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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.

from ultralytics.

Hello! It seems like the model is encountering a "KeyError" which typically suggests that there's a mismatch in the number of classes between the trained model and the class names provided during inference.

Ensure that your class names file (often labeled *.names or defined in your dataset .yaml file) correctly lists all 5 classes you trained the model on, and that it's properly linked in your inference script or command line parameters. The error suggests the model is expecting a class index that doesn't exist in the provided class names.

Here’s a basic checklist:

1. Check the .names file or similar to ensure all your classes are listed there.
2. Verify the path to your class names file in the .yaml or as a model argument is correct.
3. Re-run the inference ensuring all files are in the correct locations.

If everything appears correct and the error persists, consider re-exporting or re-saving your model configuration.

Let me know how it goes, or if there's anything else you need help with! 🚀

from ultralytics.

Thank you very much, it has been resolved. When using the yolo command line, an error will be reported. Replace the command line with the following Python code to perform inference。

yolo detect predict model=./runs/detect/train/weights/best.pt source="00001.jpg"

replace

from ultralytics import YOLO,RTDETR
model = RTDETR("./ultralytics/cfg/models/rt-detr/rtdetr-l.yaml").load('runs/detect/train/weights/last.pt')
results = model(['00001.jpg']) # return a list of Results objects
for result in results:
boxes = result.boxes # Boxes object for bounding box outputs
masks = result.masks # Masks object for segmentation masks outputs
keypoints = result.keypoints # Keypoints object for pose outputs
probs = result.probs # Probs object for classification outputs
obb = result.obb # Oriented boxes object for OBB outputs
result.show() # display to screen
result.save(filename='result.jpg') # save to disk

from ultralytics.

Hello! I'm glad to hear that the issue has been resolved 🌟! The method you used to perform inference through the Python script is indeed a great alternative if you encounter issues with the command line. It also provides more flexibility for post-processing and handling the results. Thanks for sharing your solution; it could be helpful to others facing similar problems. If there's anything more we can assist you with, don't hesitate to ask. Happy detecting! 🚀

from ultralytics.