How do fully convolutional networks work?

How do fully convolutional networks work?

The fully convolutional network first uses a CNN to extract image features, then transforms the number of channels into the number of classes via a 1×1 convolutional layer, and finally transforms the height and width of the feature maps to those of the input image via the transposed convolution.

What is a fully convolutional neural network?

A fully convolution network (FCN) is a neural network that only performs convolution (and subsampling or upsampling) operations. Equivalently, an FCN is a CNN without fully connected layers.

Can fully convolutional networks be used for object detection?

Mainstream object detectors based on the fully convolutional network has achieved impressive performance. While most of them still need a hand-designed non-maximum suppression (NMS) post-processing, which impedes fully end-to-end training.

What are fully connected layers?

Fully Connected layers in a neural networks are those layers where all the inputs from one layer are connected to every activation unit of the next layer. In most popular machine learning models, the last few layers are full connected layers which compiles the data extracted by previous layers to form the final output.

What are fully convolutional layers?

Fully Convolutional Networks, or FCNs, are an architecture used mainly for semantic segmentation. They employ solely locally connected layers, such as convolution, pooling and upsampling. Avoiding the use of dense layers means less parameters (making the networks faster to train).

What is RCNN?

Region Based Convolutional Neural Networks (R-CNN) are a family of machine learning models for computer vision and specifically object detection.

What does fully convolutional mean in a network?

Fully-convolutional implies that it doesn’t contain fully-connected layers, but only convolutional, max-pooling, and batch normalization layers all of which are invariant to the size of the image. Exactly this approach was proposed in this ground-breaking paper Fully Convolutional Networks for Semantic Segmentation.

How are convolutional neural nets used in biomedical imaging?

A similar approach was used in widely used U-Net: Convolutional Networks for Biomedical Image Segmentation, and many other architectures for object detection, pose estimation, and segmentation. Convolutional neural nets are not scale-invariant.

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.

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.