Resnet for image processing (3)
- 1. RESNET FOR IMAGE PROCESSING
- 2. WHAT IS RESNET ? ● ResNet, short for Residual Networks, is a classic neural network used as a backbone for many computer vision applications. ● ResNet makes it possible to train up to hundreds or even thousands of layers and still achieves compelling performance. Why Use ResNet? ● Humans can gather a wide variety of information from an image. our goal in computer vision is to make the same possible with computers or machines. ● A normal artificial neural network was found insufficient for such processing when there is a need to gather complex information
- 3. A picture can be considered as a matrix of numbers with each element representing a pixel value ( values from 1-225 ), which depicts the amount of a particular color. Why Use ResNet? A colored image is a stack of such red, green, and blue layers. The values of these pixels are fed into the neural network as inputs But when the quality of the image increases number of pixels increases which in turn increases the inputs nodes and this leads to overfitting. Adding the pixel values directly into the neural network can also cause loss of spatial data,
- 4. Convolutional layers are used to convolve the image into a smaller size using filters or kernels So we use convolutional neural networks To train a NN to identify fine details in an image we need to have more nodes or neurons but when increasing the number of nodes or neurons in a single layer it can cause overfitting. So we instead increase the number of layers with these nodes.
- 5. But very deep neural networks are difficult to train because of vanishing and exploding gradients Problems Resnet uses skip connections which allows take the activation from one layer and feed it to another layer much deeper in the neural network using this deeper neural network with even over 100 layers can be trained Ordinary Y=F(X) Y=f(x)+x
- 6. THANK YOU