-
Notifications
You must be signed in to change notification settings - Fork 1.1k
/
beit-base-p16_8xb64_in1k.py
43 lines (41 loc) · 1.12 KB
/
beit-base-p16_8xb64_in1k.py
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
_base_ = [
'../../_base_/datasets/imagenet_bs64_swin_224.py',
'../../_base_/schedules/imagenet_bs1024_adamw_swin.py',
'../../_base_/default_runtime.py'
]
data_preprocessor = dict(
num_classes=1000,
# RGB format normalization parameters
mean=[127.5, 127.5, 127.5],
std=[127.5, 127.5, 127.5],
# convert image from BGR to RGB
to_rgb=True,
)
model = dict(
type='ImageClassifier',
backbone=dict(
type='BEiTViT',
arch='base',
img_size=224,
patch_size=16,
out_type='avg_featmap',
use_abs_pos_emb=False,
use_rel_pos_bias=True,
use_shared_rel_pos_bias=False,
),
neck=None,
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=768,
loss=dict(
type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
),
init_cfg=[
dict(type='TruncNormal', layer='Linear', std=.02),
dict(type='Constant', layer='LayerNorm', val=1., bias=0.),
],
train_cfg=dict(augments=[
dict(type='Mixup', alpha=0.8),
dict(type='CutMix', alpha=1.0)
]))