Many measurements have been taken to tackle the COVID-19 pandemic. Among which, wearing a face mask is one way to prevent spreading the virus. This work aims to detect if a person is wearing a mask or not. With this objective, a machine learning model is developed, which leverages transfer learning to detect mask. For collecting human faces with the mask, a subset (1000 images) of the MAFA dataset is used.

The objective is to extract the registration/license plate number from different vehicle images. The task is divided into two parts. First, detect the possible ROI(region of interest) - in this case, it is the license plate of vehicles. Second, extract the letters and numbers from the detected region. To separate ROI from the images, object detection model - YOLO v4 is used. YOLO v4 (You only look once) is a family of one-stage object detectors that are fast (i.

Classification of the handwritten digits (0-9) has been performed using CNN. The data set includes 60,000 training samples and 10,000 testing samples. The dataset is collected from MNIST. Tensorflow 2.0 is used. The model includes 2 conv, 2 maxpool, 1 dense, 1 dropout layers. Earlystopping is performed. 99% accuracy is achieved. This Project’s GitHub Repository