Comments (6)
👋 Hello @Godk02, 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.
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.
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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.
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@Godk02 hello!
Thank you for reaching out with your question. Yes, you can rename the classes in a YOLO-trained model. The YOLO
class provides a method called set_classes
that allows you to set new class names.
Here’s a quick example to illustrate how you can do this:
from ultralytics import YOLO
# Load your trained model
model = YOLO('path/to/your/model.pt')
# Define your new class names
new_classes = ["class1", "class2", "class3"]
# Set the new class names
model.set_classes(new_classes)
# Verify the changes
print(model.model.names)
This will update the class names in your model to the ones specified in new_classes
.
If you encounter any issues or have further questions, please ensure you are using the latest versions of torch
and ultralytics
. If the problem persists, providing a minimum reproducible example would be very helpful for us to investigate further. You can find more details on creating a reproducible example here.
Feel free to reach out if you need more assistance. Happy coding! 😊
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@Godk02你好!
感谢您提出问题。是的,您可以重命名 YOLO 训练模型中的类。该类
YOLO
提供了一种名为 的方法set_classes
,允许您设置新的类名。以下是一个简单的例子来说明如何做到这一点:
from ultralytics import YOLO # Load your trained model model = YOLO('path/to/your/model.pt') # Define your new class names new_classes = ["class1", "class2", "class3"] # Set the new class names model.set_classes(new_classes) # Verify the changes print(model.model.names)这会将模型中的类名更新为中指定的类名
new_classes
。如果您遇到任何问题或有其他疑问,请确保您使用的是最新版本的
torch
和。如果问题仍然存在,提供最小可重现示例将非常有助于我们进一步调查。您可以在此处ultralytics
找到有关创建可重现示例的更多详细信息。如果您需要更多帮助,请随时联系我们。祝您编码愉快!😊
Thank u for your response!
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Hello @Godk02,
You're welcome! I'm glad the information was helpful. If you have any more questions or run into any issues, feel free to ask. We're here to help!
Just a quick reminder: if you encounter any bugs or issues, please ensure you are using the latest versions of torch
and ultralytics
. If the problem persists, providing a minimum reproducible example will greatly assist us in diagnosing and resolving the issue. You can find more details on creating a reproducible example here.
Happy coding! 😊
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Member
Can I import the YOLOv9 model and then modify it with this code? (I tried but it doesn't seem to work). Do you have any good suggestions? I would really appreciate it, thank you very much!
from ultralytics.
Hello @Godk02,
Thank you for your question! Yes, you can import and modify the YOLOv9 model. Here’s a concise example to guide you through the process:
from ultralytics import YOLO
# Load the YOLOv9 model
model = YOLO('yolov9.pt')
# Modify the model as needed
# For example, changing class names
new_classes = ["class1", "class2", "class3"]
model.set_classes(new_classes)
# Verify the changes
print(model.model.names)
If you encounter issues, please ensure you are using the latest versions of torch
and ultralytics
. If the problem persists, providing a minimum reproducible example will greatly help us investigate further. You can find more details on creating a reproducible example here.
Feel free to reach out if you need more assistance. Happy coding! 😊
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Related Issues (20)
- Train subclass in Coco data set HOT 4
- Oriented Bounding box health check HOT 3
- [YoloV8] Torch compile model shows metrics degradation on the coco128 dataset HOT 2
- Address Discord badge error HOT 1
- How to reduce the number of target contour points predicted by YOLOv8-Sseg HOT 3
- val step slow down during training HOT 7
- Batch inference speed same than looping through a bunch of imgs HOT 3
- Using YOLOv8(seg) with SHAP HOT 5
- yolov8 object_counting in and out doesn't differentiate for defined line HOT 4
- how to set `verbose:false` so that model can predict the batches without printing anything in the terminal HOT 1
- Questions about incremental training HOT 3
- How can I use the segmentation models of previous versions? HOT 4
- yolov8-obb plot train labels maybe error HOT 2
- Error Code 2: Internal Error (Assertion cublasStatus == CUBLAS_STATUS_SUCCESS failed. ) HOT 4
- Yolov10 Can't get attribute 'SCDown' on <module 'ultralytics.nn.modules.block' from 'C:\\Users\\ZHANG\\miniconda3\\lib\\site-packages\\ultralytics\\nn\\modules\\block.py'> HOT 20
- yolov8 -- After the cache is turned on, the memory occupied by reading val data is too large HOT 5
- YOLOv10 Performance Issue: Version 3.12 Fast, But 3.11 and Below Very Slow HOT 8
- yolo8 onnx in opencv HOT 2
- Is OBB available for yolov9 and v10 ? HOT 1
- Clamping in bbox2dist HOT 2
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