shuffle = True
ssd_train = False
validation_batch_size = 32
patience=30
# In[8]:
train_generator = train_dataset.generate(batch_size=train_batch_size,
shuffle=shuffle,
ssd_train=ssd_train,
random_rotation=20,
translate=(0.2, 0.2),
scale=(0.8, 1.2),
flip=0.5,
divide_by_stddev=255,
equalize=True,
returns={'processed_labels'},
resize=(img_height, img_width))
validation_generator = validation_dataset.generate(batch_size=validation_batch_size,
shuffle=shuffle,
ssd_train=ssd_train,
divide_by_stddev=255,
equalize=True,
returns={'processed_labels'},
resize=(img_height, img_width))
print("Number of images in the dataset:", train_dataset.get_n_samples())
epochs = 100
train_batch_size = 64
shuffle = True
ssd_train = False
validation_batch_size = 32
# In[11]:
train_generator = train_dataset.generate(
batch_size=train_batch_size,
shuffle=shuffle,
ssd_train=ssd_train,
#flip=0.5,
equalize=True,
divide_by_stddev=255,
returns={'processed_labels'},
resize=(img_height, img_width))
validation_generator = validation_dataset.generate(
batch_size=validation_batch_size,
shuffle=shuffle,
ssd_train=ssd_train,
#flip=0.5,
equalize=True,
divide_by_stddev=255,
returns={'processed_labels'},
resize=(img_height, img_width))
print("Size of validation dataset: ", num_val_images, " images")
# -----------------------------------------------------------------------------
# Dataset Generator
# -----------------------------------------------------------------------------
# Setting same batch size for both generators here.
train_generator = train_dataset.generate(batch_size=train_batch_size,
convert_colors_to_ids=False,
convert_ids_to_ids=False,
convert_to_one_hot=True,
void_class_id=None,
random_crop=False,
crop=False,
resize=False,
brightness=False,
flip=0.5,
translate=False,
scale=False,
gray=False,
to_disk=False,
shuffle=True)
val_generator = val_dataset.generate(batch_size=val_batch_size,
convert_colors_to_ids=False,
convert_ids_to_ids=False,
convert_to_one_hot=True,
void_class_id=None,
random_crop=False,
crop=False,
epochs = 90
train_batch_size = 64
shuffle = True
ssd_train = False
validation_batch_size = 32
# In[15]:
test_generator = test_dataset.generate(batch_size=train_batch_size,
shuffle=shuffle,
ssd_train=ssd_train,
divide_by_stddev = 225,
#equalize=True,
returns={'processed_labels'},
resize=(img_height, img_width))
# In[5]:
print("Number of images in the dataset:", test_dataset.get_n_samples())
# In[6]:
steps = test_dataset.get_n_samples()/train_batch_size