diff --git a/hustvl_yolop.md b/hustvl_yolop.md
index 5565c52..d13d661 100644
--- a/hustvl_yolop.md
+++ b/hustvl_yolop.md
@@ -15,8 +15,10 @@ featured_image_2: no-image
 accelerator: cuda-optional
 demo-model-link: https://huggingface.co/spaces/pytorch/YOLOP
 ---
-## Before You Start
-To install YOLOP dependencies:
+<!-- ## Before You Start -->
+## 시작하기 전에
+<!-- To install YOLOP dependencies: -->
+YOLOP 종속 패키지를 설치하려면:
 ```bash
 pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt  # install dependencies
 ```
@@ -24,17 +26,18 @@ pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt  # in
 
 ## YOLOP: You Only Look Once for Panoptic driving Perception
 
-### Model Description
+### 모델 설명
 
 <img width="800" alt="YOLOP Model" src="https://github.com/hustvl/YOLOP/raw/main/pictures/yolop.png">
 &nbsp;
 
-- YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the **BDD100K** dataset.
+<!-- - YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the **BDD100K** dataset. -->
+- YOLOP는 자율 주행에서 중요한, 다음의 세 가지 작업을 공동으로 처리할 수 있는 효율적인 다중 작업 네트워크 입니다.: 물체 감지(object detection), 주행 영역 분할(drivable area segmentation), 차선 인식(lane detection). 또한 YOLOP는  **BDD100K** 데이터셋에서 최신 기술(state-of-the-art)의 수준을 유지하면서 임베디드 기기에서 실시간성에 도달한 최초의 사례입니다.
 
 
-### Results
+### 결과
 
-#### Traffic Object Detection Result
+#### 차량 객체(Traffic Object) 인식 결과
 
 | Model          | Recall(%) | mAP50(%) | Speed(fps) |
 | -------------- | --------- | -------- | ---------- |
@@ -44,7 +47,7 @@ pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt  # in
 | `YOLOv5s`      | 86.8      | 77.2     | 82         |
 | `YOLOP(ours)`  | 89.2      | 76.5     | 41         |
 
-#### Drivable Area Segmentation Result
+#### 주행 가능 영역 인식 결과
 
 | Model         | mIOU(%) | Speed(fps) |
 | ------------- | ------- | ---------- |
@@ -53,7 +56,7 @@ pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt  # in
 | `PSPNet`      | 89.6    | 11.1       |
 | `YOLOP(ours)` | 91.5    | 41         |
 
-#### Lane Detection Result
+#### 차선 인식 결과
 
 | Model         | mIOU(%) | IOU(%) |
 | ------------- | ------- | ------ |
@@ -62,7 +65,7 @@ pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt  # in
 | `ENet-SAD`    | 36.56   | 16.02  |
 | `YOLOP(ours)` | 70.50   | 26.20  |
 
-#### Ablation Studies 1: End-to-end v.s. Step-by-step
+#### 요소 제거 모델 평가 1 (Ablation Studies 1): End-to-end v.s. Step-by-step
 
 | Training_method | Recall(%) | AP(%) | mIoU(%) | Accuracy(%) | IoU(%) |
 | --------------- | --------- | ----- | ------- | ----------- | ------ |
@@ -72,7 +75,7 @@ pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt  # in
 | `ED-S-W`        | 87.5      | 76.1  | 91.6    | 68.0        | 26.8   |
 | `End-to-end`    | 89.2      | 76.5  | 91.5    | 70.5        | 26.2   |
 
-#### Ablation Studies 2: Multi-task v.s. Single task
+#### 요소 제거 모델 평가 1 (Ablation Studies 2): Multi-task v.s. Single task
 
 | Training_method | Recall(%) | AP(%) | mIoU(%) | Accuracy(%) | IoU(%) | Speed(ms/frame) |
 | --------------- | --------- | ----- | ------- | ----------- | ------ | --------------- |
@@ -83,50 +86,59 @@ pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt  # in
 
 **Notes**:
 
-- In table 4, E, D, S and W refer to Encoder, Detect head, two Segment heads and whole network. So the Algorithm (First, we only train Encoder and Detect head. Then we freeze the Encoder and Detect head as well as train two Segmentation heads. Finally, the entire network is trained jointly for all three tasks.) can be marked as ED-S-W, and the same for others.
+<!-- - In table 4, E, D, S and W refer to Encoder, Detect head, two Segment heads and whole network. So the Algorithm (First, we only train Encoder and Detect head. Then we freeze the Encoder and Detect head as well as train two Segmentation heads. Finally, the entire network is trained jointly for all three tasks.) can be marked as ED-S-W, and the same for others. -->
+- 표 4에서 E, D, S, W는 인코더(Encoder), 검출 헤드(Detect head), 2개의 세그먼트 헤드(Segment heads) 와 전체 네트워크를 참조합니다. 그래서 알고리즘은 ED-S-W로 표기되며, 다른 알고리즘도 마찬가지입니다. (첫째, 인코더 및 검출 헤드만 학습합니다. 그 후, 인코더 및 검출 헤드를 고정하고 두 개의 분할(segmentation) 헤드를 학습합니다. 마지막으로, 전체 네트워크는 세 가지 작업 모두에 대해 함께 학습됩니다.)
 
-### Visualization
 
-#### Traffic Object Detection Result
+### 시각화
+
+#### 차량 객체(Traffic Object) 인식 결과
 
 <img width="800" alt="Traffic Object Detection Result" src="https://github.com/hustvl/YOLOP/raw/main/pictures/detect.png">
 &nbsp;
 
-#### Drivable Area Segmentation Result
+#### 주행 가능 영역 인식 결과
 
 <img width="800" alt="Drivable Area Segmentation Result" src="https://github.com/hustvl/YOLOP/raw/main/pictures/da.png">
 &nbsp;
 
-#### Lane Detection Result
+#### 차선 인식 결과
 
 <img width="800" alt="Lane Detection Result" src="https://github.com/hustvl/YOLOP/raw/main/pictures/ll.png">
 &nbsp;
 
-**Notes**:
+<!-- **Notes**: -->
+**안내**:
 
-- The visualization of lane detection result has been post processed by quadratic fitting.
+<!-- - The visualization of lane detection result has been post processed by quadratic fitting. -->
+- 차선 인식의 시각화 결과는 이차 곡선 fitting(quadratic fitting)을 통해 후처리(post processed) 되었습니다.
 
 ### Deployment
 
-Our model can reason in real-time on **Jetson Tx2**, with **Zed Camera** to capture image. We use **TensorRT** tool for speeding up. We provide code for deployment and reasoning of model in [github code](https://github.com/hustvl/YOLOP/tree/main/toolkits/deploy).
+<!-- Our model can reason in real-time on **Jetson Tx2**, with **Zed Camera** to capture image. We use **TensorRT** tool for speeding up. We provide code for deployment and reasoning of model in [github code](https://github.com/hustvl/YOLOP/tree/main/toolkits/deploy). -->
+YOLOP 모델은 이미지를 캡처하기 위해 **Zed Camera**를 사용해 **Jetson Tx2**에서 실시간으로 추론할 수 있습니다. 속도 향상을 위해 **TensorRT**를 사용합니다. 모델의 배포와 추론을 위해 [github code](https://github.com/hustvl/YOLOP/tree/main/toolkits/deploy) 에서 코드를 제공합니다.
 
 
-### Load From PyTorch Hub
-This example loads the pretrained **YOLOP** model and passes an image for inference.
+<!-- ### Load From PyTorch Hub -->
+### 파이토치 허브로부터 모델 불러오기
+<!-- This example loads the pretrained **YOLOP** model and passes an image for inference. -->
+이 예제는 사전에 학습된 **YOLOP** 모델을 불러오고 추론을 위한 이미지를 모델에 전달합니다.
 ```python
 import torch
 
-# load model
+# 모델 불러오기
 model = torch.hub.load('hustvl/yolop', 'yolop', pretrained=True)
 
-#inference
+# 추론(Inference)
 img = torch.randn(1,3,640,640)
 det_out, da_seg_out,ll_seg_out = model(img)
 ```
 
-### Citation
+### 인용(Citation)
 
-See for more detail in [github code](https://github.com/hustvl/YOLOP) and [arxiv paper](https://arxiv.org/abs/2108.11250).
+<!-- See for more detail in [github code](https://github.com/hustvl/YOLOP) and [arxiv paper](https://arxiv.org/abs/2108.11250). -->
+더 자세한 내용은 [github code](https://github.com/hustvl/YOLOP)와 [arxiv paper](https://arxiv.org/abs/2108.11250)에서 볼 수 있습니다.
 
-If you find our paper and code useful for your research, please consider giving a star and citation:
+<!-- If you find our paper and code useful for your research, please consider giving a star and citation: -->
+본 논문과 코드가 여러분의 연구에 유용하다고 판단되면, star를 주는 것과 인용하는 것을 고려해 주세요: