/
global_configs.yaml
31 lines (31 loc) · 1.31 KB
/
global_configs.yaml
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DATA:
COCO:
FILE_ROOT: 'D:\image_datasets\coco' # specific where is your coco dataset here
NUM_CLASSES: 80 # the coco class categories
COCO_LABELS_FILE: 'C:\Users\Super-IdoI\Desktop\python项目仓库\CvT_SSD\data_preprocess\COCO\coco_labels.txt'
MAX_ITER: 120000
LR_STEPS: [ 80000,100000,120000 ]
PRIOR_BOX:
MIN_DIM: 300
ASPECT_RATIOS: [ [ 2 ], [ 2, 3 ], [ 2, 3 ], [ 2, 3 ], [ 2 ], [ 2 ] ]
VARIANCE: [ 0.1,0.2 ]
FEATURE_MAPS: [ 38, 19, 10, 5, 3, 1 ]
MIN_SIZES: [ 30, 60, 111, 162, 213, 264 ]
MAX_SIZES: [ 60, 111, 162, 213, 264, 315 ]
STEPS: [ 8, 16, 32, 64, 100, 300 ]
CLIP: 'True' # can not be false, only set 0 or ''.
VOC:
FILE_ROOT: 'D:\image_datasets\voc' # specific where is your voc =>2007&2012 dataset here
NUM_CLASSES: 20 # the voc class category
MAX_ITER: 400000
LR_STEPS: [ 280000, 360000, 400000 ]
IMG_SETS: [ ['2007', 'trainval'], ['2012', 'trainval'] ]
PRIOR_BOX:
MIN_DIM: 300
ASPECT_RATIOS: [ [ 2 ], [ 2, 3 ], [ 2, 3 ], [ 2, 3 ], [ 2 ], [ 2 ] ]
VARIANCE: [ 0.1,0.2 ]
FEATURE_MAPS: [ 38, 19, 10, 5, 3, 1 ]
MIN_SIZES: [ 21, 45, 99, 153, 207, 261 ]
MAX_SIZES: [ 45, 99, 153, 207, 261, 315 ]
STEPS: [ 8, 16, 32, 64, 100, 300 ]
CLIP: 'True' # can not be false, only set 0 or ''.