras and PyTorch make it so easy to import these models with just one line of code.

     Commented 2025-02-12 by Martha Bradley