Keras-RCNN

keras-rcnn is the Keras package for region-based convolutional neural networks.

Getting Started

Let’s read and inspect some data:

training_dictionary, test_dictionary = keras_rcnn.datasets.shape.load_data()

categories = {"circle": 1, "rectangle": 2, "triangle": 3}

generator = keras_rcnn.preprocessing.ObjectDetectionGenerator()

generator = generator.flow_from_dictionary(
    dictionary=training_dictionary,
    categories=categories,
    target_size=(224, 224)
)

validation_data = keras_rcnn.preprocessing.ObjectDetectionGenerator()

validation_data = validation_data.flow_from_dictionary(
    dictionary=test_dictionary,
    categories=categories,
    target_size=(224, 224)
)

target, _ = generator.next()

target_bounding_boxes, target_categories, target_images, target_masks, target_metadata = target

target_bounding_boxes = numpy.squeeze(target_bounding_boxes)

target_images = numpy.squeeze(target_images)

target_categories = numpy.argmax(target_categories, -1)

target_categories = numpy.squeeze(target_categories)

keras_rcnn.utils.show_bounding_boxes(target_images, target_bounding_boxes, target_categories)

Let’s create an RCNN instance:

model = keras_rcnn.models.RCNN((224, 224, 3), ["circle", "rectangle", "triangle"])

and pass our preferred optimizer to the compile method:

optimizer = keras.optimizers.Adam()

model.compile(optimizer)

Finally, let’s use the fit_generator method to train our network:

model.fit_generator(
    epochs=10,
    generator=generator,
    validation_data=validation_data
)

Backbone

A backbone is the convolutional neural network (CNN) that’s responsible for extracting the features that’re used by the RCNN. Keras-RCNN provides a number of popular backbones like ResNet and VGG. You can use a backbone by passing a backbone function to the RCNN constructor:

backbone = keras_rcnn.models.backbone.VGG16

model = keras_rcnn.models.RCNN((224, 224, 3), ["circle", "rectangle", "triangle"], backbone)

External Data

The data is made up of a list of dictionaries corresponding to images.

• For each image, add a dictionary with keys 'image', 'objects'

  • 'image' is a dictionary, which contains keys 'checksum', 'pathname', and 'shape'

    • 'checksum' is the md5 checksum of the image
    • 'pathname' is the pathname of the image, put in full pathname

    • 'shape' is a dictionary with keys 'r', 'c', and 'channels'

      • 'c': number of columns
      • 'r': number of rows
      • 'channels': number of channels

  • 'objects' is a list of dictionaries, where each dictionary has keys 'bounding_box', 'category'

    • 'bounding_box' is a dictionary with keys 'minimum' and 'maximum'

      • 'minimum': dictionary with keys 'r' and 'c'

        • 'r': smallest bounding box row
        • 'c': smallest bounding box column

      • 'maximum': dictionary with keys 'r' and 'c'

        • 'r': largest bounding box row
        • 'c': largest bounding box column

    • 'category' is a string denoting the class name

Suppose this data is save in a file called training.json. To load data,

import json

with open('training.json') as f:
    d = json.load(f)

Slack

We’ve been meeting in the #keras-rcnn channel on the keras.io Slack server.

You can join the server by inviting yourself from the following website:

https://keras-slack-autojoin.herokuapp.com/