CUB-200 Image Classification Test Configuration

cub_image_config_test.yaml

# Testing specific configuration
RANDOM_SEED : 1.0

SESSION_CONFIG : {
  LOG_DEVICE_PLACEMENT : false,
  PER_PROCESS_GPU_MEMORY_FRACTION : 0.9
}
#################################################
# Metrics

ACCURACY_AT_K_METRIC : [3, 5]

# END: Metrics
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# Dataset Info

NUM_CLASSES : 200

NUM_TEST_EXAMPLES : 5794

BATCH_SIZE : 32

MODEL_NAME : 'inception_v3'

# END: Dataset Info
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# Image Processing and Augmentation 

IMAGE_PROCESSING : {
    INPUT_SIZE : 299,

    # 1) First we extract regions from the image
    REGION_TYPE : 'image', # one of 'image' or 'bbox'
    BBOX_CFG : {
        DO_EXPANSION : 0,
        EXPANSION_CFG : {
            WIDTH_EXPANSION : 2.0, # Expand the width by a factor of 2 (centrally)
            HEIGHT_EXPANSION : 2.0, # Expand the height by a factor of 2 (centrally)
        }
    },
    WHOLE_IMAGE_CFG: {},

    # Fraction of time to take a random crop from the image, 0 is never, 1 is always
    DO_RANDOM_CROP : 0,
    RANDOM_CROP_CFG: {
            MIN_AREA : 0.5, # between 0 and 1
            MAX_AREA : 1.0, # between 0 and 1
            MIN_ASPECT_RATIO : 0.7, 
            MAX_ASPECT_RATIO : 1.33,
            MAX_ATTEMPTS : 100, # maximum number of attempts to satisfy the above constraints before returning the whole image
    },    
    
    # Alternatively we can take a central crop from the image
    DO_CENTRAL_CROP : 1, # Fraction of the time to take a central crop, 0 is never, 1 is always
    CENTRAL_CROP_FRACTION : 0.875, # Between 0 and 1, fraction of size to crop

    # 2) We need to resize the extracted regions to feed into the network. 
    MAINTAIN_ASPECT_RATIO : false,
    # Avoid slower resize operations (bi-cubic, etc.)
    RESIZE_FAST : true,

    # 3) We can flip the regions
    # Randomly flip the image left right, 50% chance of flipping
    DO_RANDOM_FLIP_LEFT_RIGHT : false,

    # 4) We can distort the colors of the regions
    # Color distortion
    # The fraction of time to distort the color, 0 is never, 1 is always
    DO_COLOR_DISTORTION : 0, 
    # Avoids slower ops (random_hue and random_contrast)
    COLOR_DISTORT_FAST : false
}

# END: Image Processing and Augmentation
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# Queues
#
# Number of threads to populate the batch queue
NUM_INPUT_THREADS : 2
# Should the data be shuffled? 
SHUFFLE_QUEUE : false
# Capacity of the queue producing batched examples
QUEUE_CAPACITY : 1000
# Minimum size of the queue to ensure good shuffling
QUEUE_MIN :  200

# END: Queues
#################################################
# Regularization 
#
# The decay to use for the moving average. If 0, then moving average is not computed
MOVING_AVERAGE_DECAY : 0.9999

# End: Regularization
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