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diff --git a/Face-Mask-Detection/face_mask_detection_using_cnn.py b/Face-Mask-Detection/face_mask_detection_using_cnn.py
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+++ b/Face-Mask-Detection/face_mask_detection_using_cnn.py
@@ -0,0 +1,136 @@
+import numpy as np
+import pandas as pd
+import cv2
+import matplotlib.pyplot as plt
+from tensorflow.keras.models import Model, Sequential
+from tensorflow.keras import layers
+import tensorflow as tf
+from keras.preprocessing.image import img_to_array
+from tensorflow.keras import backend
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+import os
+from sklearn.metrics import classification_report
+import sklearn.metrics as metrics
+import itertools
+for dirname, _, filenames in os.walk('/kaggle/input'):
+    for filename in filenames:
+        print(os.path.join(dirname, filename))
+def data_set(dir_data):
+    data=[]
+    target=[]
+    data_map = {
+    'with_mask':1,
+    'without_mask':0
+    }
+    skipped=0
+    root=dir_data+'_annotations.csv'
+    df1 = pd.read_csv(root)
+    df1.dataframeName = '_annotations.csv'
+    nRow, nCol = df1.shape
+    for i in range(len(df1)):
+        without_mask='without_mask'
+        k=dir_data+df1['filename'][i]
+        image=cv2.imread(k)
+        xmin=int(df1['xmin'][i])
+        ymin=int(df1['ymin'][i])
+        xmax=int(df1['xmax'][i])
+        ymax=int(df1['ymax'][i])
+        #image=image[ymin:ymax,  xmin:xmax]
+        try:
+                # resizing to (70 x 70)
+                image = cv2.resize(image,(70,70))
+        except Exception as E:
+                skipped += 1
+                print(E)
+                continue
+        if(df1['class'][i]=='mask'):
+            without_mask='with_mask'
+        image=img_to_array(image)
+        data.append(image)
+        target.append(data_map[without_mask])
+    data = np.array(data, dtype="float") / 255.0
+    target = tf.keras.utils.to_categorical(np.array(target), num_classes=2)
+    return data, target
+training_data,training_target=data_set('./Face-Mask-Detection/kaggle/input/face-mask-detection/train/')
+testing_data,testing_target=data_set('./Face-Mask-Detection/kaggle/input/face-mask-detection/test/')
+valid_data,valid_target=data_set('./Face-Mask-Detection/kaggle/input/face-mask-detection/valid/')
+plt.figure(0, figsize=(100,100))
+for i in range(1,10):
+    plt.subplot(10,5,i)
+    plt.imshow(training_data[i])
+img_shape=training_data[0].shape
+depth, height, width=3, img_shape[0], img_shape[1]
+img_shape=(height, width, depth)
+chanDim=-1
+if backend.image_data_format() == "channels_first": #Returns a string, either 'channels_first' or 'channels_last'
+        img_shape = (depth, height, width)
+        chanDim = 1
+model=Sequential()
+model.add(layers.Conv2D(32,(3,3),input_shape=img_shape))
+model.add(layers.MaxPooling2D(pool_size=(2,2)))
+model.add(layers.Conv2D(64,(3,3)))
+model.add(layers.Activation('relu'))
+model.add(layers.MaxPooling2D(pool_size=(2,2)))
+model.add(layers.Conv2D(128,(3,3)))
+model.add(layers.Activation('relu'))
+model.add(layers.MaxPooling2D(pool_size=(2,2)))
+model.add(layers.Conv2D(256,(3,3)))
+model.add(layers.Activation('relu'))
+model.add(layers.MaxPooling2D(pool_size=(2,2)))
+model.add(layers.Flatten())
+model.add(layers.Dropout(0.5))
+model.add(layers.Dense(64,activation='relu'))
+model.add(layers.Dropout(0.4))
+model.add(layers.Dense(2,activation='softmax'))
+adam =tf.keras.optimizers.Adam(0.001)
+model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy'])
+model.summary()
+aug = ImageDataGenerator(rotation_range=25, width_shift_range=0.1,
+                         height_shift_range=0.1, shear_range=0.2, zoom_range=0.2,
+                         horizontal_flip=True, fill_mode="nearest")
+history = model.fit(aug.flow(training_data, training_target, batch_size=10),
+                   epochs=70,
+                   validation_data=(valid_data, valid_target),
+                   verbose=2,
+                   shuffle=True)
+plt.plot(history.history['accuracy'])
+plt.plot(history.history['val_accuracy'])
+plt.ylabel(['accuracy'])
+plt.xlabel(['epoch'])
+plt.legend(['accuracy', 'val_accuracy'])
+plt.plot(history.history['loss'])
+plt.plot(history.history['val_loss'])
+plt.ylabel(['loss'])
+plt.xlabel(['epoch'])
+plt.legend(['loss', 'val_loss'])
+loss, accuracy = model.evaluate(testing_data,testing_target)
+print('accuracy= ',loss," loss= ",loss)
+yhat = model.predict(testing_data)
+test_pred=np.argmax(yhat,axis=1)
+testing_target=np.argmax(testing_target,axis=1)
+report = classification_report(testing_target, test_pred)
+print(report)
+def plot_confusion_matrix(cm, classes,
+                          normalize=False,
+                          title='Confusion matrix',
+                          cmap=plt.cm.RdYlGn):
+    plt.imshow(cm, interpolation='nearest', cmap=cmap)
+    plt.title(title)
+    plt.colorbar()
+    tick_marks = np.arange(len(classes))
+    plt.xticks(tick_marks, classes, rotation=45)
+    plt.yticks(tick_marks, classes)
+
+    thresh = cm.max() / 2.
+    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
+        plt.text(j, i, cm[i, j],
+                 horizontalalignment="center",
+                 color="white" if cm[i, j] > thresh else "black")
+
+    plt.tight_layout()
+    plt.ylabel('True label')
+    plt.xlabel('Predicted label')
+
+confusion = metrics.confusion_matrix(testing_target, test_pred)
+plt.figure()
+plot_confusion_matrix(confusion, classes=['without_mask','with_mask'], title='Confusion matrix')
diff --git a/Face-Mask-Detection/readme.md b/Face-Mask-Detection/readme.md
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--- /dev/null
+++ b/Face-Mask-Detection/readme.md
@@ -0,0 +1,21 @@
+### Introduction:
+- Face mask detection had seen significant progress in the domains of Image processing and Computer vision, since the rise of the Covid-19 pandemic. Many face detection models have been created using several algorithms and techniques. The approach in this project uses deep learning, TensorFlow, Keras, and OpenCV to detect face masks.
+- Convolutional Neural Network, Data augmentation are the key to this project.
+### Example:
+![face mask sample](https://raw.githubusercontent.com/sudipg4112001/Amazing-Python-Scripts/Face_Mask/Face-Mask-Detection/Sample-images/Sample_image_3.jpg)
+![face mask sample](https://raw.githubusercontent.com/sudipg4112001/Amazing-Python-Scripts/Face_Mask/Face-Mask-Detection/Sample-images/Sample_image_2.jpg)
+### Methodology:
+![face mask sample](https://raw.githubusercontent.com/sudipg4112001/Amazing-Python-Scripts/Face_Mask/Face-Mask-Detection/Sample-images/Method.jpg)
+### Setup Instructions:
+- This project contains datset with large number of data so all the datsets(Training, Testing, Validation) are put in a file named 'Dataset.rar'. This file needs to be extracted before proceeding with the project.
+- The python script is fully made using Kaggle kernel. Anyone proceeding with the script are suggested to use Kaggle kernel, Google Colab, or Jupyter Notebook to run the script smoothly.
+- All the datasets from the file 'Dataset.rar' should be uploaded, then the path is used in the script.
+### Requirements:
+- numpy
+- pandas
+- tensorflow
+- opencv
+- keras
+- sklearn
+
+Install all the modules listed under 'Requirements' section.