diff --git a/ultralytics_yolov5.md b/ultralytics_yolov5.md index 88dfa8d..ebaa07a 100644 --- a/ultralytics_yolov5.md +++ b/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 YOLOv5 Model Comparison   -[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
(pixels) |mAPval
0.5:0.95 |mAPtest
0.5:0.95 |mAPval
0.5 |Speed
V100 (ms) | |params
(M) |FLOPS
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 | |- |-
- Table Notes (click to expand) + 표에 대한 설명 (확장하려면 클릭) * APtest denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results denote val2017 accuracy. + * APtest는 * 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` * SpeedGPU 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

- Figure Notes (click to expand) + 그림에 대한 설명 (확장하려면 클릭) * 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 [glenn.jocher@ultralytics.com](mailto:glenn.jocher@ultralytics.com). - +**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 [glenn.jocher@ultralytics.com](mailto:glenn.jocher@ultralytics.com) 으로 연락 주세요.