diff --git a/pytorch_vision_meal_v2.md b/pytorch_vision_meal_v2.md
index a5b36ff..79bb1b2 100644
--- a/pytorch_vision_meal_v2.md
+++ b/pytorch_vision_meal_v2.md
@@ -17,7 +17,7 @@ order: 10
 demo-model-link: https://huggingface.co/spaces/pytorch/MEAL-V2
 ---
 
-We require one additional Python dependency
+`timm` 종속 패키지 설치가 필요합니다.
 
 ```bash
 !pip install timm
@@ -25,21 +25,18 @@ We require one additional Python dependency
 
 ```python
 import torch
-# list of models: 'mealv1_resnest50', 'mealv2_resnest50', 'mealv2_resnest50_cutmix', 'mealv2_resnest50_380x380', 'mealv2_mobilenetv3_small_075', 'mealv2_mobilenetv3_small_100', 'mealv2_mobilenet_v3_large_100', 'mealv2_efficientnet_b0'
-# load pretrained models, using "mealv2_resnest50_cutmix" as an example
+# 모델 종류: 'mealv1_resnest50', 'mealv2_resnest50', 'mealv2_resnest50_cutmix', 'mealv2_resnest50_380x380', 'mealv2_mobilenetv3_small_075', 'mealv2_mobilenetv3_small_100', 'mealv2_mobilenet_v3_large_100', 'mealv2_efficientnet_b0'
+# 사전에 학습된 "mealv2_resnest50_cutmix"을 불러오는 예시입니다.
 model = torch.hub.load('szq0214/MEAL-V2','meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)
 model.eval()
 ```
 
-All pre-trained models expect input images normalized in the same way,
-i.e. mini-batches of 3-channel RGB images of shape `(3 x H x W)`, where `H` and `W` are expected to be at least `224`.
-The images have to be loaded in to a range of `[0, 1]` and then normalized using `mean = [0.485, 0.456, 0.406]`
-and `std = [0.229, 0.224, 0.225]`.
+사전에 학습된 모든 모델은 동일한 방식으로 정규화된 입력 이미지, 즉, `H` 와 `W` 는 최소 `224` 이상인 `(3 x H x W)` 형태의 3-채널 RGB 이미지의 미니 배치를 요구합니다. 이미지를 `[0, 1]` 범위에서 불러온 다음 `mean = [0.485, 0.456, 0.406]` 과 `std = [0.229, 0.224, 0.225]` 를 통해 정규화합니다.
 
-Here's a sample execution.
+실행 예시입니다.
 
 ```python
-# Download an example image from the pytorch website
+# 파이토치 웹사이트에서 예제 이미지 다운로드
 import urllib
 url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
 try: urllib.URLopener().retrieve(url, filename)
@@ -47,7 +44,7 @@ except: urllib.request.urlretrieve(url, filename)
 ```
 
 ```python
-# sample execution (requires torchvision)
+# 실행 예시 (torchvision 필요)
 from PIL import Image
 from torchvision import transforms
 input_image = Image.open(filename)
@@ -58,32 +55,32 @@ preprocess = transforms.Compose([
     transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
 ])
 input_tensor = preprocess(input_image)
-input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model
+input_batch = input_tensor.unsqueeze(0) # 모델에서 요구하는 미니배치 생성
 
-# move the input and model to GPU for speed if available
+# 가능하다면 속도를 위해 입력과 모델을 GPU로 옮깁니다.
 if torch.cuda.is_available():
     input_batch = input_batch.to('cuda')
     model.to('cuda')
 
 with torch.no_grad():
     output = model(input_batch)
-# Tensor of shape 1000, with confidence scores over Imagenet's 1000 classes
+# 1000개의 ImageNet 클래스에 대한 신뢰도 점수(confidence score)를 가진 1000 크기의 Tensor
 print(output[0])
-# The output has unnormalized scores. To get probabilities, you can run a softmax on it.
+# output엔 정규화되지 않은 신뢰도 점수가 있습니다. 확률 값을 얻으려면 softmax를 실행하세요.
 probabilities = torch.nn.functional.softmax(output[0], dim=0)
 print(probabilities)
 ```
 
 ```
-# Download ImageNet labels
+# ImageNet 레이블 다운로드
 !wget https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt
 ```
 
 ```
-# Read the categories
+# 카테고리 읽기
 with open("imagenet_classes.txt", "r") as f:
     categories = [s.strip() for s in f.readlines()]
-# Show top categories per image
+# 이미지별 Top5 카테고리 조회
 top5_prob, top5_catid = torch.topk(probabilities, 5)
 for i in range(top5_prob.size(0)):
     print(categories[top5_catid[i]], top5_prob[i].item())