Created
December 14, 2018 19:33
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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 |
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