Steps to get the model up and running.md

Step 01: Create a new virtual environment. Then install all python packages listed out in the requirement.txt file

If you want to adjust the underlying model used to train the Face Mask Detector, then carryout the following steps

Step 01: Download the file Fine-tuned Google MobileNet V2 model for Mask or No Mask Prediction - (V1 224 x 224).ipynb

Step 02: Download the dataset from the link provided in the Dataset Link.md file

Step 03: Open the Fine-tuned Google MobileNet V2 model for Mask or No Mask Prediction - (V1 224 x 224).ipynb and change the working directory to the location where the downloaded dataset was saved in your local drive - Coding line 03

Step 04: Make the changes that you want

Step 05: Change the working directory to the location where you want to save the trained model - Coding line 26

Step 06: Run the code

If you want to use the pre-trained model (Transfer learning), then carryout the following steps

Step 01: Download the pre-trained model from the link provided within the file "Download the pre-trained model.md" file.

Step 02: Download the file Real_time_mask_detection - Using OpenCV-python for Face detection-Final.ipynb file or the Real_time_mask_detection - Using MTCNN package for Face detection-Final.ipynb file

Step 03: Open the Real_time_mask_detection - Using OpenCV-python for Face detection-Final.ipynb file or the Real_time_mask_detection - Using MTCNN package for Face detection-Final.ipynb file

Note that the Real_time_mask_detection - Using MTCNN package for Face detection-Final will display the output in a lagged manner. This is because it uses a more computationally expensive CNN (Convolutional Neural Network) to locate faces

Step 04: Change the working directory to the location where you have saved the pre-trained model that was downloaded in Step 01 - Coding line 02

Step 05: Run the code