Evaluation metrics play an important role in achieving the optimal model during model training. It is a way to quantify the performance of the Machine learning Model. For simplicity, I'll be focusing on binary classification. But all the below metrics can be extended for multi-class classification. Let us first understand the basic building block of metrics used to evaluate a classification model. Suppose we have trained a binary image classifier model that predicts whether there is a cat or not in the image. Positive Class: Cat is a Positive class . Negative Class: No Cat is the Negative class, It can be anything(dog, horse, background, etc.) except cat in the image. Fig. 1 Fig. 2 True Positive (TP): It is an outcome when the model correctly predicts the positive class. i.e the actual image is "Cat" and the model also predicts "Cat" . False Positive (FP): It is an outcome when the model incorrectly predicts the positive class. i.e the a
(a)Final Blended Image (b) Background Image (c)Foreground Image Alpha blending Alpha blending is the process of overlaying a foreground image with transparency over a background Image. The transparent image is generally a PNG image.It consists of four channels (RGBA).The fourth channel is the alpha channel which holds the transparency magnitude. Image (b) is a background image and image (c) is the foreground / overlay image. Image (a) is the final blended image obtained by blending the overalay image using the alpha mask. Below is the image(Fig d) of the alpha channel of the overlay image. (d).Alpha Channel At every pixel of the image, we blend the background and foreground image color(F) and background color (B) using the alpha mask . At every pixel value of alpha lie in range(0,255), a pixel intensity of 0 means black color and pixel instensity of 255 means whit