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