Modify ultralytics_yolov5.md : translate english to korean

Author: yh00214

ultralytics_yolov5.md

@@ -18,18 +18,17 @@ demo-model-link: https://huggingface.co/spaces/pytorch/YOLOv5
 
 ## Before You Start
 
-Start from a **Python>=3.8** environment with **PyTorch>=1.7** installed. To install PyTorch see [https://pytorch.org/get-started/locally/](https://pytorch.org/get-started/locally/). To install YOLOv5 dependencies:
+**Python>=3.8**과 **PyTorch>=1.7** 환경을 갖춘 상태에서 시작해주세요. PyTorch를 설치해야 한다면 [https://pytorch.org/get-started/locally/](https://pytorch.org/get-started/locally/) 를 참고하세요. YOLOv5 dependency를 설치하려면:
 ```bash
-pip install -qr https://raw.githubusercontent.com/ultralytics/yolov5/master/requirements.txt  # install dependencies
+pip install -qr https://raw.githubusercontent.com/ultralytics/yolov5/master/requirements.txt  # 필요한 모듈 설치
 ```
 
-
 ## Model Description
 
 <img width="800" alt="YOLOv5 Model Comparison" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/model_comparison.png">
 &nbsp;
 
-[YOLOv5](https://ultralytics.com/yolov5) 🚀 is a family of compound-scaled object detection models trained on the COCO dataset, and includes simple functionality for Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, and export to ONNX, CoreML and TFLite.
+[YOLOv5](https://ultralytics.com/yolov5) 🚀는 compound-scaling을 사용하고 COCO dataset으로 학습한 모델들 중 하나이고, Test Time Augmentation (TTA), 모델 앙상블(model ensembling), 하이퍼파라미터 평가(hyperparameter evolution), 그리고 ONNX, CoreML과 TFLite로 변환(export)을 간단하게 해주는 기능이 포함되어 있습니다.
 
 |Model |size<br><sup>(pixels) |mAP<sup>val<br>0.5:0.95 |mAP<sup>test<br>0.5:0.95 |mAP<sup>val<br>0.5 |Speed<br><sup>V100 (ms) | |params<br><sup>(M) |FLOPS<br><sup>640 (B)
 |---   |---  |---        |---         |---             |---                |---|---              |---
@@ -40,9 +39,10 @@ pip install -qr https://raw.githubusercontent.com/ultralytics/yolov5/master/requ
 |[YOLOv5x6](https://github.com/ultralytics/yolov5/releases) TTA |1280 |**55.0** |**55.0** |**72.0** |70.8 | |-  |-
 
 <details>
-  <summary>Table Notes (click to expand)</summary>
+  <summary>표에 대한 설명 (확장하려면 클릭)</summary>
 
   * AP<sup>test</sup> denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results denote val2017 accuracy.
+  * AP<sup>test</sup>는 
   * AP values are for single-model single-scale unless otherwise noted. **Reproduce mAP** by `python test.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
   * Speed<sub>GPU</sub> averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) V100 instance, and includes FP16 inference, postprocessing and NMS. **Reproduce speed** by `python test.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45`
   * All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation).
@@ -53,7 +53,7 @@ pip install -qr https://raw.githubusercontent.com/ultralytics/yolov5/master/requ
 <p align="left"><img width="800" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/model_plot.png"></p>
 
 <details>
-  <summary>Figure Notes (click to expand)</summary>
+  <summary>그림에 대한 설명 (확장하려면 클릭)</summary>
 
   * GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS.
   * EfficientDet data from [google/automl](https://github.com/google/automl) at batch size 8.
@@ -64,27 +64,27 @@ pip install -qr https://raw.githubusercontent.com/ultralytics/yolov5/master/requ
 ## Load From PyTorch Hub
 
 
-This example loads a pretrained **YOLOv5s** model and passes an image for inference. YOLOv5 accepts **URL**, **Filename**, **PIL**, **OpenCV**, **Numpy** and **PyTorch** inputs, and returns detections in **torch**, **pandas**, and **JSON** output formats. See our [YOLOv5 PyTorch Hub Tutorial](https://github.com/ultralytics/yolov5/issues/36) for details.
+이 예제에서는 사전 훈련된(pretrained) **YOLOv5s** 모델을 불러와 이미지에 대해 추론을 진행합니다. YOLOv5s는 **URL**, **파일 이름**, **PIL**, **OpenCV**, **Numpy**와 **PyTorch** 형식의 입력을 받고, **torch**, **pandas**, **JSON** 출력 형태로 탐지 결과를 반환합니다. 자세히 알고 싶으면 [YOLOv5 파이토치 허브 튜토리얼](https://github.com/ultralytics/yolov5/issues/36) 을 참고하세요.
 
 
 ```python
 import torch
 
-# Model
+# 모델
 model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
 
-# Images
-imgs = ['https://ultralytics.com/images/zidane.jpg']  # batch of images
+# 이미지
+imgs = ['https://ultralytics.com/images/zidane.jpg']  # 이미지들의 배치(Batch)
 
-# Inference
+# 추론
 results = model(imgs)
 
-# Results
+# 결과
 results.print()
-results.save()  # or .show()
+results.save()  # 혹은 .show()
 
-results.xyxy[0]  # img1 predictions (tensor)
-results.pandas().xyxy[0]  # img1 predictions (pandas)
+results.xyxy[0]  # img1에 대한 예측 (tensor)
+results.pandas().xyxy[0]  # img1에 대한 예측 (pandas)
 #      xmin    ymin    xmax   ymax  confidence  class    name
 # 0  749.50   43.50  1148.0  704.5    0.874023      0  person
 # 1  433.50  433.50   517.5  714.5    0.687988     27     tie
@@ -101,7 +101,6 @@ results.pandas().xyxy[0]  # img1 predictions (pandas)
 ## Contact
 
 
-**Issues should be raised directly in https://github.com/ultralytics/yolov5.** For business inquiries or professional support requests please visit [https://ultralytics.com](https://ultralytics.com) or email Glenn Jocher at [[email protected]](mailto:[email protected]).
-
+**이슈가 생기면 즉시 https://github.com/ultralytics/yolov5 로 알려주세요.** 비즈니스 상의 문의나 전문적인 지원 요청은 [https://ultralytics.com](https://ultralytics.com) 을 방문하거나 Glenn Jocher의 이메일인 [[email protected]](mailto:[email protected]) 으로 연락 주세요.
 
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