Earlier, I discussed animal segmentation and highlighted some of its benefits, one being data augmentation. By segmenting the foreground and removing the background, you can isolate animals and then apply various transformations (e.g., rotation, scaling, different scenarios) to diversify your dataset.
Of course, the ideal scenario is having a large, diverse dataset that naturally includes these variations, reducing the risk of overfitting on “perfect” images with limited diversity.
In this example, I isolated the foreground (a polar bear) and used this model fromHugging Face: https://lnkd.in/dmZxfF_5 to generate different backgrounds based on my original image. During the process, I also discovered an easy-to-use background removal tool that performed excellently on the few animal images I tested: BEN2 - https://lnkd.in/dF-BJkZc
Original polar bear image courtesy of Ingrid Brehm.
