##Fetch data from google drive

```
!cp drive/My\ Drive/mask/dataset.zip ../content/
```

```
!unzip dataset.zip -d data/
```

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```
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.applications import MobileNetV2
from tensorflow.keras.models import Model, Sequential
from tensorflow.keras.layers import AveragePooling2D, Dropout, Flatten, Dense, InputLayer, Input, BatchNormalization
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
from tensorflow.keras.preprocessing.image import img_to_array, load_img
from tensorflow.keras.utils import to_categorical
from sklearn.preprocessing import LabelBinarizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from imutils import paths
import numpy as np
```

##Load data and preprocess

```
imagePaths = list(paths.list_images('data/dataset/'))
data = []
labels = []
num_images = len(imagePaths)
for i, imagePath in enumerate(imagePaths):
  image = load_img(imagePath, target_size=(224, 224))
  image = img_to_array(image)
  image = preprocess_input(image)

  data.append(image)
  print('Processed {} images of {}'.format(i+1, num_images))
  labels.append(imagePath.split('/')[-2])

```

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```
data = np.array(data)
labels = np.array(labels)
```

```
data.shape
```

    (1376, 224, 224, 3)

```
lb = LabelBinarizer()
labels = lb.fit_transform(labels)
labels = to_categorical(labels)
```

```
(X_train, X_test, y_train, y_test) = train_test_split(data, labels, train_size=0.8, stratify=labels, random_state=42)

aug = ImageDataGenerator(
    rotation_range=20,
    zoom_range=0.15,
    width_shift_range=0.2,
    height_shift_range=0.2,
    shear_range=0.15,
    horizontal_flip=True,
    fill_mode="nearest"
)

```

##Build model

```
input_shape = (224, 224, 3)
```

```
model = Sequential()
model.add(InputLayer(input_shape=input_shape))
model.add(MobileNetV2(weights='imagenet', include_top=False))
model.add(AveragePooling2D(pool_size=(7, 7)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax'))
```

    WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.
    Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5
    9412608/9406464 [==============================] - 0s 0us/step

```
# x = MobileNetV2(weights='imagenet', include_top=False)
# x.pop()
# x.summary()
```

```
LR = 1e-4
BS = 32
epochs = 20
opt = Adam(lr=LR, decay=LR / epochs)
model.compile(loss="categorical_crossentropy", optimizer=opt,
	metrics=["accuracy"])

```

```
H = model.fit(
    aug.flow(X_train, y_train, batch_size=BS),
    steps_per_epoch=len(X_train)//BS,
    validation_data=(X_test, y_test),
    validation_steps=len(X_test)//BS,
    epochs=epochs
)
```

    Epoch 1/20
    34/34 [==============================] - 19s 561ms/step - loss: 0.3328 - accuracy: 0.8614 - val_loss: 0.0414 - val_accuracy: 0.9891
    Epoch 2/20
    34/34 [==============================] - 17s 498ms/step - loss: 0.0638 - accuracy: 0.9775 - val_loss: 0.0248 - val_accuracy: 0.9928
    Epoch 3/20
    34/34 [==============================] - 17s 496ms/step - loss: 0.0352 - accuracy: 0.9897 - val_loss: 0.0221 - val_accuracy: 0.9928
    Epoch 4/20
    34/34 [==============================] - 17s 498ms/step - loss: 0.0207 - accuracy: 0.9944 - val_loss: 0.0182 - val_accuracy: 0.9928
    Epoch 5/20
    34/34 [==============================] - 17s 498ms/step - loss: 0.0218 - accuracy: 0.9925 - val_loss: 0.0176 - val_accuracy: 0.9928
    Epoch 6/20
    34/34 [==============================] - 17s 496ms/step - loss: 0.0150 - accuracy: 0.9953 - val_loss: 0.0152 - val_accuracy: 0.9928
    Epoch 7/20
    34/34 [==============================] - 17s 494ms/step - loss: 0.0157 - accuracy: 0.9953 - val_loss: 0.0108 - val_accuracy: 0.9928
    Epoch 8/20
    34/34 [==============================] - 17s 490ms/step - loss: 0.0136 - accuracy: 0.9944 - val_loss: 0.0113 - val_accuracy: 0.9964
    Epoch 9/20
    34/34 [==============================] - 17s 492ms/step - loss: 0.0047 - accuracy: 0.9991 - val_loss: 0.0104 - val_accuracy: 0.9964
    Epoch 10/20
    34/34 [==============================] - 17s 492ms/step - loss: 0.0092 - accuracy: 0.9972 - val_loss: 0.0111 - val_accuracy: 0.9964
    Epoch 11/20
    34/34 [==============================] - 17s 490ms/step - loss: 0.0057 - accuracy: 0.9991 - val_loss: 0.0076 - val_accuracy: 0.9964
    Epoch 12/20
    34/34 [==============================] - 17s 491ms/step - loss: 0.0055 - accuracy: 0.9981 - val_loss: 0.0055 - val_accuracy: 0.9964
    Epoch 13/20
    34/34 [==============================] - 17s 489ms/step - loss: 0.0050 - accuracy: 0.9991 - val_loss: 0.0080 - val_accuracy: 0.9964
    Epoch 14/20
    34/34 [==============================] - 17s 491ms/step - loss: 0.0036 - accuracy: 0.9991 - val_loss: 0.0092 - val_accuracy: 0.9964
    Epoch 15/20
    34/34 [==============================] - 17s 489ms/step - loss: 0.0065 - accuracy: 0.9972 - val_loss: 0.0083 - val_accuracy: 0.9964
    Epoch 16/20
    34/34 [==============================] - 17s 492ms/step - loss: 0.0026 - accuracy: 0.9991 - val_loss: 0.0057 - val_accuracy: 0.9964
    Epoch 17/20
    34/34 [==============================] - 17s 491ms/step - loss: 0.0035 - accuracy: 0.9991 - val_loss: 0.0046 - val_accuracy: 0.9964
    Epoch 18/20
    34/34 [==============================] - 17s 493ms/step - loss: 0.0034 - accuracy: 0.9991 - val_loss: 0.0040 - val_accuracy: 0.9964
    Epoch 19/20
    34/34 [==============================] - 17s 495ms/step - loss: 0.0040 - accuracy: 0.9991 - val_loss: 0.0046 - val_accuracy: 0.9964
    Epoch 20/20
    34/34 [==============================] - 17s 489ms/step - loss: 0.0030 - accuracy: 0.9991 - val_loss: 0.0046 - val_accuracy: 0.9964

```
model.evaluate(X_test, y_test)
```

    9/9 [==============================] - 1s 114ms/step - loss: 0.0046 - accuracy: 0.9964

    [0.004589356016367674, 0.9963768124580383]

```
y_pred = model.predict(X_test)
y_pred = np.argmax(y_pred, axis=1)
print(classification_report(y_pred=y_pred, y_true=np.argmax(y_test, axis=1)))
```

                  precision    recall  f1-score   support

               0       0.99      1.00      1.00       138
               1       1.00      0.99      1.00       138

        accuracy                           1.00       276
       macro avg       1.00      1.00      1.00       276
    weighted avg       1.00      1.00      1.00       276

```
model.save("mask_detection.h5")
```

##Testing model on custom inputs

```
!wget https://i.pinimg.com/474x/0d/de/de/0ddedeaf55601af87eb5b62f377103c7--mobile-wallpaper-the-big-bang-theory-wallpaper.jpg
```

    --2020-07-29 09:13:40--  https://i.pinimg.com/474x/0d/de/de/0ddedeaf55601af87eb5b62f377103c7--mobile-wallpaper-the-big-bang-theory-wallpaper.jpg
    Resolving i.pinimg.com (i.pinimg.com)... 151.101.76.84, 2600:1417:76:499::1931, 2600:1417:76:489::1931, ...
    Connecting to i.pinimg.com (i.pinimg.com)|151.101.76.84|:443... connected.
    HTTP request sent, awaiting response... 200 OK
    Length: 55694 (54K) [image/jpeg]
    Saving to: ‘0ddedeaf55601af87eb5b62f377103c7--mobile-wallpaper-the-big-bang-theory-wallpaper.jpg’

    0ddedeaf55601af87eb 100%[===================>]  54.39K  --.-KB/s    in 0.03s

    2020-07-29 09:13:40 (1.99 MB/s) - ‘0ddedeaf55601af87eb5b62f377103c7--mobile-wallpaper-the-big-bang-theory-wallpaper.jpg’ saved [55694/55694]

```
from google.colab.patches import cv2_imshow
image = load_img('/content/0ddedeaf55601af87eb5b62f377103c7--mobile-wallpaper-the-big-bang-theory-wallpaper.jpg', target_size=(224, 224))
image = img_to_array(image)
cv2_imshow(image)
image = preprocess_input(image)
image = np.expand_dims(image, axis=0)
x = model.predict(image)
print(lb.classes_[np.argmax(x, axis=1)], ": ", x[0][np.argmax(x, axis=1)])
```

```
!wget https://www.onemedical.com/media/images/woman-wearing-mask.width-1440.jpg
```

    --2020-07-29 09:13:51--  https://www.onemedical.com/media/images/woman-wearing-mask.width-1440.jpg
    Resolving www.onemedical.com (www.onemedical.com)... 13.224.153.128, 13.224.153.42, 13.224.153.60, ...
    Connecting to www.onemedical.com (www.onemedical.com)|13.224.153.128|:443... connected.
    HTTP request sent, awaiting response... 200 OK
    Length: 123938 (121K) [image/jpeg]
    Saving to: ‘woman-wearing-mask.width-1440.jpg’

    woman-wearing-mask. 100%[===================>] 121.03K  --.-KB/s    in 0.03s

    2020-07-29 09:13:51 (4.59 MB/s) - ‘woman-wearing-mask.width-1440.jpg’ saved [123938/123938]

```
image = load_img('woman-wearing-mask.width-1440.jpg', target_size=(224, 224))
image = img_to_array(image)
cv2_imshow(image)
image = preprocess_input(image)
image = np.expand_dims(image, axis=0)
x = model.predict(image)
print(lb.classes_[np.argmax(x, axis=1)], ": ", x[0][np.argmax(x, axis=1)])
```

```
!wget https://www.cidrap.umn.edu/sites/default/files/public/styles/detail/public/media/article/woman_wearing_mask_in_airport.jpg?itok=qykld-R-
```

    --2020-07-29 09:14:00--  https://www.cidrap.umn.edu/sites/default/files/public/styles/detail/public/media/article/woman_wearing_mask_in_airport.jpg?itok=qykld-R-
    Resolving www.cidrap.umn.edu (www.cidrap.umn.edu)... 199.167.77.60
    Connecting to www.cidrap.umn.edu (www.cidrap.umn.edu)|199.167.77.60|:443... connected.
    HTTP request sent, awaiting response... 200 OK
    Length: 13558 (13K) [image/jpeg]
    Saving to: ‘woman_wearing_mask_in_airport.jpg?itok=qykld-R-’

    woman_wearing_mask_ 100%[===================>]  13.24K  --.-KB/s    in 0s

    2020-07-29 09:14:01 (249 MB/s) - ‘woman_wearing_mask_in_airport.jpg?itok=qykld-R-’ saved [13558/13558]

```
image = load_img('/content/woman_wearing_mask_in_airport.jpg?itok=qykld-R-', target_size=(224, 224))
image = img_to_array(image)
cv2_imshow(image)
image = preprocess_input(image)
image = np.expand_dims(image, axis=0)
x = model.predict(image)
print(lb.classes_[np.argmax(x, axis=1)], ": ", x[0][np.argmax(x, axis=1)])
```

##Convert model to mlmodel

```
# coremltools is required to convert model to mlmodel
!pip install coremltools==4.0b1
```

    Collecting coremltools==4.0b1
      Downloading https://files.pythonhosted.org/packages/6f/b7/b9aebb72e977f4f4d188e01f9b917bf80cff69aa6bd27239a7b94c417efe/coremltools-4.0b1.tar.gz (600kB)
         |████████████████████████████████| 604kB 2.8MB/s
    Requirement already satisfied: numpy>=1.14.5 in /usr/local/lib/python3.6/dist-packages (from coremltools==4.0b1) (1.18.5)
    Requirement already satisfied: protobuf>=3.1.0 in /usr/local/lib/python3.6/dist-packages (from coremltools==4.0b1) (3.12.2)
    Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from coremltools==4.0b1) (1.15.0)
    Collecting attr
      Downloading https://files.pythonhosted.org/packages/de/be/ddc7f84d4e087144472a38a373d3e319f51a6faf6e5fc1ae897173675f21/attr-0.3.1.tar.gz
    Requirement already satisfied: attrs in /usr/local/lib/python3.6/dist-packages (from coremltools==4.0b1) (19.3.0)
    Requirement already satisfied: sympy in /usr/local/lib/python3.6/dist-packages (from coremltools==4.0b1) (1.1.1)
    Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from coremltools==4.0b1) (1.4.1)
    Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from coremltools==4.0b1) (4.41.1)
    Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.1.0->coremltools==4.0b1) (49.1.0)
    Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.6/dist-packages (from sympy->coremltools==4.0b1) (1.1.0)
    Building wheels for collected packages: coremltools, attr
      Building wheel for coremltools (setup.py) ... done
      Created wheel for coremltools: filename=coremltools-4.0b1-cp36-none-any.whl size=796442 sha256=f6c476ed2c426b0dfbdabcfb88945615b85e8ebb29e160aa86750c63cf37d636
      Stored in directory: /root/.cache/pip/wheels/e6/2f/39/c7f309170b37269d937c226e676611092188765da1a7213a54
      Building wheel for attr (setup.py) ... done
      Created wheel for attr: filename=attr-0.3.1-cp36-none-any.whl size=2459 sha256=ab18c3314c24ee2caf12dcf4997b88b16515e5e86b5c228f3badde53fb20e93d
      Stored in directory: /root/.cache/pip/wheels/f0/96/9b/1f8892a707d17095b5a6eab0275da9d39e68e03a26aee2e726
    Successfully built coremltools attr
    Installing collected packages: attr, coremltools
    Successfully installed attr-0.3.1 coremltools-4.0b1

```
import coremltools as ct

inputs = ct.ImageType(scale=(1/127), bias=[-1.0, -1.0, -1.0])
classifier_config=ct.ClassifierConfig(['Mask on :)', 'Mask off!!'])

mlmodel = ct.convert(model=model,
                    inputs=[inputs],
                    source='tensorflow',
                    classifier_config=classifier_config,
                    )
```

    WARNING:root:scikit-learn version 0.22.2.post1 is not supported. Minimum required version: 0.17. Maximum required version: 0.19.2. Disabling scikit-learn conversion API.
    WARNING:root:Keras version 2.3.1 detected. Last version known to be fully compatible of Keras is 2.2.4 .
    Running TensorFlow Graph Passes: 100%|██████████| 5/5 [00:00<00:00,  8.95 passes/s]
    Converting Frontend ==> MIL Ops: 100%|██████████| 434/434 [00:09<00:00, 46.19 ops/s]
    Running MIL optimization passes: 100%|██████████| 13/13 [00:11<00:00,  1.12 passes/s]
    Translating MIL ==> MLModel Ops: 100%|██████████| 709/709 [00:00<00:00, 1927.66 ops/s]

```
mlmodel
```

    input {
      name: "input_1"
      type {
        imageType {
          width: 224
          height: 224
          colorSpace: RGB
          imageSizeRange {
            widthRange {
              lowerBound: 224
              upperBound: 224
            }
            heightRange {
              lowerBound: 224
              upperBound: 224
            }
          }
        }
      }
    }
    output {
      name: "Identity"
      type {
        dictionaryType {
          stringKeyType {
          }
        }
      }
    }
    output {
      name: "classLabel"
      type {
        stringType {
        }
      }
    }
    predictedFeatureName: "classLabel"
    predictedProbabilitiesName: "Identity"
    metadata {
      userDefined {
        key: "com.github.apple.coremltools.source"
        value: "tensorflow==2.2.0"
      }
      userDefined {
        key: "com.github.apple.coremltools.version"
        value: "4.0b1"
      }
    }

```
mlmodel.save('mask_detection.mlmodel')
```

```

```