Submissions are evaluated using categorization accuracy. Each image has been labeled with one true class. For each image, you must submit a set of the predicted labels.
Before we delve into the details of each of the accuracy score, it is important to cover the four building blocks used to define the evaluation metrics:
- True Positive (TP) – Actual label is positive and prediction is also positive
- True Negative (TN) – Actual label is negative and prediction is also negative
- False Positive (FP) – Actual label is negative but prediction is positive
- False Negative (FN) – Actual label is positive but prediction is negative

![test](https://i.ibb.co/HnvkpfQ/Classification-Image.png)
Accuracy is the most basic version of evaluation metrics. It is calculated as the ratio of correct predictions (TP + TN) over all the predictions made (TP + TN + FP + FN).
![test](https://miro.medium.com/max/1210/1*N4Lo9Miw397g3XpX7o0CDw.png)
Submission File
You must submit a csv file with the image file name and class.

The 4 classes to predict are:

- hexagon
- pentagon
- square
- triangle
The order of the rows does not matter. The file must have a header and should look like the following:
id,classes
1.jpg, pentagon
2.jpg, triangle
3.jpg, triangle
...