The release of several powerful, open-source foundational models coupled with advancements in fine-tuning have brought about a new paradigm in machine learning and artificial intelligence. At the…

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The post describes how to fine-tune Meta’s Segment Anything Model (SAM) for segmenting high fidelity masks in various domains, using the example of river pixel segmentation. It covers the project requirements, the architecture of SAM, configuring prompts, and the training process. The post offers practical advice on dataset management, leveraging Google Colab and GCP for training, and discusses different prompt types for better segmentation results.

Learn Transformer Fine-Tuning and Segment Anything