Generation of labeled images for semantic segmentation

In order to use the U-Net architecture for object detection and segmentation with precise outlines you need to label each pixel with a number. 1,2,3,… for each segmented region. Creating this labeld data manualy is unfeasable, but there are several segmentation algorithms out there. Which algorithm is more suitable for this task? What is a common workflow to generate these Y labels for each X input image in your training data?

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Hi @Jaime3ddev , welcome to this community!
Here is something that may help you.
A typical workflow to generate the labels for each input image would be:

  1. Annotate a set of training images with the desired segmentations, either manually or using an annotation tool such as Labelbox or RectLabel.
  2. Preprocess the annotated images and convert the annotations into the desired format, e.g., one-hot encoding for each pixel.
  3. Train the segmentation algorithm using the annotated images and their corresponding labels as input.
  4. Validate the trained model using a separate set of validation images and evaluate the performance metrics.
  5. If necessary, fine-tune the model using additional annotated data or hyperparameter tuning.
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