config.py

import os

board_log_dir = f"{os.path.dirname(os.path.abspath(__file__))}/board_log"
save_weight_file = f"{os.path.dirname(os.path.abspath(__file__))}/weights/weights"

anchor_sizes = [[[14.000000000000002, 14.000000000000002], [22.000000000000007, 22.0], [28.0, 28.0]],
                [[42.0, 42.0], [56.00000000000001, 56.00000000000001], [84.00000000000001, 84.00000000000001]],
                [[111.99999999999997, 111.99999999999997], [200, 200], [250, 250]]]


filters = 32                        # filters基数 设为16可使所有层filters减少一半
image_target_size = (416, 416)
grid_sizes = [52, 26, 13]
layer_num = len(grid_sizes)
cell_anchor_num = 3
train_label_file = "/opt/datasets/yymnist_yolo/train_labels.txt"
train_image_dir = "/opt/datasets/yymnist_yolo/train"
val_label_file = "/opt/datasets/yymnist_yolo/val_labels.txt"
val_image_dir = "/opt/datasets/yymnist_yolo/test"
class_num = 10
batch_size = 4

metric_iou_threshold = 0.5
metric_score_threshold = 0.8

nms_iou_threshold = 0.1
nms_score_threshold = metric_score_threshold
nms_max_output = 1000

cate_names = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]