diff --git a/config.py b/config.py
new file mode 100644
index 0000000..000799f
--- /dev/null
+++ b/config.py
@@ -0,0 +1,287 @@
+rand_increasing_policies = [
+    dict(type='AutoContrast'),
+    dict(type='Equalize'),
+    dict(type='Invert'),
+    dict(type='Rotate', magnitude_key='angle', magnitude_range=(0, 30)),
+    dict(type='Posterize', magnitude_key='bits', magnitude_range=(4, 0)),
+    dict(type='Solarize', magnitude_key='thr', magnitude_range=(256, 0)),
+    dict(
+        type='SolarizeAdd',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 110)),
+    dict(
+        type='ColorTransform',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.9)),
+    dict(type='Contrast', magnitude_key='magnitude', magnitude_range=(0, 0.9)),
+    dict(
+        type='Brightness', magnitude_key='magnitude',
+        magnitude_range=(0, 0.9)),
+    dict(
+        type='Sharpness', magnitude_key='magnitude', magnitude_range=(0, 0.9)),
+    dict(
+        type='Shear',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.3),
+        direction='horizontal'),
+    dict(
+        type='Shear',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.3),
+        direction='vertical'),
+    dict(
+        type='Translate',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.45),
+        direction='horizontal'),
+    dict(
+        type='Translate',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.45),
+        direction='vertical')
+]
+dataset_type = 'ImageNet'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='RandomResizedCrop',
+        size=224,
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+    dict(
+        type='RandAugment',
+        policies=rand_increasing_policies,
+        num_policies=2,
+        total_level=10,
+        magnitude_level=9,
+        magnitude_std=0.5,
+        hparams=dict(pad_val=[104, 116, 124], interpolation='bicubic')),
+    dict(
+        type='RandomErasing',
+        erase_prob=0.25,
+        mode='rand',
+        min_area_ratio=0.02,
+        max_area_ratio=0.3333333333333333,
+        fill_color=[103.53, 116.28, 123.675],
+        fill_std=[57.375, 57.12, 58.395]),
+    dict(
+        type='Normalize',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=True),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='ToTensor', keys=['gt_label']),
+    dict(type='Collect', keys=['img', 'gt_label'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='Resize',
+        size=(256, -1),
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type='CenterCrop', crop_size=224),
+    dict(
+        type='Normalize',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=True),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+data = dict(
+    samples_per_gpu=32,
+    workers_per_gpu=4,
+    train=dict(
+        type='ImageNet',
+        data_prefix='/data/vdb/ziyuan.tw/yimian/gzn/datasets/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='RandomResizedCrop',
+                size=224,
+                backend='pillow',
+                interpolation='bicubic'),
+            dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+            dict(
+                type='RandAugment',
+                policies=[
+                    dict(type='AutoContrast'),
+                    dict(type='Equalize'),
+                    dict(type='Invert'),
+                    dict(
+                        type='Rotate',
+                        magnitude_key='angle',
+                        magnitude_range=(0, 30)),
+                    dict(
+                        type='Posterize',
+                        magnitude_key='bits',
+                        magnitude_range=(4, 0)),
+                    dict(
+                        type='Solarize',
+                        magnitude_key='thr',
+                        magnitude_range=(256, 0)),
+                    dict(
+                        type='SolarizeAdd',
+                        magnitude_key='magnitude',
+                        magnitude_range=(0, 110)),
+                    dict(
+                        type='ColorTransform',
+                        magnitude_key='magnitude',
+                        magnitude_range=(0, 0.9)),
+                    dict(
+                        type='Contrast',
+                        magnitude_key='magnitude',
+                        magnitude_range=(0, 0.9)),
+                    dict(
+                        type='Brightness',
+                        magnitude_key='magnitude',
+                        magnitude_range=(0, 0.9)),
+                    dict(
+                        type='Sharpness',
+                        magnitude_key='magnitude',
+                        magnitude_range=(0, 0.9)),
+                    dict(
+                        type='Shear',
+                        magnitude_key='magnitude',
+                        magnitude_range=(0, 0.3),
+                        direction='horizontal'),
+                    dict(
+                        type='Shear',
+                        magnitude_key='magnitude',
+                        magnitude_range=(0, 0.3),
+                        direction='vertical'),
+                    dict(
+                        type='Translate',
+                        magnitude_key='magnitude',
+                        magnitude_range=(0, 0.45),
+                        direction='horizontal'),
+                    dict(
+                        type='Translate',
+                        magnitude_key='magnitude',
+                        magnitude_range=(0, 0.45),
+                        direction='vertical')
+                ],
+                num_policies=2,
+                total_level=10,
+                magnitude_level=9,
+                magnitude_std=0.5,
+                hparams=dict(pad_val=[104, 116, 124],
+                             interpolation='bicubic')),
+            dict(
+                type='RandomErasing',
+                erase_prob=0.25,
+                mode='rand',
+                min_area_ratio=0.02,
+                max_area_ratio=0.3333333333333333,
+                fill_color=[103.53, 116.28, 123.675],
+                fill_std=[57.375, 57.12, 58.395]),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='ToTensor', keys=['gt_label']),
+            dict(type='Collect', keys=['img', 'gt_label'])
+        ],
+        ann_file='/data/vdb/ziyuan.tw/yimian/gzn/datasets/virgo_data/dailytags/train_mmcls.txt',
+        classes='/data/vdb/ziyuan.tw/yimian/gzn/datasets/virgo_data/dailytags/classname.txt'),
+    val=dict(
+        type='ImageNet',
+        data_prefix='/data/vdb/ziyuan.tw/yimian/gzn/datasets/',
+        ann_file='/data/vdb/ziyuan.tw/yimian/gzn/datasets/virgo_data/dailytags/val_mmcls.txt',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='Resize',
+                size=(256, -1),
+                backend='pillow',
+                interpolation='bicubic'),
+            dict(type='CenterCrop', crop_size=224),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ],
+        classes='/data/vdb/ziyuan.tw/yimian/gzn/datasets/virgo_data/dailytags/classname.txt'),
+    test=dict(
+        type='ImageNet',
+        data_prefix='/data/vdb/ziyuan.tw/yimian/gzn/datasets/',
+        ann_file='/data/vdb/ziyuan.tw/yimian/gzn/datasets/virgo_data/dailytags/val_mmcls.txt',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='Resize',
+                size=(256, -1),
+                backend='pillow',
+                interpolation='bicubic'),
+            dict(type='CenterCrop', crop_size=224),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ],
+        classes='/data/vdb/ziyuan.tw/yimian/gzn/datasets/virgo_data/dailytags/classname.txt'))
+evaluation = dict(interval=2, metric='accuracy', save_best='auto')
+paramwise_cfg = dict(
+    norm_decay_mult=0.0,
+    bias_decay_mult=0.0,
+    custom_keys={
+        '.cls_token': dict(decay_mult=0.0),
+        '.pos_embed': dict(decay_mult=0.0)
+    })
+
+optimizer = dict(
+    type='AdamW',
+    lr=2e-5, #5e-4 * 32 * 1 / 512, 1.25e-4  
+    weight_decay=0.1,
+    eps=1e-8,
+    betas=(0.9, 0.999),
+    paramwise_cfg=paramwise_cfg)
+optimizer_config = dict(grad_clip=dict(max_norm=5.0))
+
+# learning policy
+lr_config = dict(
+    policy='CosineAnnealing',
+    by_epoch=False,
+    min_lr_ratio=1e-2, 
+    warmup='linear',
+    warmup_ratio=1e-3,
+    warmup_iters=20,
+    warmup_by_epoch=True)
+runner = dict(type='EpochBasedRunner', max_epochs=300)
+checkpoint_config = dict(interval=1, max_keep_ckpts=20, create_symlink=True)
+log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')])
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+load_from = None
+resume_from = None
+workflow = [('train', 1)]
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='NextViT',
+        arch='small',
+        path_dropout=0.2,
+    ),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1296,
+        in_channels=1024,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+    ),
+)
+custom_hooks = [dict(type='EMAHook', momentum=4e-05, priority='ABOVE_NORMAL')]
+work_dir = './work_dir/'
+gpu_ids = range(0, 32)