Inputs to Yolo

LABELS = ['Shirt', 'Trousers', 'Swimwear', 'Tie', 'Bus', 'Truck', 'Train', 'Motorcycle', 'Helmet', 'Shorts', 'Airplane', 
         'Sunglasses', 'Jacket', 'Dress', 'Human eye', 'Suit', 'Footwear', 'Woman', 'Human face', 'Man', 'Human arm', 
          'Human head','Human hand', 'Human leg', 'Human nose', 'Human mouth', 'Human ear', 'Human beard', 'Human foot', 'Car',
          'Wheel', 'Boat', 'House', 'Bird', 'Guitar', 'Fast food', 'Hat', 'Dog', 'Laptop', 'Beer', 'Cat', 'Lantern', 'Fountain']

# Setting the input image size to 608 X 608
IMAGE_H, IMAGE_W = 608, 608

# We wil use 19X19 grids for our images. This will lead us to a total of 608/19 = 32 grids for an image
GRID_H,  GRID_W  = 19 , 19
BOX              = 5
# Getting the total number of classes/labels we will be predicting.
CLASS            = len(LABELS)

# Assigning 1's to all class labels
CLASS_WEIGHTS    = np.ones(CLASS, dtype='float32')

# Pr (object in class) * Pr (class of the object) < Obj_threshold, then it disregards this anchor box
OBJ_THRESHOLD    = 0.3#0.5

# If there are many overlapping boxes and IOU is > NMS_thereshold, then we will drop the one with a lower probability. 
NMS_THRESHOLD    = 0.3#0.45

# Anchor Boxes Dimensions
ANCHORS          = [0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282, 3.52778, 9.77052, 9.16828]

NO_OBJECT_SCALE  = 1.0
OBJECT_SCALE     = 5.0
COORD_SCALE      = 1.0
CLASS_SCALE      = 1.0

BATCH_SIZE       = 16
WARM_UP_BATCHES  = 0
TRUE_BOX_BUFFER  = 50