Detailed introduction on how to use KerasCV DeepLabv3+ for semantic segmentation - complete code attached

This article discusses solving semantic segmentation problems by fine-tuning the KerasCV DeepLabv3+ model.

DeepLabv3+ is a popular semantic segmentation model that can be used in various applications of image segmentation, such as medical imaging, autonomous driving, etc. 

KerasCV also integrates DeepLabv3+ into its library. In this blog post, we will discuss extensively how to leverage DeepLabv3+ and fine-tune it based on our custom data. Specifically, we will use the following ImageNet pre-trained backbone as a feature extractor to fine-tune DeepLabv3+:

• ResNet50_V2
• EfficientNetv2_small

Finally, we will also compare the results of these models.