The article discusses the training of Meta’s Segment Anything Model (SAM) for high fidelity mask segmentation in any domain. It highlights the use of powerful foundational models and fine-tuning techniques, particularly focusing on the transformer model. This approach has democratized the development of accurate domain-specific models, even for researchers with limited resources.
The article explores the application of SAM for river pixel segmentation using remote sensing data. It details the process of dataset preparation, including the creation of a suitable dataset and the use of HuggingFace for data storage and retrieval. The article also delves into the architecture of SAM, emphasizing the image encoder, prompt encoder, and mask decoder components.
Training SAM involves freezing the image and prompt encoders while updating the mask decoder weights. The article provides insights into the generation of prompts, the dataset loader creation, and the training loop setup. It also discusses the performance improvements achieved through fine-tuning SAM for river segmentation tasks.
The training process involves loading the model, freezing specific parameters, and training the model for the desired number of iterations. The article concludes with a discussion on the improved segmentation performance achieved through fine-tuning SAM, showcasing the model’s ability to generalize from imperfect training data.
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Source link: https://towardsdatascience.com/learn-transformer-fine-tuning-and-segment-anything-481c6c4ac802