diff --git a/.gitattributes b/.gitattributes
index a6344aa..97c8e80 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -1,35 +1,32 @@
 *.7z filter=lfs diff=lfs merge=lfs -text
 *.arrow filter=lfs diff=lfs merge=lfs -text
 *.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
 *.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
 *.ftz filter=lfs diff=lfs merge=lfs -text
 *.gz filter=lfs diff=lfs merge=lfs -text
 *.h5 filter=lfs diff=lfs merge=lfs -text
 *.joblib filter=lfs diff=lfs merge=lfs -text
 *.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
 *.model filter=lfs diff=lfs merge=lfs -text
 *.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
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 *.parquet filter=lfs diff=lfs merge=lfs -text
 *.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
 *.pt filter=lfs diff=lfs merge=lfs -text
 *.pth filter=lfs diff=lfs merge=lfs -text
 *.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
 saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
 *.tflite filter=lfs diff=lfs merge=lfs -text
 *.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
 *.xz filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*.tfevents* filter=lfs diff=lfs merge=lfs -text
+*.db* filter=lfs diff=lfs merge=lfs -text
+*.ark* filter=lfs diff=lfs merge=lfs -text
+**/*ckpt*data* filter=lfs diff=lfs merge=lfs -text
+**/*ckpt*.meta filter=lfs diff=lfs merge=lfs -text
+**/*ckpt*.index filter=lfs diff=lfs merge=lfs -text
diff --git a/README.md b/README.md
index 8b1bca6..f3a6632 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,202 @@
-# ViT_image_classification
+---
+tasks:
+- image-classification
+model-type:
+- Transformer
+domain:
+- cv
+frameworks:
+- pytorch
+backbone:
+- ViT-Base
+metrics:
+- accuracy
+finetune-support: True
+customized-quickstart: True
+integrating: False
+license: Apache License 2.0
+tags:
+- Alibaba
+- Image classification
+- Transformer
+- Dailylife tags
+datasets:
+  evaluation:
+  - online dataset
+  train:
+  - tany0699/dailytags
+  test: 
+  - tany0699/dailytags
 
-ViT是2020年Google团队提出的将Transformer应用在图像分类的模型，虽然不是第一篇将transformer应用在视觉任务的论文，但是因为其模型‘’简单”且效果好，可扩展性强（scalable，模型越大效果越好），成为了transformer在CV领域应用的里程碑著作，也引爆了后续相关研究。
+widgets:
+  - task: image-classification
+    inputs:
+      - type: image
+    examples:
+      - name: 1
+        inputs:
+          - name: image
+            data: git://resources/test.jpg
+---
+
+# 日常物体识别模型介绍
+自建1300类常见物体标签体系，覆盖常见的日用品，动物，植物，家具，设备，食物等物体，标签从海量中文互联网社区语料进行提取，保留了出现频率较高的常见物体名称。模型结构采用最新的ViT-Base结构。  
+创空间快速可视化展示: [ViT图像分类-中文-日常物品](https://modelscope.cn/studios/tany0699/cv_vit-base_image-classification_Dailylife-labels/summary)  
+  
+本系列还有如下模型，欢迎试用:  
+- [ViT图像分类-通用](https://modelscope.cn/models/damo/cv_vit-base_image-classification_ImageNet-labels/summary)
+- [NextViT实时图像分类-中文-日常物品](https://modelscope.cn/models/damo/cv_nextvit-small_image-classification_Dailylife-labels/summary)
+- [ConvNeXt图像分类-中文-垃圾分类](https://modelscope.cn/models/damo/cv_convnext-base_image-classification_garbage/summary)
+- [BEiTv2图像分类-通用-base](https://modelscope.cn/models/damo/cv_beitv2-base_image-classification_patch16_224_pt1k_ft22k_in1k/summary)
+- [BEiTv2图像分类-通用-large](https://modelscope.cn/models/damo/cv_beitv2-large_image-classification_patch16_224_pt1k_ft22k_in1k/summary)
+
+## 模型描述
+
+采用Transformer经典的[ViT-Base](https://github.com/google-research/vision_transformer)结构, 并采用了DeiT的知识蒸馏方式进行训练。  
+<img src="./resources/overview.jpg" alt="overview"/>
+
+## 期望模型使用方式以及适用范围
+
+本模型适用范围较广，覆盖大部分日常生活常见的物品类目，包括日用品，动物，植物，家具，设备，食物等。也可作为下游任务的预训练backbone。
+
+### 如何使用
+
+在ModelScope框架上，提供输入图片，即可通过简单的Pipeline调用来使用。
+
+#### 代码范例
+```python
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+img_path = 'https://modelscope.oss-cn-beijing.aliyuncs.com/test/images/bird.JPEG'
+image_classification = pipeline(Tasks.image_classification, 
+                                model='damo/cv_vit-base_image-classification_Dailylife-labels')
+result = image_classification(img_path)
+print(result)
+```
+
+### 模型局限性以及可能的偏差
+
+- 支持1300类常见物体识别
+
+
+## 训练数据介绍
+
+- 140万包含常见物体的图像集
+
+
+## 模型训练流程
+
+- 主要训练参数参考[DeiT论文](https://arxiv.org/abs/2012.12877)的设置，除了weight decay在复现时设置为0.1，模型训练未使用pretrained参数进行初始化。
+
+### 预处理
+
+测试时主要的预处理如下：
+- Resize：先将原始图片的短边缩放至256
+- Normalize：图像归一化，减均值除以标准差
+- CenterCrop：裁切为224x224
+
+## 数据评估及结果
+
+模型在自建测试集进行测试，结果如下:
+
+| Model | top-1 acc | top-5 acc | #params  | Remark       | 
+|:--------:|:-------:|:--------:|:-------:|--------------|
+|  ViT-base  | 74.3   |  95.3   |  86M   | modelscope |
+
+
+## 模型训练
+使用托管在modelscope DatasetHub上的小型数据集[mini_imagenet100](https://modelscope.cn/datasets/tany0699/mini_imagenet100/summary)进行finetune训练的示例代码: 
+
+```python
+from modelscope.msdatasets import MsDataset
+from modelscope.metainfo import Trainers
+from modelscope.trainers import build_trainer
+import tempfile
+
+model_id = 'damo/cv_vit-base_image-classification_Dailylife-labels'
+
+# 加载数据
+ms_train_dataset = MsDataset.load(
+            'mini_imagenet100', namespace='tany0699',
+            subset_name='default', split='train')      # 加载训练集
+
+ms_val_dataset = MsDataset.load(
+            'mini_imagenet100', namespace='tany0699',
+            subset_name='default', split='validation') # 加载验证集
+
+tmp_dir = tempfile.TemporaryDirectory().name # 使用临时目录作为工作目录
+
+# 修改配置文件
+def cfg_modify_fn(cfg):
+    cfg.train.dataloader.batch_size_per_gpu = 32 # batch大小
+    cfg.train.dataloader.workers_per_gpu = 2     # 每个gpu的worker数目
+    cfg.train.max_epochs = 1                     # 最大训练epoch数
+    cfg.model.mm_model.head.num_classes = 100                     # 分类数
+    cfg.model.mm_model.train_cfg.augments[0].num_classes = 100    # 分类数
+    cfg.model.mm_model.train_cfg.augments[1].num_classes = 100    # 分类数
+    cfg.train.optimizer.lr = 1e-4                # 学习率
+    cfg.train.lr_config.warmup_iters = 1         # 预热次数
+    cfg.train.evaluation.metric_options = {'topk': (1, 5)}  # 训练时的评估指标
+    cfg.evaluation.metric_options = {'topk': (1, 5)}        # 评估时的评估指标
+    return cfg
+
+# 构建训练器
+kwargs = dict(
+    model=model_id,                 # 模型id
+    work_dir=tmp_dir,               # 工作目录
+    train_dataset=ms_train_dataset, # 训练集  
+    eval_dataset=ms_val_dataset,    # 验证集
+    cfg_modify_fn=cfg_modify_fn,    # 用于修改训练配置文件的回调函数
+    model_revision='v1.0.2'
+    )
+trainer = build_trainer(name=Trainers.image_classification, default_args=kwargs)
+
+# 进行训练
+trainer.train()
+
+# 进行评估
+result = trainer.evaluate()
+print('result:', result)
+```
+训练说明见示例代码中的注释，更详细的训练说明和用法见官方的[训练文档](https://modelscope.cn/docs/%E6%A8%A1%E5%9E%8B%E7%9A%84%E8%AE%AD%E7%BB%83Train)。训练过程产生的log和模型权重文件保存在work_dir工作目录中，并以前缀为'best_'的文件保存了在验证集上最优精度的权重。evaluate()默认使用精度最高的模型权重进行评估。
+
+
+## 模型评估
+使用训练好的模型对需要评估的数据集进行精度评估示例代码如下:
+
+```python
+from modelscope.msdatasets import MsDataset
+from modelscope.metainfo import Trainers
+from modelscope.trainers import build_trainer
+import tempfile
+
+model_id = 'damo/cv_vit-base_image-classification_Dailylife-labels'
+
+# 加载用于评估的数据集
+ms_val_dataset = MsDataset.load(
+            'dailytags', namespace='tany0699',
+            subset_name='default', split='validation') 
+
+tmp_dir = tempfile.TemporaryDirectory().name # 使用临时目录作为工作目录
+
+# 构建训练器
+kwargs = dict(
+    model=model_id,                 # 模型id
+    work_dir=tmp_dir,               # 工作目录
+    train_dataset=None,  
+    eval_dataset=ms_val_dataset,    # 评估的数据集
+    model_revision='v1.0.2'
+    )
+trainer = build_trainer(name=Trainers.image_classification, default_args=kwargs)
+
+# 开始评估
+result = trainer.evaluate()
+print('result:', result)
+```
+评估过程默认使用模型中自带的预训练权重进行评估。  
+
+#### Clone with HTTP
+```bash
+ git clone https://www.modelscope.cn/damo/cv_vit-base_image-classification_Dailylife-labels.git
+```
\ No newline at end of file
diff --git a/config.py b/config.py
new file mode 100644
index 0000000..ef2266e
--- /dev/null
+++ b/config.py
@@ -0,0 +1,354 @@
+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=[
+            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'])
+]
+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=16,
+    train=dict(
+        type='ImageNet',
+        data_prefix='/data/oss_bucket_0/',
+        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/oss_bucket_0/virgo_data/dailytags/train_mmcls.txt',
+        classes='/data/oss_bucket_0/virgo_data/dailytags/classname.txt'),
+    val=dict(
+        type='ImageNet',
+        data_prefix='/data/oss_bucket_0/',
+        ann_file='/data/oss_bucket_0/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/oss_bucket_0/virgo_data/dailytags/classname.txt'),
+    test=dict(
+        type='ImageNet',
+        data_prefix='/data/oss_bucket_0/',
+        ann_file='/data/oss_bucket_0/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/oss_bucket_0/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=dict({
+        '.absolute_pos_embed': dict(decay_mult=0.0),
+        '.relative_position_bias_table': dict(decay_mult=0.0),
+        '.cls_token': dict(decay_mult=0.0),
+        '.pos_embed': dict(decay_mult=0.0)
+    }))
+optimizer = dict(
+    type='AdamW',
+    lr=0.001,
+    weight_decay=0.1,
+    eps=1e-08,
+    betas=(0.9, 0.999),
+    paramwise_cfg=dict(
+        norm_decay_mult=0.0,
+        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),
+            '.cls_token': dict(decay_mult=0.0),
+            '.pos_embed': dict(decay_mult=0.0)
+        })))
+optimizer_config = dict(grad_clip=dict(max_norm=5.0))
+lr_config = dict(
+    policy='CosineAnnealing',
+    by_epoch=False,
+    min_lr_ratio=0.01,
+    warmup='linear',
+    warmup_ratio=0.001,
+    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=False)
+log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')])
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+load_from = None
+resume_from = '/data/oss_bucket_1/emian/VisualContentRecognition/mmlab/work_dirs/dailytags_deit-base_pt-16xb64_in1k_gzn2_pai_wd0.1/epoch_296.pth'
+workflow = [('train', 1)]
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='VisionTransformer',
+        arch='deit-base',
+        img_size=224,
+        patch_size=16,
+        drop_path_rate=0.1),
+    neck=None,
+    head=dict(
+        type='VisionTransformerClsHead',
+        num_classes=1296,
+        in_channels=768,
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original')),
+    init_cfg=[
+        dict(type='TruncNormal', layer='Linear', std=0.02),
+        dict(type='Constant', layer='LayerNorm', val=1.0, bias=0.0)
+    ],
+    train_cfg=dict(augments=[
+        dict(type='BatchMixup', alpha=0.8, num_classes=1296, prob=0.5),
+        dict(type='BatchCutMix', alpha=1.0, num_classes=1296, prob=0.5)
+    ]))
+custom_hooks = [dict(type='EMAHook', momentum=4e-05, priority='ABOVE_NORMAL')]
+work_dir = '/data/oss_bucket_1/emian/VisualContentRecognition/mmlab/work_dirs/dailytags_deit-base_pt-16xb64_in1k_gzn2_pai_wd0.1/'
+gpu_ids = range(0, 32)
diff --git a/configuration.json b/configuration.json
new file mode 100644
index 0000000..9bf6009
--- /dev/null
+++ b/configuration.json
@@ -0,0 +1,332 @@
+{
+    "framework":"pytorch",
+    "task":"image-classification",
+    "pipeline":{
+        "type":"vit-base_image-classification_Dailylife-labels"
+    },
+    "model":{
+        "type": "ClassificationModel",
+        "mm_model": {
+            "type": "ImageClassifier",
+            "pretrained": null,
+            "backbone": {
+                "type": "VisionTransformer",
+                "arch": "deit-base",
+                "img_size": 224,
+                "patch_size": 16,
+                "drop_path_rate": 0.1
+            },
+            "neck": null,
+            "head": {
+                "type": "VisionTransformerClsHead",
+                "num_classes": 1296,
+                "in_channels": 768,
+                "loss": {
+                    "type": "LabelSmoothLoss",
+                    "label_smooth_val": 0.1,
+                    "mode": "original"
+                }
+            },
+            "init_cfg": [
+                {
+                    "type": "TruncNormal",
+                    "layer": "Linear",
+                    "std": 0.02
+                },
+                {
+                    "type": "Constant",
+                    "layer": "LayerNorm",
+                    "val": 1.0,
+                    "bias": 0.0
+                }
+            ],
+            "train_cfg": 
+            {
+                "augments": [
+                    {
+                        "type": "BatchMixup",
+                        "alpha": 0.8,
+                        "num_classes": 1296,
+                        "prob": 0.5
+                    },
+                    {
+                        "type": "BatchCutMix",
+                        "alpha": 1.0,
+                        "num_classes": 1296,
+                        "prob": 0.5
+                    }
+                ]
+            }
+        }
+    },
+
+    "dataset": {
+        "classes": null
+    },
+
+    "preprocessor": {
+        "type": "image-classification-bypass-preprocessor",
+        "train": [
+            {   
+                "type": "LoadImageFromFile"
+            },
+            {
+                "type": "RandomResizedCrop",
+                "size": 224,
+                "backend": "pillow",
+                "interpolation": "bicubic"
+            },
+            {
+                "type": "RandomFlip",
+                "flip_prob": 0.5,
+                "direction": "horizontal"
+            },
+            {
+                "type": "RandAugment",
+                "policies": [
+                    {
+                        "type": "AutoContrast"
+                    },
+                    {
+                        "type": "Equalize"
+                    },
+                    {
+                        "type": "Invert"
+                    },
+                    {
+                        "type": "Rotate",
+                        "magnitude_key": "angle",
+                        "magnitude_range": [0, 30]
+
+                    },
+                    {
+                        "type": "Posterize",
+                        "magnitude_key": "bits",
+                        "magnitude_range": [4, 0]
+
+                    },
+                    {
+                        "type": "Solarize",
+                        "magnitude_key": "thr",
+                        "magnitude_range": [256, 0]
+
+                    },
+                    {
+                        "type": "SolarizeAdd",
+                        "magnitude_key": "magnitude",
+                        "magnitude_range": [0, 110]
+
+                    },
+                    {
+                        "type": "ColorTransform",
+                        "magnitude_key": "magnitude",
+                        "magnitude_range": [0, 0.9]
+
+                    },
+                    {
+                        "type": "Contrast",
+                        "magnitude_key": "magnitude",
+                        "magnitude_range": [0, 0.9]
+
+                    },
+                    {
+                        "type": "Brightness",
+                        "magnitude_key": "magnitude",
+                        "magnitude_range": [0, 0.9]
+
+                    },
+                    {
+                        "type": "Sharpness",
+                        "magnitude_key": "magnitude",
+                        "magnitude_range": [0, 0.9]
+
+                    },
+                    {
+                        "type": "Shear",
+                        "magnitude_key": "magnitude",
+                        "magnitude_range": [0, 0.3],
+                        "direction": "horizontal"
+
+                    },
+                    {
+                        "type": "Shear",
+                        "magnitude_key": "magnitude",
+                        "magnitude_range": [0, 0.3],
+                        "direction": "vertical"
+
+                    },
+                    {
+                        "type": "Translate",
+                        "magnitude_key": "magnitude",
+                        "magnitude_range": [0, 0.45],
+                        "direction": "horizontal"
+
+                    },
+                    {
+                        "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": {
+                    "pad_val": [104, 116, 124],
+                    "interpolation": "bicubic"
+                }
+            },
+            {
+                "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]
+            },
+            {
+                "type": "Normalize",
+                "mean": [123.675, 116.28, 103.53],
+                "std": [58.395, 57.12, 57.375],
+                "to_rgb": true
+            },
+            {
+                "type": "ImageToTensor",
+                "keys": ["img"]
+            },
+            {
+                "type": "ToTensor",
+                "keys": ["gt_label"]
+            },
+            {
+                "type": "Collect",
+                "keys": ["img", "gt_label"]
+            }
+        ],
+        
+        "val": [
+            {   
+                "type": "LoadImageFromFile"
+            },
+            {
+                "type": "Resize",
+                "size": [256, -1],
+                "backend": "pillow",
+                "interpolation": "bicubic"
+            },
+            {
+                "type": "CenterCrop",
+                "crop_size": 224
+            },
+            {
+                "type": "Normalize",
+                "mean": [123.675, 116.28, 103.53],
+                "std": [58.395, 57.12, 57.375],
+                "to_rgb": true
+            },
+            {
+                "type": "ImageToTensor",
+                "keys": ["img"]
+            },
+            {
+                "type": "Collect",
+                "keys": ["img"]
+            }
+        ]
+    },
+
+    "train": {
+        "dataloader": {
+            "batch_size_per_gpu": 32,
+            "workers_per_gpu": 4
+        },
+        "max_epochs": 1,
+        "runner": {
+            "type": "EpochBasedRunner",
+            "max_epochs": 300
+        },
+        "evaluation": {
+            "interval": 1,
+            "metric": "accuracy",
+            "save_best": "auto"
+        },
+        "checkpoint_config": {
+            "interval": 1,
+            "max_keep_ckpts": 20,
+            "create_symlink": true
+        },
+        "log_config": {
+            "interval": 100,
+            "hooks": [
+                {
+                    "type": "TextLoggerHook"
+                }
+            ]
+        },
+        "custom_hooks": [
+            {
+                "type": "EMAHook",
+                "momentum": 4e-05,
+                "priority": "ABOVE_NORMAL"
+            }
+        ],
+        "workflow": [
+            ["train", 1]
+        ],
+        "work_dir": "./work_dir",
+        "optimizer": {
+            "type": "AdamW",
+            "lr": 0.001,
+            "weight_decay": 0.1,
+            "eps": 1e-08,
+            "betas": [0.9, 0.999],
+            "paramwise_cfg": {
+                "norm_decay_mult": 0.0,
+                "bias_decay_mult": 0.0,
+                "custom_keys": {
+                    ".absolute_pos_embed": {
+                        "decay_mult": 0.0
+                    },
+                    ".relative_position_bias_table": {
+                        "decay_mult": 0.0
+                    },
+                    ".cls_token": {
+                        "decay_mult": 0.0
+                    },
+                    ".pos_embed": {
+                        "decay_mult": 0.0
+                    }
+                }
+            }
+        },
+        "optimizer_config": {
+            "grad_clip": {
+                "max_norm": 5.0
+            }
+        },
+        "lr_config": {
+            "policy": "CosineAnnealing",
+            "by_epoch": false,
+            "min_lr_ratio": 0.01,
+            "warmup": "linear",
+            "warmup_ratio": 0.001,
+            "warmup_iters": 20,
+            "warmup_by_epoch": true
+        }
+    },
+
+    "evaluation": {
+        "dataloader": {
+            "batch_size_per_gpu": 32,
+            "workers_per_gpu": 4
+        },
+        "metrics": ["accuracy"],
+        "metric_options": {
+            "topk": [1, 5]
+        }
+    }
+}
diff --git a/pytorch_model.pt b/pytorch_model.pt
new file mode 100644
index 0000000..fc9e9e0
--- /dev/null
+++ b/pytorch_model.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1732b9c9c2daad2b55b013d8a4e75a5af9b0bffe1c0843561849194f50a2bf7f
+size 347270855
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index 0000000..e69de29
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