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auto_scale_lr = dict(base_batch_size=96)
custom_hooks = [
dict(momentum=0.0001, priority='ABOVE_NORMAL', type='EMAHook'),
]
data_preprocessor = dict(
mean=[
123.675,
116.28,
103.53,
],
num_classes=2,
std=[
58.395,
57.12,
57.375,
],
to_rgb=True)
dataset_type = 'CustomDataset'
default_hooks = dict(
checkpoint=dict(interval=2, type='CheckpointHook'),
logger=dict(interval=100, type='LoggerHook'),
param_scheduler=dict(type='ParamSchedulerHook'),
sampler_seed=dict(type='DistSamplerSeedHook'),
timer=dict(type='IterTimerHook'),
visualization=dict(
enable=True,
interval=1,
out_dir=None,
type='VisualizationHook',
wait_time=2))
default_scope = 'mmpretrain'
env_cfg = dict(
cudnn_benchmark=False,
dist_cfg=dict(backend='nccl'),
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
launcher = 'none'
load_from = './ConvNeXt_v2-v2_ep90.pth'
log_level = 'INFO'
model = dict(
backbone=dict(
arch='tiny',
drop_path_rate=0.5,
layer_scale_init_value=0.0,
type='ConvNeXt',
use_grn=True),
head=dict(
in_channels=768,
init_cfg=None,
loss=dict(label_smooth_val=0.2, type='LabelSmoothLoss'),
num_classes=2,
type='LinearClsHead'),
init_cfg=dict(
bias=0.0, layer=[
'Conv2d',
'Linear',
], std=0.02, type='TruncNormal'),
train_cfg=dict(augments=[
dict(alpha=0.8, type='Mixup'),
dict(alpha=1.0, type='CutMix'),
]),
type='ImageClassifier')
optim_wrapper = dict(
accumulative_counts=3,
clip_grad=None,
loss_scale='dynamic',
optimizer=dict(
betas=(
0.9,
0.999,
),
eps=1e-08,
lr=0.00032,
type='AdamW',
weight_decay=0.05),
paramwise_cfg=dict(
bias_decay_mult=0.0,
custom_keys=dict({
'.absolute_pos_embed': dict(decay_mult=0.0),
'.relative_position_bias_table': dict(decay_mult=0.0)
}),
flat_decay_mult=0.0,
norm_decay_mult=0.0),
type='AmpOptimWrapper')
param_scheduler = [
dict(
by_epoch=True,
convert_to_iter_based=True,
end=2,
start_factor=0.001,
type='LinearLR'),
dict(begin=2, by_epoch=True, eta_min=8e-05, type='CosineAnnealingLR'),
]
randomness = dict(deterministic=False, seed=None)
resume = False
test_cfg = dict()
test_dataloader = dict(
batch_size=16,
collate_fn=dict(type='default_collate'),
dataset=dict(
data_root='./testimgs',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
backend='pillow',
interpolation='bicubic',
scale=384,
type='Resize'),
dict(type='PackInputs'),
],
type='CustomDataset'),
num_workers=5,
persistent_workers=True,
pin_memory=True,
sampler=dict(shuffle=False, type='DefaultSampler'))
test_evaluator = dict(topk=(1, ), type='Accuracy')
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(backend='pillow', interpolation='bicubic', scale=384, type='Resize'),
dict(type='PackInputs'),
]
train_cfg = dict(by_epoch=True, max_epochs=120, val_interval=1)
train_dataloader = dict(
batch_size=32,
collate_fn=dict(type='default_collate'),
dataset=dict(
data_root='./procset',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
backend='pillow',
interpolation='bicubic',
scale=384,
type='RandomResizedCrop'),
dict(direction='horizontal', prob=0.5, type='RandomFlip'),
dict(type='PackInputs'),
],
type='CustomDataset'),
num_workers=5,
persistent_workers=True,
pin_memory=True,
sampler=dict(shuffle=True, type='DefaultSampler'))
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
backend='pillow',
interpolation='bicubic',
scale=384,
type='RandomResizedCrop'),
dict(direction='horizontal', prob=0.5, type='RandomFlip'),
dict(type='PackInputs'),
]
val_cfg = dict()
val_dataloader = dict(
batch_size=16,
collate_fn=dict(type='default_collate'),
dataset=dict(
data_root='./valset',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
backend='pillow',
interpolation='bicubic',
scale=384,
type='Resize'),
dict(type='PackInputs'),
],
type='CustomDataset'),
num_workers=5,
persistent_workers=True,
pin_memory=True,
sampler=dict(shuffle=False, type='DefaultSampler'))
val_evaluator = dict(topk=(1, ), type='Accuracy')
vis_backends = [
dict(type='LocalVisBackend'),
]
visualizer = dict(
type='UniversalVisualizer', vis_backends=[
dict(type='LocalVisBackend'),
])
work_dir = './work_dirs\\convnext-v2-tiny_32xb32_in1k-384px'
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