Learning-based techniques for image segmentation are a hot research field. Highly imbalanced datasets are one of the most typical problems in this assignment. To compensate for the fewer crowded courses, several techniques have been offered.

They are mostly divided into two groups:

- Data-level strategies, which artificially increase the number of training samples through over and under-sampling training sets;
- Algorithm-level methods improve the relevance of smaller classes without changing the training data distribution.

The Jaccard Index is a metric that is used to determine **how similar sample sets are**. The length of the overlap dividing it by the total of the union of the sample sets is a formal definition of the measurement, which stresses resemblance between finite sample sets.

**IoU is a popular metric for comparing the accuracy of a proposed picture segmentation to a recognized segmentation.**

The IoU is preferred over accuracy in segmentation tasks because it is less impacted by the class imbalances that are inherent in segmentation tasks. For example, if a ground truth image contains 90% background pixels, a suggested segmentation that labels all pixels as “background” will have a 90% accuracy while having a 0% Intersection over Union segmentation.

The Jaccard distance is a measure of dissimilarity across sample sets, akin to the Jaccard Index, which would be a measure of similarity. The Jaccard distance is computed by subtracting the Jaccard index from one, or by dividing the differences at the junction of the two sets.

The IoU is commonly given as a percentage because it can vary from 0 to 1, however, the idea underlying what an IoU score indicates in relation to visual error is not clear. How much better is a 0.7 IoU score than a 0.6 IoU score? In terms of accuracy, does this suggest that 10% more events were categorized correctly? This difficulty is exacerbated by the fact that a single IoU score might correlate to many segmentations.

## Implementation

CNNs are frequently used in image recognition applications, using the IoU metrics to conceptualize object detection accuracy. If a computer vision system is charged with recognizing faces from an image, for example, the IoU can assess the commonalities between the computer’s face detection and the training data.

Semantic segmentation IoU is not the only commonly used metric.

**Pixel accuracy**– The idea of pixel precision is likely the easiest to grasp. It’s the percentage of elements in your photo that are correctly categorized.

While it is simple to comprehend, it is far from the greatest measure. It may be difficult to detect the problem with this statistic at first look.

T**he main issue here is a disparity in classes**. When our classes are severely unbalanced, it indicates that one or more categories dominate the picture, while the rest of the image is dominated by other classes. Unfortunately, class imbalance can’t be disregarded because it’s ubiquitous in many real-world data sets. As a result, the Jaccard machine learning index and Dice Coefficient are two alternate measures that are more effective in addressing this problem.

**Dice Coefficient-**The Dice coefficient and the IoU are quite close. They are positively linked, which means that if one claims model A is better at segmenting a picture than model B, the other will agree. They, like the IoU, vary from zero to 1, with 1 denoting the most similarity between expected and true values.

Dice Coefficient and the IoU are the most often used measurements for semantic segmentation.