A Guide to Using U-Nets for Image Segmentation

You can easily try out different backbones by selecting them from the Component's Backbone setting. And other settings like Activation, Output Activation, Pooling, and Unpooling methods, can just as easily be experimented with in a similar manner. From there, it's just a matter of viewing the training and validation results in Perceptilabs' Statistics View as you experiment with different values as shown in Figure 8: The Statistics View shows real-time metrics including the predicted segmentation overlayed on ground truth (upper left) and the Intersection Over Union (IoU) (middle right) for validation and training across epochs. IoU is a great method to assess the model's accuracy. It goes beyond pixel accuracy (which can be unbalanced due to having more background than object-level pixels) by comparing how much the objects in the output overlap those in ground truth. You can also view this for the model's test data in PerceptiLabs' Test View as shown in Figure 9: Alternatively, you can build U-Nets from scratch in PerceptiLabs.