用coco2017数据集训练完,用生成的best.pt权重文件进行测试,无法识别出物体,没有识别框框 · Issue #11316 · ultralytics/yolov5 · GitHub 您所在的位置:网站首页 apple care有没有用 用coco2017数据集训练完,用生成的best.pt权重文件进行测试,无法识别出物体,没有识别框框 · Issue #11316 · ultralytics/yolov5 · GitHub

用coco2017数据集训练完,用生成的best.pt权重文件进行测试,无法识别出物体,没有识别框框 · Issue #11316 · ultralytics/yolov5 · GitHub

2023-04-11 12:10| 来源: 网络整理| 查看: 265

👋 Hello @Melancholy792, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

If this is a 🐛 Bug Report, please provide a minimum reproducible example to help us debug it.

If this is a custom training ❓ Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our Tips for Best Training Results.

Requirements

Python>=3.7.0 with all requirements.txt installed including PyTorch>=1.7. To get started:

git clone https://github.com/ultralytics/yolov5 # clone cd yolov5 pip install -r requirements.txt # install Environments

YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

Notebooks with free GPU: Run on Gradient Open In Colab Open In Kaggle Google Cloud Deep Learning VM. See GCP Quickstart Guide Amazon Deep Learning AMI. See AWS Quickstart Guide Docker Image. See Docker Quickstart Guide Docker Pulls Status

YOLOv5 CI

If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training, validation, inference, export and benchmarks on MacOS, Windows, and Ubuntu every 24 hours and on every commit.

Introducing YOLOv8 🚀

We're excited to announce the launch of our latest state-of-the-art (SOTA) object detection model for 2023 - YOLOv8 🚀!

Designed to be fast, accurate, and easy to use, YOLOv8 is an ideal choice for a wide range of object detection, image segmentation and image classification tasks. With YOLOv8, you'll be able to quickly and accurately detect objects in real-time, streamline your workflows, and achieve new levels of accuracy in your projects.

Check out our YOLOv8 Docs for details and get started with:

pip install ultralytics


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