alishakiba / image-classification

How to do Image Classification using Keras

Image Classification in Keras

How to develop an Image Classifiier in keras using tensorflow backend.

Getting Started

Prerequisites

1. TensorFlow
2. Keras

Dataset

1. Download https://www.kaggle.com/c/dogs-vs-cats
2. Create a folder named "dataset_image" in the root directory.
3. Create two folders - "cat" and "dog" inside the folder "dataset_image".
4. Put the downloaded images into the respective folders.

Training

Run train.py

Testing

1. Put an image of a dog/cat in the folder named "images".
2. Run predict.py

Model

model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(64, 64, 3), padding='same', activation='relu', kernel_constraint=maxnorm(3)))
model.add(Dropout(0.2))
model.add(Conv2D(32, (3, 3), activation='relu', padding='same', kernel_constraint=maxnorm(3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(512, activation='relu', kernel_constraint=maxnorm(3)))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
lrate = 0.01
decay = lrate/epochs
sgd = SGD(lr=lrate, momentum=0.9, decay=decay, nesterov=False)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])

License

This project is licensed under the MIT License