Comments (4)
👋 Hello @toni-santos, 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.
from ultralytics.
Hello!
Thanks for reaching out with your question. The auto_annotate
function indeed utilizes pre-trained models which are capable of detecting and segmenting multiple classes. If you're seeing unwanted classes like "car" or "table," it's because the pre-trained model includes these classes by default.
To generate segmentations only for your specific class, you would need to modify the detection model to detect only your class of interest before running auto_annotate
. This involves re-training the detection model on your dataset with only the relevant class. Once you have a model trained specifically for your class, you can then use auto_annotate
with this model to generate the desired segmentation annotations.
If you need further guidance on re-training your model or any other questions, feel free to ask. Happy to help!
from ultralytics.
That's it! :) Thank you very much!
from ultralytics.
You're welcome! 😊 If you have any more questions in the future, feel free to reach out. Happy coding!
from ultralytics.
Related Issues (20)
- run train HOT 4
- Why when I put Pretrained = False, yolov8 still transfer and freeze weights HOT 4
- YOLOv8 is jointly trained with other models HOT 3
- Optimizer='auto' problem HOT 2
- Docker run yolov8 report error:Killed, OOM HOT 6
- Is there any other way to get faster YOLOv8n results without using GPU HOT 2
- Default training parameters for yolov8n? HOT 6
- Exporting a YOLO model fails when current directory is in a different filesystem HOT 6
- YOLOv8 resizes input images differently when training for classification? HOT 3
- FedAvg with YOLO HOT 6
- YOLOv8, v10, RT-DETR albumentation do not apply HOT 5
- How can i train better my project ? YOLOV8 HOT 14
- Codebase for running YoloV10 with ONNX HOT 8
- xywh returns wrong result while xyxy returns right result HOT 1
- Support distributed evaluation during training process HOT 1
- Is there an example of yolov8n-segn Android split HOT 2
- @glenn-jocher tracker is not working for custom trained models,
- multi input video to YOLOv8 and using bytetrack.yaml return same ID to different object and keep increasing HOT 2
- The engine model RTX3060 exported by RTX4070 cannot be inferred HOT 3
- YOLO(model_yaml).load(model.pt) not work. HOT 5
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