What is transfer learning in object detection?

What is transfer learning in object detection?

Transfer learning in object detection is a technique where a model developed for a specific task is reused as the starting point for a model on a second task.

How does it improve the performance of object detection models?

It allows models to leverage learned features from large datasets, reducing training time and improving accuracy on smaller datasets.

Can you give an example of how it's applied?

Sure! For instance, a model trained on the COCO dataset can be fine-tuned to detect specific objects in a custom dataset, like medical images.

What are the challenges associated with transfer learning?

Challenges include domain mismatch, where the source and target datasets differ significantly, and overfitting on small datasets.