mmpretrain/configs/xcit/xcit-small-24-p16_8xb128_in...

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814 B
Python

_base_ = [
'../_base_/datasets/imagenet_bs64_swin_224.py',
'../_base_/schedules/imagenet_bs1024_adamw_swin.py',
'../_base_/default_runtime.py',
]
model = dict(
type='ImageClassifier',
backbone=dict(
type='XCiT',
patch_size=16,
embed_dims=384,
depth=24,
num_heads=8,
mlp_ratio=4,
qkv_bias=True,
layer_scale_init_value=1e-5,
tokens_norm=True,
out_type='cls_token',
),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=384,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
),
train_cfg=dict(augments=[
dict(type='Mixup', alpha=0.8),
dict(type='CutMix', alpha=1.0),
]),
)
# dataset settings
train_dataloader = dict(batch_size=128)