-
Notifications
You must be signed in to change notification settings - Fork 5
/
Copy pathconfig.yml
136 lines (125 loc) · 2.59 KB
/
config.yml
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
# Test runs on 2x RTX 2080Ti
default_run:
epochs: 10
batch: 256
lr: 0.001
img_size: 224
checkpoint:
parallel: True # Use torch.cuda.device_count() instead?
img_folder: "adjacent_hu_cropped"
architecture: "efficientnet-b0"
stage: "test1"
cv_scheme: "team_folds_v1"
# Test runs on GTX 1080
default_run_1080:
epochs: 10
batch: 128
lr: 0.001
img_size: 224
checkpoint:
parallel: False
img_folder: "adjacent_hu_cropped"
architecture: "efficientnet-b0"
stage: "test1"
cv_scheme: "team_folds_v1"
# Stage 1 training runs
efficientnetb0:
epochs: 10
batch: 256
lr: 0.001
img_size: 224
checkpoint:
parallel: True # Use torch.cuda.device_count() instead?
img_folder: "adjacent_hu_cropped"
architecture: "efficientnet-b0"
stage: "test1"
cv_scheme: "team_folds_v1"
efficientnetb5:
epochs: 10
batch: 24
lr: 0.001
img_size: 456
checkpoint:
parallel: True
img_folder: "adjacent_hu_cropped"
architecture: "efficientnet-b5"
stage: "test1"
cv_scheme: "team_folds_v1"
efficientnetb3:
epochs: 10
batch: 64
lr: 0.001
img_size: 300
checkpoint:
parallel: True
img_folder: "brain-subdural-bone"
architecture: "efficientnet-b3"
stage: "test1"
cv_scheme: "team_folds_v2"
densenet169:
epochs: 10
batch: 32
lr: 0.001
img_size: 448
checkpoint:
parallel: True
img_folder: "adjacent_hu_cropped"
architecture: "densenet169"
stage: "test1"
cv_scheme: "team_folds_v2"
seresnext:
epochs: 10
batch: 20
lr: 0.001
img_size: 512
checkpoint:
parallel: True
img_folder: "adjacent_hu_cropped"
architecture: "seresnext"
stage: "test1"
cv_scheme: "team_folds_v2"
vgg19:
epochs: 10
batch: 64
lr: 0.001
img_size: 512
checkpoint:
parallel: True
img_folder: "adjacent_hu_cropped"
architecture: "vgg19"
stage: "test1"
cv_scheme: "team_folds_v2"
# Stage 2 inference runs
efficientnetb0-stage2:
epochs: 1
batch: 256
lr: 0.001
img_size: 224
checkpoint: "20191021-213751"
parallel: True # Use torch.cuda.device_count() instead?
img_folder: "adjacent_hu_cropped"
architecture: "efficientnet-b0"
stage: "test2"
cv_scheme: "team_folds_v1"
efficientnetb5-stage2:
epochs: 1
batch: 24
lr: 0.001
img_size: 456
checkpoint: "20191024-052723"
parallel: True
img_folder: "adjacent_hu_cropped"
architecture: "efficientnet-b5"
stage: "test2"
cv_scheme: "team_folds_v1"
efficientnetb3-stage2:
epochs: 1
batch: 64
lr: 0.001
img_size: 300
checkpoint: "20191101-063045"
parallel: True
img_folder: "brain-subdural-bone"
architecture: "efficientnet-b3"
stage: "test2"
cv_scheme: "team_folds_v2"