Consider a typical convolutional neural network like this example that recognizes 10 different kinds of objects from the CIFAR-10 dataset:
""" Convolutional network applied to CIFAR-10 dataset classification task. References: Learning Multiple Layers of Features from Tiny Images, A. Krizhevsky, 2009. Links: [CIFAR-10 Dataset](https://www.cs.toronto.edu/~kriz/cifar.html) """ from __future__ import division, print_function, absolute_import import tflearn from tflearn.data_utils import shuffle, to_categorical from tflearn.layers.core import input_data, dropout, fully_connected from tflearn.layers.conv import conv_2d, max_pool_2d from tflearn.layers.estimator import regression from tflearn.data_preprocessing import ImagePreprocessing from tflearn.data_augmentation import ImageAugmentation # Data loading and preprocessing from tflearn.datasets import cifar10 (X, Y), (X_test, Y_test) = cifar10.load_data() X, Y = shuffle(X, Y) Y = to_categorical(Y, 10) Y_test = to_categorical(Y_test, 10) # Real-time data preprocessing img_prep = ImagePreprocessing() img_prep.add_featurewise_zero_center() img_prep.add_featurewise_stdnorm() # Real-time data augmentation img_aug = ImageAugmentation() img_aug.add_random_flip_leftright() img_aug.add_random_rotation(max_angle=25.) # Convolutional network building network = input_data(shape=[None, 32, 32, 3], data_preprocessing=img_prep, data_augmentation=img_aug) network = conv_2d(network, 32, 3, activation='relu') network = max_pool_2d(network, 2) network = conv_2d(network, 64, 3, activation='relu') network = conv_2d(network, 64, 3, activation='relu') network = max_pool_2d(network, 2) network = fully_connected(network, 512, activation='relu') network = dropout(network, 0.5) network = fully_connected(network, 10, activation='softmax') network = regression(network, optimizer='adam', loss='categorical_crossentropy', learning_rate=0.001) # Train using classifier model = tflearn.DNN(network, tensorboard_verbose=0) model.fit(X, Y, n_epoch=50, shuffle=True, validation_set=(X_test, Y_test), show_metric=True, batch_size=96, run_id='cifar10_cnn')
It's a CNN with several layers, ending with 10 outputs, one for each type of object recognized.
But now think of a slightly different problem: Let's say I only want to recognize one type of object, but also detect its position within the image frame. Let's say I want to distinguish between:
- object is in center
- object is left of center
- object is right of center
- no recognizable object
Assume I build a CNN exactly like the one in the CIFAR-10 example, but only with 3 outputs:
And of course, if none of the outputs fires, then there is no recognizable object.
Assume I have a large training corpus of images, with the same kind of object in many different positions within the image, the set is grouped and annotated properly, and I train the CNN using the usual methods.
Should I expect the CNN to just "magically" work? Or are there different kinds of architectures required to deal with object position? If so, what are those architectures?