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It contains a trained model to detect the number of faces in the given photo.

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Icyfire18/Face_Counting

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Face_Counting

The method of face detection in pictures is very complicated because of variability it presents in human faces such as position and orientation, skin colour, pose, expression, the presence of glasses or facial hair, differences in camera gain, lighting conditions, and image resolution.

Object detection is one of the Computer Technologies which is connected to the Computer Vision and Image Processing. It interacts with detecting instances of an object such as Human Faces, Building, Tree, Car, etc. The primary aim of face detection algorithms is to determine whether there is any face in an Image or not.

We are provided with a training set of images with coordinates of bounding box and head count for each image and need to predict the headcount for each image in the test set.

The evaluation metric is RMSE (root mean squared error) over the head counts predicted for test images.

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It contains a trained model to detect the number of faces in the given photo.

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