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# mean and std in RGB order, actually this part should remain unchanged as long as your dataset is similar to coco.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
# this is coco anchors, change it if necessary
#anchors_scales: '[2 ** 0, 2 ** 1/3, 2 ** 2/3]'
#anchors_ratios: '[(0.7, 1.4), (1.0, 1.0), (1.4, 0.7)]'
#anchors_scales: '[5.125, 0.625, 1.625]'
#anchors_ratios: '[(1, 0.7317073170731707), (1, 0.85), (1, 0.7884615384615384)]'
anchors_scales: '[2**0, 2**0.70, 2**1.32]'
anchors_ratios: '[(0.62, 1.58), (1.0, 1.0), (1.58, 0.62)]'
# must match your dataset's category_id.
# category_id is one_indexed,
# for example, index of 'car' here is 2, while category_id of is 3
#obj_list: ['pedestrian',
# 'rider',
# 'car',
# 'truck',
# 'bus',
# 'train',
# 'motorcycle',
# 'bicycle',
# 'traffic light',
# 'traffic sign']
obj_list: ['car']
seg_list: ['road',
'lane']
dataset:
color_rgb: false
dataroot: ./datasets/bdd100k
labelroot: ./datasets/data2/zwt/bdd/bdd100k/labels/100k
laneroot: ./datasets/bdd_lane_gt
maskroot: ./datasets/bdd_seg_gt
data_format: jpg
flip: true
hsv_h: 0.015
hsv_s: 0.7
hsv_v: 0.4
org_img_size:
- 720
- 1280
rot_factor: 10
scale_factor: 0.25
shear: 0.0
test_set: val
train_set: train
translate: 0.1
model:
image_size:
- 640
- 384
need_autoanchor: false
pin_memory: false
num_seg_class: 2 |