2. INTRODUCTION
• This challenge involves employing Computer Vision to
identify whether a person is wearing nose masks or not.
• This challenge is to help ensure that masks are being
worn appropriately in public.
• The goal is to use data to create an image classification
model to accurately predict whether an image contains
a person wearing nose mask or not
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3. MODEL PIPELINE
• DATA STRUCTURE ANALYSIS
• DATA PREPROCESSING
• DATA PREPARATION
• MODEL SELECTION
• MODEL TRAINING
• MODEL AND HYPERPARAMTER FINE-TUNING
• MODEL EVALUATION
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4. METHODOLOGY(1)
• DATA STRUCTURE ANALYSIS
• USE OF PANDA DATAFRAME FUNCTIONS TO ANALYZE THE DATA FROM THE
TRAIN.CSV
• TRAIN AND TEST IMAGES BOTH GROUPED INTO ONE FOLDER
• DATA PREPROCESSING
• TRAIN AND TEST IMAGES BOTH GROUPED INTO ONE FOLDER HAD TO BE
SEPARATE FOLDERS
• USING THE TARGET IN THE TRAIN..CSV FILE, THE IMAGES WERE
SEPARATED INTO WITH NOSE MASKS AND WITHOUT NOSE MASKS
• DATA PREPARATION
• PREPROCESS THE DATA BY RESIZING THE IMAGES AND CREATED
DATAFRAMES FOR BOTH WITH NOSE MASKS AND WITHOUT NOSE MASKS
• USE AN IMAGE DATA GENERATOR FROM TENSORFLOW TO AUGMENT
THE DATA
• SPLIT THE DATASET AFTER AUGMENTATION INTO TWO USING
TRAIN_TEST_SPLIT
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5. METHODOLOGY(2)
• MODEL SELECTION
• PRE-TRAINED MODELS SUCH AS VGG16, RESNET AND DENSET WERE
SELECTED FOR THEIR IMAGE CLASSIFICATION ABILITIES
• A CONVOLUTIONAL NEURAL NETWORK (CNN) IS BUILT USING THE PRE
TRAINED MODELS
• MODEL TRAINING
• USING THE PRE-TRAINED MODEL (DENSENET200), A CNN OF 3 LAYERS
IS BUILT WITH GLOBALAVERAGEPOOLING2D, DENSE AND DROPOUT
• THE MODEL IS TRAINED USING THE DATASET WITH ONE SET TRAIINING
AND THE OTHER SET EVALUATING IT
• MODEL AND HYPERPARAMETERS FINE-TUNING
• THE MODEL IS FINE-TUNED BY FREEZING SOME LAYERS AND TRAINING
THE REMAINING LAYERS
• HYPERPARAMETERS ARE OPTIMIZED AS WELL TO IMPROVE
PERFORMANCE
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7. RESULTS
• THE CNN MODEL YIELED AN ACCURACY OF 0.95875 DURING
TRAINING AND IMPROVED TO 0.98769
• THE ACCURACY OF THE TEST WAS 0.990180639
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