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| # shape of input image to YOLO | |
| W, H = 448, 448 | |
| # grid size after last convolutional layer of YOLO | |
| S = 7 | |
| # anchors of YOLO model | |
| ANCHORS = [[1.5340836003942058, 1.258424277571925], | |
| [1.4957766780406023, 2.2319885681948217], | |
| [1.2508985343739407, 0.8233350471152914]] | |
| # number of anchors boxes | |
| BOX = len(ANCHORS) | |
| # maximum number of stamps on image | |
| STAMP_NB_MAX = 10 | |
| # minimal confidence of presence a stamp in the grid cell | |
| OUTPUT_THRESH = 0.7 | |
| # maximal iou score to consider boxes different | |
| IOU_THRESH = 0.3 | |
| # path to folder containing images | |
| IMAGE_FOLDER = './data/images' | |
| # path to .cvs file containing annotations | |
| ANNOTATIONS_PATH = './data/all_annotations.csv' | |
| # standard deviation and mean of pixel values for normalization | |
| STD = (0.229, 0.224, 0.225) | |
| MEAN = (0.485, 0.456, 0.406) | |
| # box color to show the bounding box on image | |
| BOX_COLOR = (0, 0, 255) | |
| # dimenstion of image embedding | |
| Z_DIM = 128 | |
| # hidden dimensions for encoder model | |
| ENC_HIDDEN_DIM = 16 | |
| # hidden dimensions for decoder model | |
| DEC_HIDDEN_DIM = 64 |