diff --git a/new/cv_serving/.dockerignore b/new/cv_serving/.dockerignore
new file mode 100644
index 0000000..0a53e44
--- /dev/null
+++ b/new/cv_serving/.dockerignore
@@ -0,0 +1,89 @@
+# Git
+.git
+.gitignore
+.gitattributes
+
+
+# CI
+.codeclimate.yml
+.travis.yml
+.taskcluster.yml
+
+# Docker
+docker-compose.yml
+Dockerfile
+.docker
+.dockerignore
+
+# Byte-compiled / optimized / DLL files
+**/__pycache__/
+**/*.py[cod]
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.cache
+nosetests.xml
+coverage.xml
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Virtual environment
+.env
+.venv/
+venv/
+
+# PyCharm
+.idea
+
+# Python mode for VIM
+.ropeproject
+**/.ropeproject
+
+# Vim swap files
+**/*.swp
+
+# VS Code
+.vscode/
diff --git a/new/cv_serving/.gitignore b/new/cv_serving/.gitignore
new file mode 100644
index 0000000..1e5b720
--- /dev/null
+++ b/new/cv_serving/.gitignore
@@ -0,0 +1,163 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+.idea/
+
+# VSCode
+.vscode/
diff --git a/new/cv_serving/Dockerfile b/new/cv_serving/Dockerfile
new file mode 100644
index 0000000..a12de8e
--- /dev/null
+++ b/new/cv_serving/Dockerfile
@@ -0,0 +1,14 @@
+FROM python:3.9.13-slim as requirements-stage
+
+WORKDIR /tmp
+RUN pip install poetry
+COPY ./pyproject.toml ./poetry.lock* /tmp/
+RUN poetry export -f requirements.txt --output requirements.txt --without-hashes
+
+FROM python:3.9.13-slim
+
+WORKDIR /code
+COPY --from=requirements-stage /tmp/requirements.txt /code/requirements.txt
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+COPY . /code
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "80"]
diff --git a/new/cv_serving/README.md b/new/cv_serving/README.md
new file mode 100644
index 0000000..969df2c
--- /dev/null
+++ b/new/cv_serving/README.md
@@ -0,0 +1,80 @@
+# CV Serving Example through Web Single Pattern
+
+CV 모델을 Web Single Pattern으로 서빙하는 예제입니다.
+
+## Pre-requisites
+
+- Python >= 3.9
+- Poetry >= 1.1.4
+
+## Installation
+
+```bash
+poetry install
+```
+
+## Run
+
+먼저 `.env` 파일을 생성하고, 아래와 같이 모델 경로를 설정합니다.
+
+```bash
+# .env
+MODEL_PATH=./model/model.pkl
+```
+
+그리고 아래와 같이 실행합니다.
+
+```bash
+PYTHONPATH=.
+poetry run python main.py
+```
+
+## Usage
+
+### Predict
+
+```bash
+curl -X POST -F "file=@./example_images/0a101263343a4a60a8dcd94d1fc8e8e253dadf14.jpg" http://0.0.0.0:8000/predict
+ 
+{"id":1,"result":4}%
+```
+
+### Get all predictions
+
+```bash
+curl "http://0.0.0.0:8000/predict"
+
+[{"id":1,"result":0},{"id":2,"result":4},{"id":3,"result":3}]
+```
+
+### Get a prediction
+
+```bash
+curl "http://0.0.0.0:8000/predict/1"
+{"id":1,"result":0}
+```
+
+## Build
+
+```bash
+docker build -t web_single_example .
+```
+
+## Project Structure
+
+```bash
+.
+├── .dockerignore    # 도커 이미지 빌드 시 제외할 파일 목록
+├── .gitignore       # git에서 제외할 파일 목록
+├── Dockerfile       # 도커 이미지 빌드 설정 파일
+├── README.md        # 프로젝트 설명 파일
+├── __init__.py
+├── api.py           # API 엔드포인트 정의 파일
+├── config.py        # Config 정의 파일
+├── database.py      # 데이터베이스 연결 파일
+├── dependencies.py  # 앱 의존성 관련 로직 파일
+├── main.py          # 앱 실행 파일
+├── model/           # 모델 관련 디렉토리
+├── poetry.lock      # Poetry 라이브러리 버전 관리 파일
+└── pyproject.toml   # Poetry 프로젝트 설정 파일
+```
\ No newline at end of file
diff --git a/new/cv_serving/__init__.py b/new/cv_serving/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/new/cv_serving/api.py b/new/cv_serving/api.py
new file mode 100644
index 0000000..c7d738c
--- /dev/null
+++ b/new/cv_serving/api.py
@@ -0,0 +1,74 @@
+from fastapi import APIRouter, HTTPException, status, UploadFile
+from pydantic import BaseModel
+from sqlmodel import Session
+from PIL import Image
+from torchvision.transforms import transforms
+
+from database import PredictionResult, engine
+from dependencies import get_model
+
+router = APIRouter()
+
+
+class PredictionResponse(BaseModel):
+    id: int
+    result: int
+
+
+# FastAPI 경로
+@router.post("/predict")
+def predict(file: UploadFile) -> PredictionResponse:
+    # 이미지 파일이 맞는지 검증
+    if not file.content_type.startswith("image/"):
+        raise HTTPException(
+            detail="File type not supported", status_code=status.HTTP_400_BAD_REQUEST
+        )
+    image = Image.open(file.file)
+
+    # 이미지 파일을 텐서로 변환
+    image_tensor = transforms.ToTensor()(image)
+
+    # 텐서의 크기가 맞는지 검증
+    if image_tensor.shape != (3, 512, 384):
+        raise HTTPException(
+            detail="Image size must be 384x512", status_code=status.HTTP_400_BAD_REQUEST
+        )
+
+    # 모델 추론
+    model = get_model()
+    prediction = int(model(image_tensor.unsqueeze(0)).argmax())
+
+    # 결과를 데이터베이스에 저장
+    prediction_result = PredictionResult(result=prediction)
+    with Session(engine) as session:
+        session.add(prediction_result)
+        session.commit()
+        session.refresh(prediction_result)
+
+    # 응답
+    return PredictionResponse(id=prediction_result.id, result=prediction)
+
+
+@router.get("/predict/{id}")
+def get_prediction(id: int) -> PredictionResponse:
+    # 데이터베이스에서 결과를 가져옴
+    with Session(engine) as session:
+        prediction_result = session.get(PredictionResult, id)
+        if not prediction_result:
+            raise HTTPException(
+                detail="Not found", status_code=status.HTTP_404_NOT_FOUND
+            )
+        return PredictionResponse(
+            id=prediction_result.id, result=prediction_result.result
+        )
+
+
+@router.get("/predict")
+def get_predictions() -> list[PredictionResponse]:
+    # 데이터베이스에서 결과를 가져옴
+    with Session(engine) as session:
+        prediction_results = session.query(PredictionResult).all()
+        return [
+            PredictionResponse(id=prediction_result.id, result=prediction_result.result)
+            for prediction_result in prediction_results
+        ]
diff --git a/new/cv_serving/config.py b/new/cv_serving/config.py
new file mode 100644
index 0000000..8f133ff
--- /dev/null
+++ b/new/cv_serving/config.py
@@ -0,0 +1,11 @@
+from pydantic import Field
+from pydantic_settings import BaseSettings
+
+
+class Config(BaseSettings):
+    db_url: str = Field(default="sqlite:///./db.sqlite3", env="DB_URL")
+    model_path: str = Field(default="model.joblib", env="MODEL_PATH")
+    num_classes: int = Field(default=18, env="NUM_CLASSES")
+
+
+config = Config()
diff --git a/new/cv_serving/database.py b/new/cv_serving/database.py
new file mode 100644
index 0000000..e7d19c7
--- /dev/null
+++ b/new/cv_serving/database.py
@@ -0,0 +1,15 @@
+import datetime
+from typing import Optional
+
+from sqlmodel import Field, SQLModel, create_engine
+
+from config import config
+
+
+class PredictionResult(SQLModel, table=True):
+    id: Optional[int] = Field(default=None, primary_key=True)
+    result: int
+    created_at: Optional[str] = Field(default_factory=datetime.datetime.now)
+
+
+engine = create_engine(config.db_url)
diff --git a/new/cv_serving/dependencies.py b/new/cv_serving/dependencies.py
new file mode 100644
index 0000000..14252ae
--- /dev/null
+++ b/new/cv_serving/dependencies.py
@@ -0,0 +1,23 @@
+import torch
+from loguru import logger
+
+from model.model import BaseModel
+
+model = None
+
+
+def load_model(model_path: str) -> None:
+    global model
+    logger.info(f"Loading model from {model_path}.")
+
+    model = BaseModel(num_classes=18)
+    device = torch.device("cpu")
+    model.load_state_dict(torch.load(model_path, map_location=device))
+
+    logger.info("Model loaded.")
+
+
+def get_model() -> BaseModel:
+    global model
+    return model
+
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diff --git a/new/cv_serving/main.py b/new/cv_serving/main.py
new file mode 100644
index 0000000..079fb66
--- /dev/null
+++ b/new/cv_serving/main.py
@@ -0,0 +1,37 @@
+from dotenv import load_dotenv
+
+load_dotenv(dotenv_path=".env")
+
+from contextlib import asynccontextmanager
+
+from fastapi import FastAPI
+from loguru import logger
+from sqlmodel import SQLModel
+
+from api import router
+from config import config
+from database import engine
+from dependencies import load_model
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    # 데이터베이스 테이블 생성
+    logger.info("Creating database tables")
+    SQLModel.metadata.create_all(engine)
+
+    # 모델 로드
+    logger.info("Loading model")
+    load_model(config.model_path)
+
+    yield
+
+
+app = FastAPI(lifespan=lifespan)
+app.include_router(router)
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="0.0.0.0", port=8000)
diff --git a/new/cv_serving/model/README.md b/new/cv_serving/model/README.md
new file mode 100644
index 0000000..7efec13
--- /dev/null
+++ b/new/cv_serving/model/README.md
@@ -0,0 +1,100 @@
+# CV 기초대회 베이스라인 코드
+
+## Project Structure
+
+```
+${PROJECT}
+├── dataset.py
+├── inference.py
+├── loss.py
+├── model.py
+├── README.md
+├── requirements.txt
+├── sample_submission.ipynb
+└── train.py
+```
+
+- dataset.py : This file contains dataset class for model training and validation
+- inference.py : This file used for predict the model
+- loss.py : This file defines the loss functions used during training
+- model.py : This file defines the model
+- README.md
+- requirements.txt : contains the necessary packages to be installed
+- sample_submission.ipynb : an example notebook for submission
+- train.py : This file used for training the model
+
+## Getting Started
+
+### Stteing up Vitual Enviornment
+
+1. Install `virtualenv` if you haven't yet:
+
+```
+pip install virtualenv
+```
+
+2. Create a virtual environment in the project directory
+
+```
+cd ${PROJECT}
+python -m venv /path/to/venv
+```
+
+3. Activate the virtual environment
+
+- On Windows:
+
+```
+.\venv\Scripts\activate
+```
+
+- On Unix or MacOS:
+
+```
+source venv/bin/activate
+```
+
+4. To deactivate and exit the virtual environment, simply run:
+
+```
+deactivate
+```
+
+### Install Requirements
+
+To Insall the necessary packages liksted in `requirements.txt`, run the following command while your virtual environment is activated:
+
+
+```
+pip install -r requirements.txt
+```
+
+### Usage
+
+#### Training
+
+To train the model with your custom dataset, set the appropriate directories for the training images and model saving, then run the training script.
+
+```
+SM_CHANNEL_TRAIN=/path/to/images SM_MODEL_DIR=/path/to/model python train.py
+```
+
+or 
+
+```
+python train.py --data_dir /path/to/images --model_dir /path/to/model
+```
+
+#### Inference
+
+For generating predictions with a trained model, provide directories for evaluation data, the trained model, and output, then run the inference script.
+
+```
+SM_CHANNEL_EVAL=/path/to/images SM_CHANNEL_MODEL=/path/to/model SM_OUTPUT_DATA_DIR=/path/to/output python inference.py
+```
+
+or 
+
+```
+python inference.py --data_dir /path/to/images --model_dir /path/to/model --output_dir /path/to/model
+```
\ No newline at end of file
diff --git a/new/cv_serving/model/__init__.py b/new/cv_serving/model/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/new/cv_serving/model/best.pth b/new/cv_serving/model/best.pth
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index 0000000..a9b9dbe
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diff --git a/new/cv_serving/model/dataset.py b/new/cv_serving/model/dataset.py
new file mode 100644
index 0000000..0919c49
--- /dev/null
+++ b/new/cv_serving/model/dataset.py
@@ -0,0 +1,417 @@
+import os
+import random
+from collections import defaultdict
+from enum import Enum
+from typing import Tuple, List
+
+import numpy as np
+import torch
+from PIL import Image
+from torch.utils.data import Dataset, Subset, random_split
+from torchvision.transforms import (
+    Resize,
+    ToTensor,
+    Normalize,
+    Compose,
+    CenterCrop,
+    ColorJitter,
+)
+
+# 지원되는 이미지 확장자 리스트
+IMG_EXTENSIONS = [
+    ".jpg",
+    ".JPG",
+    ".jpeg",
+    ".JPEG",
+    ".png",
+    ".PNG",
+    ".ppm",
+    ".PPM",
+    ".bmp",
+    ".BMP",
+]
+
+
+def is_image_file(filename):
+    """파일 이름이 이미지 확장자를 가지는지 확인하는 함수
+
+    Args:
+        filename (str): 확인하고자 하는 파일 이름
+
+    Returns:
+        bool: 파일 이름이 이미지 확장자를 가지면 True, 그렇지 않으면 False.
+    """
+    return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
+
+
+class BaseAugmentation:
+    """
+    기본적인 Augmentation을 담당하는 클래스
+
+    Attributes:
+        transform (Compose): 이미지를 변환을 위한 torchvision.transforms.Compose 객체
+    """
+
+    def __init__(self, resize, mean, std, **args):
+        """
+        Args:
+            resize (tuple): 이미지의 리사이즈 대상 크지
+            mean (tuple): Normalize 변환을 위한 평균 값
+            std (tuple): Normalize 변환을 위한 표준 값
+        """
+        self.transform = Compose(
+            [
+                Resize(resize, Image.BILINEAR),
+                ToTensor(),
+                Normalize(mean=mean, std=std),
+            ]
+        )
+
+    def __call__(self, image):
+        """
+        이미지에 저장된 transform 적용
+
+        Args:
+            Image (PIL.Image): Augumentation을 적용할 이미지
+
+        Returns:
+            Tensor: Argumentation이 적용된 이미지
+        """
+        return self.transform(image)
+
+
+class AddGaussianNoise(object):
+    """이미지에 Gaussian Noise를 추가하는 클래스"""
+
+    def __init__(self, mean=0.0, std=1.0):
+        self.std = std
+        self.mean = mean
+
+    def __call__(self, tensor):
+        return tensor + torch.randn(tensor.size()) * self.std + self.mean
+
+    def __repr__(self):
+        return self.__class__.__name__ + "(mean={0}, std={1})".format(
+            self.mean, self.std
+        )
+
+
+class CustomAugmentation:
+    """커스텀 Augmentation을 담당하는 클래스"""
+
+    def __init__(self, resize, mean, std, **args):
+        self.transform = Compose(
+            [
+                CenterCrop((320, 256)),
+                Resize(resize, Image.BILINEAR),
+                ColorJitter(0.1, 0.1, 0.1, 0.1),
+                ToTensor(),
+                Normalize(mean=mean, std=std),
+                AddGaussianNoise(),
+            ]
+        )
+
+    def __call__(self, image):
+        return self.transform(image)
+
+
+class MaskLabels(int, Enum):
+    """마스크 라벨을 나타내는 Enum 클래스"""
+
+    MASK = 0
+    INCORRECT = 1
+    NORMAL = 2
+
+
+class GenderLabels(int, Enum):
+    """성별 라벨을 나타내는 Enum 클래스"""
+
+    MALE = 0
+    FEMALE = 1
+
+    @classmethod
+    def from_str(cls, value: str) -> int:
+        """문자열로부터 해당하는 성별 라벨을 찾아 반환하는 클래스 메서드"""
+        value = value.lower()
+        if value == "male":
+            return cls.MALE
+        elif value == "female":
+            return cls.FEMALE
+        else:
+            raise ValueError(
+                f"Gender value should be either 'male' or 'female', {value}"
+            )
+
+
+class AgeLabels(int, Enum):
+    """나이 라벨을 나타내는 Enum 클래스"""
+
+    YOUNG = 0
+    MIDDLE = 1
+    OLD = 2
+
+    @classmethod
+    def from_number(cls, value: str) -> int:
+        """숫자로부터 해당하는 나이 라벨을 찾아 반환하는 클래스 메서드"""
+        try:
+            value = int(value)
+        except Exception:
+            raise ValueError(f"Age value should be numeric, {value}")
+
+        if value < 30:
+            return cls.YOUNG
+        elif value < 60:
+            return cls.MIDDLE
+        else:
+            return cls.OLD
+
+
+class MaskBaseDataset(Dataset):
+    """마스크 데이터셋의 기본 클래스"""
+
+    num_classes = 3 * 2 * 3
+
+    _file_names = {
+        "mask1": MaskLabels.MASK,
+        "mask2": MaskLabels.MASK,
+        "mask3": MaskLabels.MASK,
+        "mask4": MaskLabels.MASK,
+        "mask5": MaskLabels.MASK,
+        "incorrect_mask": MaskLabels.INCORRECT,
+        "normal": MaskLabels.NORMAL,
+    }
+
+    image_paths = []
+    mask_labels = []
+    gender_labels = []
+    age_labels = []
+
+    def __init__(
+        self,
+        data_dir,
+        mean=(0.548, 0.504, 0.479),
+        std=(0.237, 0.247, 0.246),
+        val_ratio=0.2,
+    ):
+        self.data_dir = data_dir
+        self.mean = mean
+        self.std = std
+        self.val_ratio = val_ratio
+
+        self.transform = None
+        self.setup()  # 데이터셋을 설정
+        self.calc_statistics()  # 통계시 계산 (평균 및 표준 편차)
+
+    def setup(self):
+        """데이터 디렉토리로부터 이미지 경로와 라벨을 설정하는 메서드"""
+        profiles = os.listdir(self.data_dir)
+        for profile in profiles:
+            if profile.startswith("."):  # "." 로 시작하는 파일은 무시합니다
+                continue
+
+            img_folder = os.path.join(self.data_dir, profile)
+            for file_name in os.listdir(img_folder):
+                _file_name, ext = os.path.splitext(file_name)
+                if (
+                    _file_name not in self._file_names
+                ):  # "." 로 시작하는 파일 및 invalid 한 파일들은 무시합니다
+                    continue
+
+                img_path = os.path.join(
+                    self.data_dir, profile, file_name
+                )  # (resized_data, 000004_male_Asian_54, mask1.jpg)
+                mask_label = self._file_names[_file_name]
+
+                id, gender, race, age = profile.split("_")
+                gender_label = GenderLabels.from_str(gender)
+                age_label = AgeLabels.from_number(age)
+
+                self.image_paths.append(img_path)
+                self.mask_labels.append(mask_label)
+                self.gender_labels.append(gender_label)
+                self.age_labels.append(age_label)
+
+    def calc_statistics(self):
+        """데이터셋의 통계치를 계산하는 메서드"""
+        has_statistics = self.mean is not None and self.std is not None
+        if not has_statistics:
+            print(
+                "[Warning] Calculating statistics... It can take a long time depending on your CPU machine"
+            )
+            sums = []
+            squared = []
+            for image_path in self.image_paths[:3000]:
+                image = np.array(Image.open(image_path)).astype(np.int32)
+                sums.append(image.mean(axis=(0, 1)))
+                squared.append((image**2).mean(axis=(0, 1)))
+
+            self.mean = np.mean(sums, axis=0) / 255
+            self.std = (np.mean(squared, axis=0) - self.mean**2) ** 0.5 / 255
+
+    def set_transform(self, transform):
+        """변환(transform)을 설정하는 메서드"""
+        self.transform = transform
+
+    def __getitem__(self, index):
+        """인덱스에 해당하는 데이터를 가져오는 메서드"""
+        assert self.transform is not None, ".set_tranform 메소드를 이용하여 transform 을 주입해주세요"
+
+        image = self.read_image(index)
+        mask_label = self.get_mask_label(index)
+        gender_label = self.get_gender_label(index)
+        age_label = self.get_age_label(index)
+        multi_class_label = self.encode_multi_class(mask_label, gender_label, age_label)
+
+        image_transform = self.transform(image)
+        return image_transform, multi_class_label
+
+    def __len__(self):
+        """데이터셋의 길이를 반환하는 메서드"""
+        return len(self.image_paths)
+
+    def get_mask_label(self, index) -> MaskLabels:
+        """인덱스에 해당하는 마스크 라벨을 반환하는 메서드"""
+        return self.mask_labels[index]
+
+    def get_gender_label(self, index) -> GenderLabels:
+        """인덱스에 해당하는 성별 라벨을 반환하는 메서드"""
+        return self.gender_labels[index]
+
+    def get_age_label(self, index) -> AgeLabels:
+        """인덱스에 해당하는 나이 라벨을 반환하는 메서드"""
+        return self.age_labels[index]
+
+    def read_image(self, index):
+        """인덱스에 해당하는 이미지를 읽는 메서드"""
+        image_path = self.image_paths[index]
+        return Image.open(image_path)
+
+    @staticmethod
+    def encode_multi_class(mask_label, gender_label, age_label) -> int:
+        """다중 라벨을 하나의 클래스로 인코딩하는 메서드"""
+        return mask_label * 6 + gender_label * 3 + age_label
+
+    @staticmethod
+    def decode_multi_class(
+        multi_class_label,
+    ) -> Tuple[MaskLabels, GenderLabels, AgeLabels]:
+        """인코딩된 다중 라벨을 각각의 라벨로 디코딩하는 메서드"""
+        mask_label = (multi_class_label // 6) % 3
+        gender_label = (multi_class_label // 3) % 2
+        age_label = multi_class_label % 3
+        return mask_label, gender_label, age_label
+
+    @staticmethod
+    def denormalize_image(image, mean, std):
+        """정규화된 이미지를 원래대로 되돌리는 메서드"""
+        img_cp = image.copy()
+        img_cp *= std
+        img_cp += mean
+        img_cp *= 255.0
+        img_cp = np.clip(img_cp, 0, 255).astype(np.uint8)
+        return img_cp
+
+    def split_dataset(self) -> Tuple[Subset, Subset]:
+        """데이터셋을 학습과 검증용으로 나누는 메서드
+        데이터셋을 train 과 val 로 나눕니다,
+        pytorch 내부의 torch.utils.data.random_split 함수를 사용하여 torch.utils.data.Subset 클래스 둘로 나눕니다.
+        """
+        n_val = int(len(self) * self.val_ratio)
+        n_train = len(self) - n_val
+        train_set, val_set = random_split(self, [n_train, n_val])
+        return train_set, val_set
+
+
+class MaskSplitByProfileDataset(MaskBaseDataset):
+    """
+    train / val 나누는 기준을 이미지에 대해서 random 이 아닌 사람(profile)을 기준으로 나눕니다.
+    구현은 val_ratio 에 맞게 train / val 나누는 것을 이미지 전체가 아닌 사람(profile)에 대해서 진행하여 indexing 을 합니다.
+    이후 `split_dataset` 에서 index 에 맞게 Subset 으로 dataset 을 분기합니다.
+    """
+
+    def __init__(
+        self,
+        data_dir,
+        mean=(0.548, 0.504, 0.479),
+        std=(0.237, 0.247, 0.246),
+        val_ratio=0.2,
+    ):
+        self.indices = defaultdict(list)
+        super().__init__(data_dir, mean, std, val_ratio)
+
+    @staticmethod
+    def _split_profile(profiles, val_ratio):
+        """프로필을 학습과 검증용으로 나누는 메서드"""
+        length = len(profiles)
+        n_val = int(length * val_ratio)
+
+        val_indices = set(random.sample(range(length), k=n_val))
+        train_indices = set(range(length)) - val_indices
+        return {"train": train_indices, "val": val_indices}
+
+    def setup(self):
+        """데이터셋 설정을 하는 메서드. 프로필 기준으로 나눈다."""
+        profiles = os.listdir(self.data_dir)
+        profiles = [profile for profile in profiles if not profile.startswith(".")]
+        split_profiles = self._split_profile(profiles, self.val_ratio)
+
+        cnt = 0
+        for phase, indices in split_profiles.items():
+            for _idx in indices:
+                profile = profiles[_idx]
+                img_folder = os.path.join(self.data_dir, profile)
+                for file_name in os.listdir(img_folder):
+                    _file_name, ext = os.path.splitext(file_name)
+                    if (
+                        _file_name not in self._file_names
+                    ):  # "." 로 시작하는 파일 및 invalid 한 파일들은 무시합니다
+                        continue
+
+                    img_path = os.path.join(
+                        self.data_dir, profile, file_name
+                    )  # (resized_data, 000004_male_Asian_54, mask1.jpg)
+                    mask_label = self._file_names[_file_name]
+
+                    id, gender, race, age = profile.split("_")
+                    gender_label = GenderLabels.from_str(gender)
+                    age_label = AgeLabels.from_number(age)
+
+                    self.image_paths.append(img_path)
+                    self.mask_labels.append(mask_label)
+                    self.gender_labels.append(gender_label)
+                    self.age_labels.append(age_label)
+
+                    self.indices[phase].append(cnt)
+                    cnt += 1
+
+    def split_dataset(self) -> List[Subset]:
+        """프로필 기준으로 나눈 데이터셋을 Subset 리스트로 반환하는 메서드"""
+        return [Subset(self, indices) for phase, indices in self.indices.items()]
+
+
+class TestDataset(Dataset):
+    """테스트 데이터셋 클래스"""
+
+    def __init__(
+        self, img_paths, resize, mean=(0.548, 0.504, 0.479), std=(0.237, 0.247, 0.246)
+    ):
+        self.img_paths = img_paths
+        self.transform = Compose(
+            [
+                Resize(resize, Image.BILINEAR),
+                ToTensor(),
+                Normalize(mean=mean, std=std),
+            ]
+        )
+
+    def __getitem__(self, index):
+        """인덱스에 해당하는 데이터를 가져오는 메서드"""
+        image = Image.open(self.img_paths[index])
+
+        if self.transform:
+            image = self.transform(image)
+        return image
+
+    def __len__(self):
+        """데이터셋의 길이를 반환하는 메서드"""
+        return len(self.img_paths)
diff --git a/new/cv_serving/model/inference.py b/new/cv_serving/model/inference.py
new file mode 100644
index 0000000..48b7630
--- /dev/null
+++ b/new/cv_serving/model/inference.py
@@ -0,0 +1,144 @@
+import argparse
+import multiprocessing
+import os
+from importlib import import_module
+
+import pandas as pd
+import torch
+from torch.utils.data import DataLoader
+
+from dataset import TestDataset, MaskBaseDataset
+
+
+def load_model(saved_model, num_classes, device):
+    """
+    저장된 모델의 가중치를 로드하는 함수입니다.
+
+    Args:
+        saved_model (str): 모델 가중치가 저장된 디렉토리 경로
+        num_classes (int): 모델의 클래수 수
+        device (torch.device): 모델이 로드될 장치 (CPU 또는 CUDA)
+
+    Returns:
+        model (nn.Module): 가중치가 로드된 모델
+    """
+    model_cls = getattr(import_module("model"), args.model)
+    model = model_cls(num_classes=num_classes)
+
+    # tarpath = os.path.join(saved_model, 'best.tar.gz')
+    # tar = tarfile.open(tarpath, 'r:gz')
+    # tar.extractall(path=saved_model)
+
+    # 모델 가중치를 로드한다.
+    model_path = os.path.join(saved_model, "best.pth")
+    model.load_state_dict(torch.load(model_path, map_location=device))
+
+    return model
+
+
+@torch.no_grad()
+def inference(data_dir, model_dir, output_dir, args):
+    """
+    모델 추론을 수행하는 함수
+
+    Args:
+        data_dir (str): 테스트 데이터가 있는 디렉토리 경로
+        model_dir (str): 모델 가중치가 저장된 디렉토리 경로
+        output_dir (str): 결과 CSV를 저장할 디렉토리 경로
+        args (argparse.Namespace): 커맨드 라인 인자
+
+    Returns:
+        None
+    """
+
+    # CUDA를 사용할 수 있는지 확인
+    use_cuda = torch.cuda.is_available()
+    device = torch.device("cuda" if use_cuda else "cpu")
+
+    # 클래스의 개수를 설정한다. (마스크, 성별, 나이의 조합으로 18)
+    num_classes = MaskBaseDataset.num_classes  # 18
+    model = load_model(model_dir, num_classes, device).to(device)
+    model.eval()
+
+    # 이미지 파일 경로와 정보 파일을 읽어온다.
+    img_root = os.path.join(data_dir, "images")
+    info_path = os.path.join(data_dir, "info.csv")
+    info = pd.read_csv(info_path)
+
+    # 이미지 경로를 리스트로 생성한다.
+    img_paths = [os.path.join(img_root, img_id) for img_id in info.ImageID]
+    dataset = TestDataset(img_paths, args.resize)
+    loader = torch.utils.data.DataLoader(
+        dataset,
+        batch_size=args.batch_size,
+        num_workers=multiprocessing.cpu_count() // 2,
+        shuffle=False,
+        pin_memory=use_cuda,
+        drop_last=False,
+    )
+
+    print("Calculating inference results..")
+    preds = []
+    with torch.no_grad():
+        for idx, images in enumerate(loader):
+            images = images.to(device)
+            pred = model(images)
+            pred = pred.argmax(dim=-1)
+            preds.extend(pred.cpu().numpy())
+
+    # 예측 결과를 데이터프레임에 저장하고 csv 파일로 출력한다.
+    info["ans"] = preds
+    save_path = os.path.join(output_dir, f"output.csv")
+    info.to_csv(save_path, index=False)
+    print(f"Inference Done! Inference result saved at {save_path}")
+
+
+if __name__ == "__main__":
+    # 커맨드 라인 인자를 파싱한다.
+    parser = argparse.ArgumentParser()
+
+    # 데이터와 모델 체크포인트 디렉터리 관련 인자
+    parser.add_argument(
+        "--batch_size",
+        type=int,
+        default=1000,
+        help="input batch size for validing (default: 1000)",
+    )
+    parser.add_argument(
+        "--resize",
+        nargs=2,
+        type=int,
+        default=(96, 128),
+        help="resize size for image when you trained (default: (96, 128))",
+    )
+    parser.add_argument(
+        "--model", type=str, default="BaseModel", help="model type (default: BaseModel)"
+    )
+
+    # 컨테이너 환경 변수
+    parser.add_argument(
+        "--data_dir",
+        type=str,
+        default=os.environ.get("SM_CHANNEL_EVAL", "/opt/ml/input/data/eval"),
+    )
+    parser.add_argument(
+        "--model_dir",
+        type=str,
+        default=os.environ.get("SM_CHANNEL_MODEL", "./model/exp"),
+    )
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default=os.environ.get("SM_OUTPUT_DATA_DIR", "./output"),
+    )
+
+    args = parser.parse_args()
+
+    data_dir = args.data_dir
+    model_dir = args.model_dir
+    output_dir = args.output_dir
+
+    os.makedirs(output_dir, exist_ok=True)
+
+    # 모델 추론을 수행한다.
+    inference(data_dir, model_dir, output_dir, args)
diff --git a/new/cv_serving/model/loss.py b/new/cv_serving/model/loss.py
new file mode 100644
index 0000000..ed5450d
--- /dev/null
+++ b/new/cv_serving/model/loss.py
@@ -0,0 +1,125 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+# Focal Loss 구현
+# 이는 불균형한 데이터셋에서 사용되며, 잘못 분류된 샘플에 더 많은 중요도를 부여한다.
+# https://discuss.pytorch.org/t/is-this-a-correct-implementation-for-focal-loss-in-pytorch/43327/8
+class FocalLoss(nn.Module):
+    def __init__(self, weight=None, gamma=2.0, reduction="mean"):
+        nn.Module.__init__(self)
+        self.weight = weight
+        self.gamma = gamma
+        self.reduction = reduction
+
+    def forward(self, input_tensor, target_tensor):
+        log_prob = F.log_softmax(input_tensor, dim=-1)
+        prob = torch.exp(log_prob)
+        return F.nll_loss(
+            ((1 - prob) ** self.gamma) * log_prob,
+            target_tensor,
+            weight=self.weight,
+            reduction=self.reduction,
+        )
+
+
+# Label Smoothing Loss 구현
+# 모델이 너무 자신만만하게 예측하는 것을 방지하기 위해 사용된다.
+class LabelSmoothingLoss(nn.Module):
+    def __init__(self, classes=3, smoothing=0.0, dim=-1):
+        super(LabelSmoothingLoss, self).__init__()
+        self.confidence = 1.0 - smoothing
+        self.smoothing = smoothing
+        self.cls = classes
+        self.dim = dim
+
+    def forward(self, pred, target):
+        pred = pred.log_softmax(dim=self.dim)
+        with torch.no_grad():
+            true_dist = torch.zeros_like(pred)
+            true_dist.fill_(self.smoothing / (self.cls - 1))
+            true_dist.scatter_(1, target.data.unsqueeze(1), self.confidence)
+        return torch.mean(torch.sum(-true_dist * pred, dim=self.dim))
+
+
+# F1 Score 손실 함수 구현
+# F1 Score는 precision과 recall의 조화 평균이며, 이를 손실로 사용한다.
+# https://gist.github.com/SuperShinyEyes/dcc68a08ff8b615442e3bc6a9b55a354
+class F1Loss(nn.Module):
+    def __init__(self, classes=3, epsilon=1e-7):
+        super().__init__()
+        self.classes = classes
+        self.epsilon = epsilon
+
+    def forward(self, y_pred, y_true):
+        assert y_pred.ndim == 2
+        assert y_true.ndim == 1
+        y_true = F.one_hot(y_true, self.classes).to(torch.float32)
+        y_pred = F.softmax(y_pred, dim=1)
+
+        tp = (y_true * y_pred).sum(dim=0).to(torch.float32)
+        tn = ((1 - y_true) * (1 - y_pred)).sum(dim=0).to(torch.float32)
+        fp = ((1 - y_true) * y_pred).sum(dim=0).to(torch.float32)
+        fn = (y_true * (1 - y_pred)).sum(dim=0).to(torch.float32)
+
+        precision = tp / (tp + fp + self.epsilon)
+        recall = tp / (tp + fn + self.epsilon)
+
+        f1 = 2 * (precision * recall) / (precision + recall + self.epsilon)
+        f1 = f1.clamp(min=self.epsilon, max=1 - self.epsilon)
+        return 1 - f1.mean()
+
+
+# 사용 가능한 손실 함수의 진입점
+_criterion_entrypoints = {
+    "cross_entropy": nn.CrossEntropyLoss,
+    "focal": FocalLoss,
+    "label_smoothing": LabelSmoothingLoss,
+    "f1": F1Loss,
+}
+
+
+def criterion_entrypoint(criterion_name):
+    """
+    주어진 손실 함수 이름에 해당하는 손실 함수
+
+    Args:
+        criterion_name (str): 반환할 손실 함수 이름
+
+    Returns:
+        callable: 주어진 이름에 해당하는 손실 함수
+    """
+    return _criterion_entrypoints[criterion_name]
+
+
+def is_criterion(criterion_name):
+    """
+    주어진 손실 함수 이름이 지원되는지 확인한다.
+
+    Args:
+        criterion_name (str): 확인할 손실 함수 이름
+
+    Returns:
+        bool: 지원되면 True, 그렇지 않으며 False
+    """
+    return criterion_name in _criterion_entrypoints
+
+
+def create_criterion(criterion_name, **kwargs):
+    """
+    지정된 인수를 사용하여 손실 함수 객체를 생성한다.
+
+    Args:
+        criterion_name (str): 생성할 손실 함수 이름
+        **kargs: 손실 함수 생성자에 전달된 키워드 인자
+
+    Returns:
+        nn.Module: 생성된 손실 함수 객체
+    """
+    if is_criterion(criterion_name):
+        create_fn = criterion_entrypoint(criterion_name)
+        criterion = create_fn(**kwargs)
+    else:
+        raise RuntimeError("Unknown loss (%s)" % criterion_name)
+    return criterion
diff --git a/new/cv_serving/model/model.py b/new/cv_serving/model/model.py
new file mode 100644
index 0000000..c20f24b
--- /dev/null
+++ b/new/cv_serving/model/model.py
@@ -0,0 +1,69 @@
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class BaseModel(nn.Module):
+    """
+    기본적인 컨볼루션 신경망 모델
+    """
+
+    def __init__(self, num_classes):
+        """
+        모델의 레이어 초기화
+
+        Args:
+            num_classes (int): 출력 레이어의 뉴런 수
+        """
+        super().__init__()
+
+        self.conv1 = nn.Conv2d(3, 32, kernel_size=7, stride=1)
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1)
+        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=1)
+        self.dropout1 = nn.Dropout(0.25)
+        self.dropout2 = nn.Dropout(0.25)
+        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+        self.fc = nn.Linear(128, num_classes)
+
+    def forward(self, x):
+        """
+        Args:
+            x (torch.Tensor): 입력 이미지 텐서
+
+        Returns:
+            x (torch.Tensor): num_classes 크기의 출력 텐서
+        """
+        x = self.conv1(x)
+        x = F.relu(x)
+
+        x = self.conv2(x)
+        x = F.relu(x)
+        x = F.max_pool2d(x, 2)
+        x = self.dropout1(x)
+
+        x = self.conv3(x)
+        x = F.relu(x)
+        x = F.max_pool2d(x, 2)
+        x = self.dropout2(x)
+
+        x = self.avgpool(x)
+        x = x.view(-1, 128)
+        return self.fc(x)
+
+
+# Custom Model Template
+class MyModel(nn.Module):
+    def __init__(self, num_classes):
+        super().__init__()
+
+        """
+        1. 위와 같이 생성자의 parameter 에 num_claases 를 포함해주세요.
+        2. 나만의 모델 아키텍쳐를 디자인 해봅니다.
+        3. 모델의 output_dimension 은 num_classes 로 설정해주세요.
+        """
+
+    def forward(self, x):
+        """
+        1. 위에서 정의한 모델 아키텍쳐를 forward propagation 을 진행해주세요
+        2. 결과로 나온 output 을 return 해주세요
+        """
+        return x
diff --git a/new/cv_serving/model/requirements.txt b/new/cv_serving/model/requirements.txt
new file mode 100644
index 0000000..5b3c9b5
--- /dev/null
+++ b/new/cv_serving/model/requirements.txt
@@ -0,0 +1,10 @@
+black==23.9.1
+matplotlib==3.7.1
+numpy==1.23.5
+pandas==1.5.3
+Pillow==9.4.0
+python-dotenv==1.0.0
+scikit-learn==1.2.2
+tensorboard==2.13.0
+torch==2.1.1
+torchvision==0.16.1
\ No newline at end of file
diff --git a/new/cv_serving/model/sample_submission.csv b/new/cv_serving/model/sample_submission.csv
new file mode 100644
index 0000000..8b58b73
--- /dev/null
+++ b/new/cv_serving/model/sample_submission.csv
@@ -0,0 +1,12601 @@
+ImageID,ans
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diff --git a/new/cv_serving/model/sample_submission.ipynb b/new/cv_serving/model/sample_submission.ipynb
new file mode 100644
index 0000000..913d2f2
--- /dev/null
+++ b/new/cv_serving/model/sample_submission.ipynb
@@ -0,0 +1,203 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "about-heavy",
+   "metadata": {},
+   "source": [
+    "## 0. Libarary 불러오기 및 경로설정"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "cubic-scoop",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "from PIL import Image\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "\n",
+    "from torchvision import transforms\n",
+    "from torchvision.transforms import Resize, ToTensor, Normalize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "built-elevation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 테스트 데이터셋 폴더 경로를 지정해주세요.\n",
+    "test_dir = '/opt/ml/input/data/eval'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "quiet-organizer",
+   "metadata": {},
+   "source": [
+    "## 1. Model 정의"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "acknowledged-easter",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class MyModel(nn.Module):\n",
+    "    def __init__(self, num_classes: int = 1000):\n",
+    "        super(MyModel, self).__init__()\n",
+    "        self.features = nn.Sequential(\n",
+    "            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),\n",
+    "            nn.BatchNorm2d(64),\n",
+    "            nn.ReLU(inplace=True),\n",
+    "        )\n",
+    "        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))\n",
+    "        self.classifier = nn.Sequential(\n",
+    "            nn.Dropout(),\n",
+    "            nn.Linear(64, 32),\n",
+    "            nn.ReLU(inplace=True),\n",
+    "            nn.Linear(32, num_classes),\n",
+    "        )\n",
+    "\n",
+    "    def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
+    "        x = self.features(x)\n",
+    "        x = self.avgpool(x)\n",
+    "        x = torch.flatten(x, 1)\n",
+    "        x = self.classifier(x)\n",
+    "        return x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "domestic-channels",
+   "metadata": {},
+   "source": [
+    "## 2. Test Dataset 정의"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "extensive-north",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class TestDataset(Dataset):\n",
+    "    def __init__(self, img_paths, transform):\n",
+    "        self.img_paths = img_paths\n",
+    "        self.transform = transform\n",
+    "\n",
+    "    def __getitem__(self, index):\n",
+    "        image = Image.open(self.img_paths[index])\n",
+    "\n",
+    "        if self.transform:\n",
+    "            image = self.transform(image)\n",
+    "        return image\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self.img_paths)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "continued-feelings",
+   "metadata": {},
+   "source": [
+    "## 3. Inference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "coral-shade",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "test inference is done!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# meta 데이터와 이미지 경로를 불러옵니다.\n",
+    "submission = pd.read_csv(os.path.join(test_dir, 'info.csv'))\n",
+    "image_dir = os.path.join(test_dir, 'images')\n",
+    "\n",
+    "# Test Dataset 클래스 객체를 생성하고 DataLoader를 만듭니다.\n",
+    "image_paths = [os.path.join(image_dir, img_id) for img_id in submission.ImageID]\n",
+    "transform = transforms.Compose([\n",
+    "    Resize((512, 384), Image.BILINEAR),\n",
+    "    ToTensor(),\n",
+    "    Normalize(mean=(0.5, 0.5, 0.5), std=(0.2, 0.2, 0.2)),\n",
+    "])\n",
+    "dataset = TestDataset(image_paths, transform)\n",
+    "\n",
+    "loader = DataLoader(\n",
+    "    dataset,\n",
+    "    shuffle=False\n",
+    ")\n",
+    "\n",
+    "# 모델을 정의합니다. (학습한 모델이 있다면 torch.load로 모델을 불러주세요!)\n",
+    "device = torch.device('cuda')\n",
+    "model = MyModel(num_classes=18).to(device)\n",
+    "model.eval()\n",
+    "\n",
+    "# 모델이 테스트 데이터셋을 예측하고 결과를 저장합니다.\n",
+    "all_predictions = []\n",
+    "for images in loader:\n",
+    "    with torch.no_grad():\n",
+    "        images = images.to(device)\n",
+    "        pred = model(images)\n",
+    "        pred = pred.argmax(dim=-1)\n",
+    "        all_predictions.extend(pred.cpu().numpy())\n",
+    "submission['ans'] = all_predictions\n",
+    "\n",
+    "# 제출할 파일을 저장합니다.\n",
+    "submission.to_csv(os.path.join(test_dir, 'submission.csv'), index=False)\n",
+    "print('test inference is done!')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "verbal-sample",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/new/cv_serving/model/train.py b/new/cv_serving/model/train.py
new file mode 100644
index 0000000..b3507f4
--- /dev/null
+++ b/new/cv_serving/model/train.py
@@ -0,0 +1,354 @@
+import argparse
+import glob
+import json
+import multiprocessing
+import os
+import random
+import re
+from importlib import import_module
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+from torch.optim.lr_scheduler import StepLR
+from torch.utils.data import DataLoader
+from torch.utils.tensorboard import SummaryWriter
+
+from dataset import MaskBaseDataset
+from loss import create_criterion
+
+
+def seed_everything(seed):
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)  # if use multi-GPU
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+    np.random.seed(seed)
+    random.seed(seed)
+
+
+def get_lr(optimizer):
+    for param_group in optimizer.param_groups:
+        return param_group["lr"]
+
+
+def grid_image(np_images, gts, preds, n=16, shuffle=False):
+    batch_size = np_images.shape[0]
+    assert n <= batch_size
+
+    choices = random.choices(range(batch_size), k=n) if shuffle else list(range(n))
+    figure = plt.figure(
+        figsize=(12, 18 + 2)
+    )  # cautions: hardcoded, 이미지 크기에 따라 figsize 를 조정해야 할 수 있습니다. T.T
+    plt.subplots_adjust(
+        top=0.8
+    )  # cautions: hardcoded, 이미지 크기에 따라 top 를 조정해야 할 수 있습니다. T.T
+    n_grid = int(np.ceil(n**0.5))
+    tasks = ["mask", "gender", "age"]
+    for idx, choice in enumerate(choices):
+        gt = gts[choice].item()
+        pred = preds[choice].item()
+        image = np_images[choice]
+        gt_decoded_labels = MaskBaseDataset.decode_multi_class(gt)
+        pred_decoded_labels = MaskBaseDataset.decode_multi_class(pred)
+        title = "\n".join(
+            [
+                f"{task} - gt: {gt_label}, pred: {pred_label}"
+                for gt_label, pred_label, task in zip(
+                    gt_decoded_labels, pred_decoded_labels, tasks
+                )
+            ]
+        )
+
+        plt.subplot(n_grid, n_grid, idx + 1, title=title)
+        plt.xticks([])
+        plt.yticks([])
+        plt.grid(False)
+        plt.imshow(image, cmap=plt.cm.binary)
+
+    return figure
+
+
+def increment_path(path, exist_ok=False):
+    """Automatically increment path, i.e. runs/exp --> runs/exp0, runs/exp1 etc.
+
+    Args:
+        path (str or pathlib.Path): f"{model_dir}/{args.name}".
+        exist_ok (bool): whether increment path (increment if False).
+    """
+    path = Path(path)
+    if (path.exists() and exist_ok) or (not path.exists()):
+        return str(path)
+    else:
+        dirs = glob.glob(f"{path}*")
+        matches = [re.search(rf"%s(\d+)" % path.stem, d) for d in dirs]
+        i = [int(m.groups()[0]) for m in matches if m]
+        n = max(i) + 1 if i else 2
+        return f"{path}{n}"
+
+
+def train(data_dir, model_dir, args):
+    seed_everything(args.seed)
+
+    save_dir = increment_path(os.path.join(model_dir, args.name))
+
+    # -- settings
+    use_cuda = torch.cuda.is_available()
+    device = torch.device("cuda" if use_cuda else "cpu")
+
+    # -- dataset
+    dataset_module = getattr(
+        import_module("dataset"), args.dataset
+    )  # default: MaskBaseDataset
+    dataset = dataset_module(
+        data_dir=data_dir,
+    )
+    num_classes = dataset.num_classes  # 18
+
+    # -- augmentation
+    transform_module = getattr(
+        import_module("dataset"), args.augmentation
+    )  # default: BaseAugmentation
+    transform = transform_module(
+        resize=args.resize,
+        mean=dataset.mean,
+        std=dataset.std,
+    )
+    dataset.set_transform(transform)
+
+    # -- data_loader
+    train_set, val_set = dataset.split_dataset()
+
+    train_loader = DataLoader(
+        train_set,
+        batch_size=args.batch_size,
+        num_workers=multiprocessing.cpu_count() // 2,
+        shuffle=True,
+        pin_memory=use_cuda,
+        drop_last=True,
+    )
+
+    val_loader = DataLoader(
+        val_set,
+        batch_size=args.valid_batch_size,
+        num_workers=multiprocessing.cpu_count() // 2,
+        shuffle=False,
+        pin_memory=use_cuda,
+        drop_last=True,
+    )
+
+    # -- model
+    model_module = getattr(import_module("model"), args.model)  # default: BaseModel
+    model = model_module(num_classes=num_classes).to(device)
+    model = torch.nn.DataParallel(model)
+
+    # -- loss & metric
+    criterion = create_criterion(args.criterion)  # default: cross_entropy
+    opt_module = getattr(import_module("torch.optim"), args.optimizer)  # default: SGD
+    optimizer = opt_module(
+        filter(lambda p: p.requires_grad, model.parameters()),
+        lr=args.lr,
+        weight_decay=5e-4,
+    )
+    scheduler = StepLR(optimizer, args.lr_decay_step, gamma=0.5)
+
+    # -- logging
+    logger = SummaryWriter(log_dir=save_dir)
+    with open(os.path.join(save_dir, "config.json"), "w", encoding="utf-8") as f:
+        json.dump(vars(args), f, ensure_ascii=False, indent=4)
+
+    best_val_acc = 0
+    best_val_loss = np.inf
+    for epoch in range(args.epochs):
+        # train loop
+        model.train()
+        loss_value = 0
+        matches = 0
+        for idx, train_batch in enumerate(train_loader):
+            inputs, labels = train_batch
+            inputs = inputs.to(device)
+            labels = labels.to(device)
+
+            optimizer.zero_grad()
+
+            outs = model(inputs)
+            preds = torch.argmax(outs, dim=-1)
+            loss = criterion(outs, labels)
+
+            loss.backward()
+            optimizer.step()
+
+            loss_value += loss.item()
+            matches += (preds == labels).sum().item()
+            if (idx + 1) % args.log_interval == 0:
+                train_loss = loss_value / args.log_interval
+                train_acc = matches / args.batch_size / args.log_interval
+                current_lr = get_lr(optimizer)
+                print(
+                    f"Epoch[{epoch}/{args.epochs}]({idx + 1}/{len(train_loader)}) || "
+                    f"training loss {train_loss:4.4} || training accuracy {train_acc:4.2%} || lr {current_lr}"
+                )
+                logger.add_scalar(
+                    "Train/loss", train_loss, epoch * len(train_loader) + idx
+                )
+                logger.add_scalar(
+                    "Train/accuracy", train_acc, epoch * len(train_loader) + idx
+                )
+
+                loss_value = 0
+                matches = 0
+
+        scheduler.step()
+
+        # val loop
+        with torch.no_grad():
+            print("Calculating validation results...")
+            model.eval()
+            val_loss_items = []
+            val_acc_items = []
+            figure = None
+            for val_batch in val_loader:
+                inputs, labels = val_batch
+                inputs = inputs.to(device)
+                labels = labels.to(device)
+
+                outs = model(inputs)
+                preds = torch.argmax(outs, dim=-1)
+
+                loss_item = criterion(outs, labels).item()
+                acc_item = (labels == preds).sum().item()
+                val_loss_items.append(loss_item)
+                val_acc_items.append(acc_item)
+
+                if figure is None:
+                    inputs_np = (
+                        torch.clone(inputs).detach().cpu().permute(0, 2, 3, 1).numpy()
+                    )
+                    inputs_np = dataset_module.denormalize_image(
+                        inputs_np, dataset.mean, dataset.std
+                    )
+                    figure = grid_image(
+                        inputs_np,
+                        labels,
+                        preds,
+                        n=16,
+                        shuffle=args.dataset != "MaskSplitByProfileDataset",
+                    )
+
+            val_loss = np.sum(val_loss_items) / len(val_loader)
+            val_acc = np.sum(val_acc_items) / len(val_set)
+            best_val_loss = min(best_val_loss, val_loss)
+            if val_acc > best_val_acc:
+                print(
+                    f"New best model for val accuracy : {val_acc:4.2%}! saving the best model.."
+                )
+                torch.save(model.module.state_dict(), f"{save_dir}/best.pth")
+                best_val_acc = val_acc
+            torch.save(model.module.state_dict(), f"{save_dir}/last.pth")
+            print(
+                f"[Val] acc : {val_acc:4.2%}, loss: {val_loss:4.2} || "
+                f"best acc : {best_val_acc:4.2%}, best loss: {best_val_loss:4.2}"
+            )
+            logger.add_scalar("Val/loss", val_loss, epoch)
+            logger.add_scalar("Val/accuracy", val_acc, epoch)
+            logger.add_figure("results", figure, epoch)
+            print()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+
+    # Data and model checkpoints directories
+    parser.add_argument(
+        "--seed", type=int, default=42, help="random seed (default: 42)"
+    )
+    parser.add_argument(
+        "--epochs", type=int, default=1, help="number of epochs to train (default: 1)"
+    )
+    parser.add_argument(
+        "--dataset",
+        type=str,
+        default="MaskBaseDataset",
+        help="dataset augmentation type (default: MaskBaseDataset)",
+    )
+    parser.add_argument(
+        "--augmentation",
+        type=str,
+        default="BaseAugmentation",
+        help="data augmentation type (default: BaseAugmentation)",
+    )
+    parser.add_argument(
+        "--resize",
+        nargs=2,
+        type=int,
+        default=[128, 96],
+        help="resize size for image when training",
+    )
+    parser.add_argument(
+        "--batch_size",
+        type=int,
+        default=64,
+        help="input batch size for training (default: 64)",
+    )
+    parser.add_argument(
+        "--valid_batch_size",
+        type=int,
+        default=1000,
+        help="input batch size for validing (default: 1000)",
+    )
+    parser.add_argument(
+        "--model", type=str, default="BaseModel", help="model type (default: BaseModel)"
+    )
+    parser.add_argument(
+        "--optimizer", type=str, default="SGD", help="optimizer type (default: SGD)"
+    )
+    parser.add_argument(
+        "--lr", type=float, default=1e-3, help="learning rate (default: 1e-3)"
+    )
+    parser.add_argument(
+        "--val_ratio",
+        type=float,
+        default=0.2,
+        help="ratio for validaton (default: 0.2)",
+    )
+    parser.add_argument(
+        "--criterion",
+        type=str,
+        default="cross_entropy",
+        help="criterion type (default: cross_entropy)",
+    )
+    parser.add_argument(
+        "--lr_decay_step",
+        type=int,
+        default=20,
+        help="learning rate scheduler deacy step (default: 20)",
+    )
+    parser.add_argument(
+        "--log_interval",
+        type=int,
+        default=20,
+        help="how many batches to wait before logging training status",
+    )
+    parser.add_argument(
+        "--name", default="exp", help="model save at {SM_MODEL_DIR}/{name}"
+    )
+
+    # Container environment
+    parser.add_argument(
+        "--data_dir",
+        type=str,
+        default=os.environ.get("SM_CHANNEL_TRAIN", "/opt/ml/input/data/train/images"),
+    )
+    parser.add_argument(
+        "--model_dir", type=str, default=os.environ.get("SM_MODEL_DIR", "./model")
+    )
+
+    args = parser.parse_args()
+    print(args)
+
+    data_dir = args.data_dir
+    model_dir = args.model_dir
+
+    train(data_dir, model_dir, args)
diff --git a/new/cv_serving/poetry.lock b/new/cv_serving/poetry.lock
new file mode 100644
index 0000000..76d1c74
--- /dev/null
+++ b/new/cv_serving/poetry.lock
@@ -0,0 +1,1555 @@
+# This file is automatically @generated by Poetry and should not be changed by hand.
+
+[[package]]
+name = "annotated-types"
+version = "0.6.0"
+description = "Reusable constraint types to use with typing.Annotated"
+category = "main"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "annotated_types-0.6.0-py3-none-any.whl", hash = "sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43"},
+    {file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"},
+]
+
+[[package]]
+name = "anyio"
+version = "3.7.1"
+description = "High level compatibility layer for multiple asynchronous event loop implementations"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "anyio-3.7.1-py3-none-any.whl", hash = "sha256:91dee416e570e92c64041bd18b900d1d6fa78dff7048769ce5ac5ddad004fbb5"},
+    {file = "anyio-3.7.1.tar.gz", hash = "sha256:44a3c9aba0f5defa43261a8b3efb97891f2bd7d804e0e1f56419befa1adfc780"},
+]
+
+[package.dependencies]
+exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
+idna = ">=2.8"
+sniffio = ">=1.1"
+
+[package.extras]
+doc = ["Sphinx", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme (>=1.2.2)", "sphinxcontrib-jquery"]
+test = ["anyio[trio]", "coverage[toml] (>=4.5)", "hypothesis (>=4.0)", "mock (>=4)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
+trio = ["trio (<0.22)"]
+
+[[package]]
+name = "certifi"
+version = "2023.11.17"
+description = "Python package for providing Mozilla's CA Bundle."
+category = "main"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "certifi-2023.11.17-py3-none-any.whl", hash = "sha256:e036ab49d5b79556f99cfc2d9320b34cfbe5be05c5871b51de9329f0603b0474"},
+    {file = "certifi-2023.11.17.tar.gz", hash = "sha256:9b469f3a900bf28dc19b8cfbf8019bf47f7fdd1a65a1d4ffb98fc14166beb4d1"},
+]
+
+[[package]]
+name = "click"
+version = "8.1.7"
+description = "Composable command line interface toolkit"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "click-8.1.7-py3-none-any.whl", hash = "sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28"},
+    {file = "click-8.1.7.tar.gz", hash = "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de"},
+]
+
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+
+[[package]]
+name = "colorama"
+version = "0.4.6"
+description = "Cross-platform colored terminal text."
+category = "main"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
+files = [
+    {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"},
+    {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
+]
+
+[[package]]
+name = "dnspython"
+version = "2.4.2"
+description = "DNS toolkit"
+category = "main"
+optional = false
+python-versions = ">=3.8,<4.0"
+files = [
+    {file = "dnspython-2.4.2-py3-none-any.whl", hash = "sha256:57c6fbaaeaaf39c891292012060beb141791735dbb4004798328fc2c467402d8"},
+    {file = "dnspython-2.4.2.tar.gz", hash = "sha256:8dcfae8c7460a2f84b4072e26f1c9f4101ca20c071649cb7c34e8b6a93d58984"},
+]
+
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+optional = false
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+
+[metadata]
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diff --git a/new/cv_serving/pyproject.toml b/new/cv_serving/pyproject.toml
new file mode 100644
index 0000000..90a484a
--- /dev/null
+++ b/new/cv_serving/pyproject.toml
@@ -0,0 +1,24 @@
+[tool.poetry]
+name = "web-single"
+version = "0.1.0"
+description = ""
+authors = ["heumsi <heumsi@naver.com>"]
+readme = "README.md"
+packages = [{include = "web_single"}]
+
+[tool.poetry.dependencies]
+python = "^3.9"
+fastapi = {extras = ["all"], version = "^0.105.0"}
+scikit-learn = "^1.3.2"
+sqlmodel = "^0.0.14"
+pydantic-settings = "^2.1.0"
+loguru = "^0.7.2"
+python-multipart = "^0.0.6"
+
+
+[tool.poetry.group.dev.dependencies]
+pandas = "^2.1.4"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"