Transfer Learning: Don't Train Vision Models from Scratch
Don't train a vision model from scratch. Transfer learning reuses a model trained on a huge dataset (like ImageNet) as a starting point for your specific task. This lets you achieve high accuracy on new image types with much less data and compute.
Don't train a computer vision model from scratch; stand on the shoulders of giants. Transfer learning takes a model pre-trained on a massive, general dataset and adapts it for a new, related task. For example, a model that recognizes cars can be fine-tuned to specialize in recognizing trucks. The common mistake is assuming you need a million images; transfer learning lets you achieve state-of-the-art results with a much smaller, targeted dataset.
Read the original → Wikipedia: Transfer learning
- #computer vision
- #machine learning
- #deep learning
- #fine-tuning
Get five bites like this every day.
Tezvyn delivers a daily feed of 60-second tech bites with quizzes to lock in what you learn.