Home Artificial Intelligence Face-Mask Detection using SVM — Intel oneAPI Optimised Scikit-Learn Library An easy explanation of how SVM works: Prerequisites: Installation of scikit-learn-intelex library: Dataset: Importing the Dataset and Converting it to NumPy array: Data Preprocessing: Training the Model: Testing:

Face-Mask Detection using SVM — Intel oneAPI Optimised Scikit-Learn Library An easy explanation of how SVM works: Prerequisites: Installation of scikit-learn-intelex library: Dataset: Importing the Dataset and Converting it to NumPy array: Data Preprocessing: Training the Model: Testing:

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Face-Mask Detection using SVM — Intel oneAPI Optimised Scikit-Learn Library
An easy explanation of how SVM works:
Prerequisites:
Installation of scikit-learn-intelex library:
Dataset:
Importing the Dataset and Converting it to NumPy array:
Data Preprocessing:
Training the Model:
Testing:

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  1. Data points are plotted on a graph, where each point represents an remark with several features.
  2. The algorithm tries to search out a line (or a hyperplane in higher dimensions) that separates the 2 classes with the biggest possible margin.
  3. The margin is the gap between the choice boundary and the closest data points from each classes.
  4. The info points closest to the choice boundary are called support vectors, they usually determine the position and orientation of the boundary.
  5. Once the boundary is found, the algorithm can classify latest data points by determining on which side of the boundary they fall.
  • Python 3.7 or above.
  • Packages Required : Numpy, Pandas, scikit-learn-intelex, scikit-learn, PIL, matplotlib
The person with a mask is detected.
The person with out a mask is detected.

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