Android Application for Dog Breed Classification using TensorFlow Lite
Image Classification, TensorFlow Lite, MobileNetV2, Android Application
1. Data Set
The Stanford Dogs data set consists of 20,580 images of 120 dog breeds from around the world.
2. Data Preparation
Resize the images by using ImageDataGenerator and create a training set that stores 80% of the images and validation set that stores 20% of images.
IMAGE_SIZE = 224
BATCH_SIZE = 64datagen = tf.keras.preprocessing.image.ImageDataGenerator(
rescale=1./255,
validation_split=0.2)train_generator = datagen.flow_from_directory(
image_dir,
target_size=(IMAGE_SIZE, IMAGE_SIZE),
batch_size=BATCH_SIZE,
subset='training')val_generator = datagen.flow_from_directory(
image_dir,
target_size=(IMAGE_SIZE, IMAGE_SIZE),
batch_size=BATCH_SIZE,
subset='validation')
3. Model Pipeline
Initialize Base Model
MobileNet V2 model was pre-trained on ImageNet data set, which was trained on over 1.4 million images of 1000 classes. We use this model to extract the features from the custom data set. Change the include_top parameter to False to exclude the classification layers
base_model= tf.keras.applications.MobileNetV2(
input_shape=IMG_SHAPE,
include_top=False,
weights='imagenet')Feature Extraction
Freeze the convolution base of the base model to use it as a feature extractor and add a classifier on top of it to train the model on custom data set.
base_model.trainable = Falsemodel = tf.keras.Sequential([base_model,
tf.keras.layers.Conv2D(32, 3, activation='relu'),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.GlobalAveragePooling2D(),
tf.keras.layers.Dense(120, activation='softmax')])
Compile Model
model.compile(optimizer=tf.keras.optimizers.Adam(),
loss='categorical_crossentropy',
metrics=['accuracy'])epochs = 10
history = model.fit(train_generator,
steps_per_epoch=len(train_generator),
epochs=epochs,
validation_data=val_generator, validation_steps=len(val_generator))
Convert to TFLite Format
saved_model_dir = 'Directory to store the converted model'
tf.saved_model.save(model, saved_model_dir)converter= tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
tflite_model = converter.convert()with open('model.tflite', 'wb') as f:
f.write(tflite_model)
4. Dog Breed Android Application
Setup the environment by cloning the Tensorflow’s GitHub repository. This repository includes examples of various deep learning applications deployed on multiple platforms.
Step 1: Clone the Tensorflow’s repository
git clone https://github.com/tensorflow/examples.gitStep 2: Add the TF Lite file and labels to the assets folder
#Path to assets directory/lite/examples/image_classification/android/app/src/main/assets/
Step 3: Install Android Studio and open the project folder as an existing Android Studio project
#Path to project directory/examples/lite/examples/image_classification/android
Step 4: Enable developer options and USB debugging on your Android device
- Android 9 (API level 28) and higher: Settings > System > Advanced > Developer Options > USB debugging
- Android 8.0.0 (API level 26) and Android 8.1.0 (API level 26): Settings > System > Developer Options > USB debugging
Step 5: Sync project with Gradle files
Step 6: Select the device and run the application
