patience=100)
csv_logger = CSVLogger('output/{}_fcn_vgg16.csv'.format(
datetime.datetime.now().isoformat()))
datagen = PascalVocGenerator(**init_args['pascal_voc_generator']['train'])
train_loader = ImageSetLoader(**init_args['image_set_loader']['train'])
val_loader = ImageSetLoader(**init_args['image_set_loader']['val'])
fcn_vgg16 = FCN(basenet='vgg16', input_shape=(500, 500, 3), num_output=21)
fcn_vgg16.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy', 'categorical_accuracy'])
flow_args = init_args['pascal_voc_generator']['flow_from_imageset']
train_flow_args = flow_args.copy()
train_flow_args['image_set_loader'] = train_loader
val_flow_args = flow_args.copy()
val_flow_args['image_set_loader'] = val_loader
fcn_vgg16.fit_generator(
datagen.flow_from_imageset(**train_flow_args),
steps_per_epoch=1112,
epochs=100,
validation_data=datagen.flow_from_imageset(**val_flow_args),
validation_steps=1111,
verbose=1,
max_q_size=100,
callbacks=[lr_reducer, early_stopper, csv_logger])
fcn_vgg16.save('output/fcn_vgg16.h5')
from fcn import FCN
# set PATH to location of Pacal VOC dataset
PATH = '/home/seth/Datasets/VOCtrainval_11-May-2012/VOCdevkit/VOC2012/'
fcn = FCN(path=PATH)
#fcn.load('my_model')
fcn.train(epochs=75)
fcn.save('my_model')
fcn.evaluate(val=False)
fcn.evaluate()
for id in fcn.train_list[:10]:
fcn.data.show_seg(id)
fcn.predict(id)
for id in fcn.val_list[:10]:
fcn.data.show_seg(id)
fcn.predict(id)