tiny_imagenet_config.py

# import packages
from os import path

# define the paths to the training and validation directories
TRAIN_IMAGES = "../datasets/tiny-imagenet-200/train"
VAL_IMAGES = "../datasets/tiny-imagenet-200/val/images"

# define the path to the file that maps validation filenames to
# their corresponding class labels
VAL_MAPPINGS = "../datasets/tiny-imagenet-200/val/val_annotations.txt"

# define the paths to the WordNet hierarchy files
# which are used to generate class labels
WORDNET_IDS = "../datasets/tiny-imagenet-200/wnids.txt"
WORD_LABELS = "../datasets/tiny-imagenet-200/words.txt"

# since we do not have access to testing data we need to
# take a number of images from the training data and use it instead
NUM_CLASSES = 200
NUM_TEST_IMAGES = 50 * NUM_CLASSES

# define the path to the output training, validation, and testing HDF5 files
TRAIN_HDF5 = "../datasets/tiny-imagenet-200/hdf5/train.hdf5"
VAL_HDF5 = "../datasets/tiny-imagenet-200/hdf5/val.hdf5"
TEST_HDF5 = "../datasets/tiny-imagenet-200/hdf5/test.hdf5"

# define the path to the dataset mean
DATASET_MEAN = "output/tiny-imagenet-200-mean.json"

# define the path to the output directory used for storing plots,
# classification reports, etc
OUTPUT_PATH = "output"
MODEL_PATH = path.sep.join([OUTPUT_PATH, "checkpoints/epochs_70.hdf5"])
# MODEL_PATH = path.sep.join([OUTPUT_PATH, "googlenet_tiny_imagenet_2.hdf5"])
FIG_PATH = path.sep.join([OUTPUT_PATH, "googlenet_tinyimagenet_3.png"])
JSON_PATH = path.sep.join([OUTPUT_PATH, "googlenet_tinyimagenet_3.json"])