YOLOv5, the fifth iteration of the revolutionary "You Only Look Once" object detection model, is designed to deliver high-speed, high-accuracy results in real-time.
YOLOv5是革命性的*你只要看一次*目标检测模型的第五个迭代版本，旨在实时提供高速、高精度的结果。
Built on PyTorch, this powerful deep learning framework has garnered immense popularity for its versatility, ease of use, and high performance. Our documentation guides you through the installation process, explains the architectural nuances of the model, showcases various use-cases, and provides a series of detailed tutorials. These resources will help you harness the full potential of YOLOv5 for your computer vision projects.
构建在PyTorch之上，这个强大的深度学习框架因其多功能性、易用性和高性能而广受欢迎。我们的文档将指导您完成安装过程，解释模型的架构细节，展示各种用例，并提供一系列详细的教程[1]。这些资源将帮助您充分发挥YOLOv5在您的计算机视觉项目中的潜力。
- 代码仓安装:
git clone https://github.com/ultralytics/yolov5 # clone cd yolov5 pip install -r requirements.txt # install
- 推理
import torch # Model model = torch.hub.load("ultralytics/yolov5", "yolov5s") # or yolov5n - yolov5x6, custom # Images img = "https://ultralytics.com/images/zidane.jpg" # or file, Path, PIL, OpenCV, numpy, list # Inference results = model(img) # Results results.print() # or .show(), .save(), .crop(), .pandas(), etc.
- 训练
python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128 yolov5s 64 yolov5m 40 yolov5l 24 yolov5x 16
参考:
[1] GitHub - ultralytics/yolov5: YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
