image 1/1 D:\yolov8\ultralytics-main\ultralytics\assets\bus.jpg: 640x480 (no detections), 510.2ms Speed: 15.5ms preprocess, 510.2ms inference, 18.0ms postprocess per image at shape (1, 3, 640, 480) about ultralytics

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👋 Hello @lndabgjk, thank you for your interest in Ultralytics YOLOv8 🚀! We recommend a visit to the Docs for new users where you can find many Python and CLI usage examples and where many of the most common questions may already be answered.

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

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Install

Pip install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.

pip install ultralytics

Environments

YOLOv8 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:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

Status

If this badge is green, all Ultralytics CI tests are currently passing. CI tests verify correct operation of all YOLOv8 Modes and Tasks on macOS, Windows, and Ubuntu every 24 hours and on every commit.

[glenn-jocher]

@lndabgjk hello,

Thank you for reaching out and providing detailed information about the issue you're encountering. It appears that you're experiencing a problem with detecting objects using a model built from the yolov8n.yaml configuration file, while the pre-trained yolov8n.pt model works as expected.

To assist you better, could you please confirm the following:

1. Ensure you are using the latest versions of the ultralytics and torch packages. You can update them using:

   pip install --upgrade ultralytics torch

2. Verify that the model built from yolov8n.yaml has been trained before making predictions. The yolov8n.yaml file only defines the model architecture and does not include pre-trained weights. You need to train the model on a dataset before it can make accurate predictions. Here’s an example of how to train the model:

   from ultralytics import YOLO
   
   # Load a model from YAML and train it
   model = YOLO('yolov8n.yaml')
   model.train(data='coco128.yaml', epochs=100, imgsz=640)

3. After training, you can then use the trained model to make predictions:

   result = model.predict(source='ultralytics/assets/bus.jpg')

If you have already trained the model and are still facing issues, please provide any additional details or errors you might be encountering. This will help us reproduce the issue and investigate further.

Thank you for your cooperation, and we look forward to resolving this for you! 😊

[lndabgjk]

解决了，因为用的pycharm2024版本，配置conda环境时不能直接设置conda环境，于是选择了系统解释器，但是现在我可以直接设置conda环境了，成功运行出想要的结果。

[glenn-jocher]

Hello @lndabgjk,

I'm glad to hear that you resolved the issue by configuring the conda environment correctly in PyCharm! 🎉

If you encounter any further questions or need additional assistance, feel free to reach out. We're here to help!

Happy coding! 😊