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'