How can I deal with images of variable dimensions when doing image?

How can I deal with images of variable dimensions when doing image?

A place to start is to look at these two notable papers: Feature Pyramid Networks for Object Detection and High-Resolution Representations for Labeling Pixels and Regions.

What should you do when working on image segmentation?

The first thing you should do when working on any machine learning problem (image segmentation, object detection included) is assessing quality and understanding your data. Common data problems when training Object Detection and Image Segmentation models include:

Why is class imbalance bad in image segmentation?

This resulted in huge class imbalance, a problem commonly faced in image segmentation. Why this is bad? This is bad because loss computed for majority class dominates over loss computed for minority class leading to a very low error signal.

When to use padding in image segmentation?

Padding will be necessary but to a degree that is manageable and will not blow the size of the dataset too much. Dataset with a lot of extreme values (very wide images mixed with very narrow ones) – this case is much more tricky and there are more advanced techniques to avoid excessive padding.

Which is the best network for image segmentation?

As you want to perform image segmentation, you can use U-Net, which does not have fully connected layers, but it is a fully convolutional network, which makes it able to handle inputs of any dimension. You should read the linked papers for more info.

Can a network accept images of any dimension?

This is usually the case if one has fully-connected layers, however, if the network is fully-convolutional, then it should be able to accept images of any dimension.

How to resize an image in a neural network?

Try resizing the image to the input dimensions of your neural network architecture (keeping it fixed to something like 128*128 in a standard 2D U-net architecture) using nearest neighbor interpolation technique. This is because if you resize your image using any other interpolation, it may result in tampering with the ground truth labels.

How does Multi-Output Model work in keras?

The first output layer structure is based on a single Dense layer, while the second output layer is constructed with two Dense layers. You are free to adjust and create any configuration, intermediate layers can be merged and split, this is the beauty of Keras functional API:

Can you feed the same image multiple times?

This paper suggests that in a segmentation task one can feed the same image multiple times to the network but with a different scale and then aggregate the results. If I understand this approach correctly, it would only work if all the input images have the same aspect ratio. Please correct me if I am wrong.

How to prepare the varied size input in CNN?

It is because when you define a CNN architecture, you plan as to how many layers you should have depending on the input size. Without having a fixed input shape, you cannot define architecture of your model. It is therefore necessary to convert all your images to same size. There is a way to include both image sizes.

How to pad input sequences for variable length?

Sequence Padding 1 Pre-Sequence Padding. The example below demonstrates pre-padding 3-input sequences with 0 values. 2 Post-Sequence Padding. Padding can also be applied to the end of the sequences, which may be more appropriate for some problem domains. 3 Pad Sequences To Length.

Can a network change the size of the input?

You can’t change the input size of a network. In other words, you can’t feed your network with different input sizes for convolutional networks. A typical solution for dealing with such situations is to resize the input. There are some ways to deal with it but they do not solve the problem well.