diff --git a/pytorch_vision_resnet.md b/pytorch_vision_resnet.md
index a64244a..1cf498e 100644
--- a/pytorch_vision_resnet.md
+++ b/pytorch_vision_resnet.md
@@ -20,7 +20,7 @@ demo-model-link: https://huggingface.co/spaces/pytorch/ResNet
 ```python
 import torch
 model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet18', pretrained=True)
-# or any of these variants
+#  Layer의 수에 따라 달라지는 Resnet 모델들.
 # model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet34', pretrained=True)
 # model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet50', pretrained=True)
 # model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet101', pretrained=True)
@@ -28,15 +28,13 @@ model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet18', 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]`.
+모든 사전학습 된(pre-trained) 모델들은 인풋 이미지 같은 방법으로 정규화된다. 3개의 채널을 가진 RGB 미니배치는 H와 W가 최소 224 이상인 (3 x H x W)의 모양을 가진다.  
+이미지는 [0, 1]범위로 로드한 다음  mean = [0.485, 0.456, 0.406],  std = [0.229, 0.224, 0.225]을 통해 정규화된다. 
 
-Here's a sample execution.
+다음은 실행한 예제이다. 
 
 ```python
-# Download an example image from the pytorch website
+# 파이토치 웹사이트에서 예제 이미지를 다운로드 받기 
 import urllib
 url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
 try: urllib.URLopener().retrieve(url, filename)
@@ -44,7 +42,7 @@ except: urllib.request.urlretrieve(url, filename)
 ```
 
 ```python
-# sample execution (requires torchvision)
+# 실행 예제(torchvision을 필요로함)
 from PIL import Image
 from torchvision import transforms
 input_image = Image.open(filename)
@@ -57,7 +55,7 @@ preprocess = transforms.Compose([
 input_tensor = preprocess(input_image)
 input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model
 
-# move the input and model to GPU for speed if available
+# 가능하다면 속도를 위해 입력과 모델을 GPU로 옮기기
 if torch.cuda.is_available():
     input_batch = input_batch.to('cuda')
     model.to('cuda')
@@ -66,32 +64,29 @@ with torch.no_grad():
     output = model(input_batch)
 # Tensor of shape 1000, with confidence scores over Imagenet's 1000 classes
 print(output[0])
-# The output has unnormalized scores. To get probabilities, you can run a softmax on it.
+# 결과는 정규화되지 않은 점수를 가집니다. 확률을 얻기 위해서는 소프트맥스로 돌리세요.
 probabilities = torch.nn.functional.softmax(output[0], dim=0)
 print(probabilities)
 ```
 
 ```
-# Download ImageNet labels
+# 이미지넷 라벨들을 다운로드 
 !wget https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt
 ```
 
 ```
-# Read the categories
+# 카테고리들 읽기 
 with open("imagenet_classes.txt", "r") as f:
     categories = [s.strip() for s in f.readlines()]
-# Show top categories per image
+# 각각의 이미지별로 탑 카테고리를 보이기 
 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())
 ```
 
-### Model Description
+### 모델 묘사
 
-Resnet models were proposed in "Deep Residual Learning for Image Recognition".
-Here we have the 5 versions of resnet models, which contains 18, 34, 50, 101, 152 layers respectively.
-Detailed model architectures can be found in Table 1.
-Their 1-crop error rates on imagenet dataset with pretrained models are listed below.
+“이미지 인식을 위한 잔차 딥러닝 ([Deep Residual Learning for Image Recognition](https://openaccess.thecvf.com/content_cvpr_2016/papers/He_Deep_Residual_Learning_CVPR_2016_paper.pdf))”에서 Resnet 모델들이 제안되었다. 각각 18, 34, 50, 101, 152개의 층을 가진 5개 버전의 resnet 모델이 있다. 상세 모델 구조는 1번 표에서 찾을 수 있다. 모델들의 1-crop 에러 비율은 선행 학습된(pre-trained) 이미지넷 데이터셋 밑에 나열돼있다.
 
 | Model structure | Top-1 error | Top-5 error |
 | --------------- | ----------- | ----------- |
@@ -101,6 +96,6 @@ Their 1-crop error rates on imagenet dataset with pretrained models are listed b
 |  resnet101      | 22.63       | 6.44        |
 |  resnet152      | 21.69       | 5.94        |
 
-### References
+### 참고자료
 
  - [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)