Label Studio & Hub: Semantic Segmentation Projects
Blog post from Activeloop
This tutorial demonstrates how to simplify semantic segmentation projects using Label Studio and Hub, focusing on identifying smiles in images. Semantic segmentation involves classifying each pixel in an image, and in this case, determining whether pixels belong to a "smiling" or "non-smiling" class. The project utilizes the GENKI-4K dataset, which contains 4000 face images labeled for smile presence. After filtering and selecting images labeled as "smiling," the tutorial guides users through labeling these images using Label Studio and exporting the labeled data. The data is then used to create a computer vision dataset compatible with Hub, facilitating the training of a semantic segmentation model, specifically a UNet model. The training process involves resizing images, ensuring binary mask values, and using TensorFlow to create a dataset for training the model. The tutorial includes instructions for setting up the model, compiling it with metrics like binary accuracy, and executing training with callbacks for optimization. Finally, the tutorial covers testing the model on non-labeled images, adjusting probability thresholds for predictions, and encourages further labeling and parameter optimization for improved results.
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