diff --git a/computer_vision/facemaskdetectorproject.py b/computer_vision/facemaskdetectorproject.py
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+#Face-Mask-Detector-Project In Python Programming Language
+
+
+##Part-01: Training Part
+
+from keras.optimizers import RMSprop
+from keras.preprocessing.image import ImageDataGenerator
+import cv2
+from keras.models import Sequential
+from keras.layers import Conv2D, Input, ZeroPadding2D, BatchNormalization, Activation, MaxPooling2D, Flatten, Dense,Dropout
+from keras.models import Model, load_model
+from keras.callbacks import TensorBoard, ModelCheckpoint
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import f1_score
+from sklearn.utils import shuffle
+import imutils
+import numpy as np
+
+model =Sequential([
+    Conv2D(100, (3,3), activation='relu', input_shape=(150, 150, 3)),
+    MaxPooling2D(2,2),
+    
+    Conv2D(100, (3,3), activation='relu'),
+    MaxPooling2D(2,2),
+    
+    Flatten(),
+    Dropout(0.5),
+    Dense(50, activation='relu'),
+    Dense(2, activation='softmax')
+])
+model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])
+
+TRAINING_DIR = "./train"
+train_datagen = ImageDataGenerator(rescale=1.0/255,
+                                   rotation_range=40,
+                                   width_shift_range=0.2,
+                                   height_shift_range=0.2,
+                                   shear_range=0.2,
+                                   zoom_range=0.2,
+                                   horizontal_flip=True,
+                                   fill_mode='nearest')
+
+train_generator = train_datagen.flow_from_directory(TRAINING_DIR, 
+                                                    batch_size=10, 
+                                                    target_size=(150, 150))
+VALIDATION_DIR = "./test"
+validation_datagen = ImageDataGenerator(rescale=1.0/255)
+
+validation_generator = validation_datagen.flow_from_directory(VALIDATION_DIR, 
+                                                         batch_size=10, 
+                                                         target_size=(150, 150))
+checkpoint = ModelCheckpoint('model2-{epoch:03d}.model',monitor='val_loss',verbose=0,save_best_only=True,mode='auto')
+
+
+history = model.fit_generator(train_generator,
+                              epochs=10,
+                              validation_data=validation_generator,
+                              callbacks=[checkpoint])
+                              
+                               
+##Part-02: Testing Of Model
+
+import cv2
+import numpy as np
+from keras.models import load_model
+model=load_model("./model-010.h5")
+
+labels_dict={0:'without mask',1:'mask'}
+color_dict={0:(0,0,255),1:(0,255,0)}
+
+size = 4
+webcam = cv2.VideoCapture(0) #Use camera 0
+
+# We load the xml file
+classifier = cv2.CascadeClassifier('/home/shivam/.local/lib/python3.6/site-packages/cv2/data/haarcascade_frontalface_default.xml')
+
+while True:
+    (rval, im) = webcam.read()
+    im=cv2.flip(im,1,1) #Flip to act as a mirror
+
+    # Resize the image to speed up detection
+    mini = cv2.resize(im, (im.shape[1] // size, im.shape[0] // size))
+
+    # detect MultiScale / faces 
+    faces = classifier.detectMultiScale(mini)
+
+    # Draw rectangles around each face
+    for f in faces:
+        (x, y, w, h) = [v * size for v in f] #Scale the shapesize backup
+        #Save just the rectangle faces in SubRecFaces
+        face_img = im[y:y+h, x:x+w]
+        resized=cv2.resize(face_img,(150,150))
+        normalized=resized/255.0
+        reshaped=np.reshape(normalized,(1,150,150,3))
+        reshaped = np.vstack([reshaped])
+        result=model.predict(reshaped)
+        #print(result)
+        
+        label=np.argmax(result,axis=1)[0]
+      
+        cv2.rectangle(im,(x,y),(x+w,y+h),color_dict[label],2)
+        cv2.rectangle(im,(x,y-40),(x+w,y),color_dict[label],-1)
+        cv2.putText(im, labels_dict[label], (x, y-10),cv2.FONT_HERSHEY_SIMPLEX,0.8,(255,255,255),2)
+        
+    # Show the image
+    cv2.imshow('LIVE',   im)
+    key = cv2.waitKey(10)
+    # if Esc key is press then break out of the loop 
+    if key == 27: #The Esc key
+        break
+# Stop video
+webcam.release()
+
+# Close all started windows
+cv2.destroyAllWindows()
+
+#end