Comments (3)
Hello!
Thank you for your interest in extending YOLOv8 with class incremental learning capabilities. Currently, there isn't a built-in feature in YOLOv8 specifically designed for incremental class learning. However, this is indeed an interesting area for future development.
While we don't have immediate plans to incorporate this feature, we are always open to community contributions and suggestions. If you or anyone else is interested in developing this feature, we encourage you to fork the repository and experiment with your ideas. Contributions that align with our goals and show promising results could potentially be merged into the main branch.
For now, you might consider adapting existing incremental learning frameworks to work with YOLOv8 or exploring ways to modify the training process to accommodate new classes without retraining from scratch.
Thanks again for your suggestion, and we look forward to seeing innovative contributions from the community!
from ultralytics.
👋 Hello @taheritajar, 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.
Join the vibrant Ultralytics Discord 🎧 community for real-time conversations and collaborations. This platform offers a perfect space to inquire, showcase your work, and connect with fellow Ultralytics users.
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 there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help.
For additional resources and information, please see the links below:
- Docs: https://docs.ultralytics.com
- HUB: https://hub.ultralytics.com
- Community: https://community.ultralytics.com
Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!
Thank you for your contributions to YOLO 🚀 and Vision AI ⭐
from ultralytics.
Related Issues (20)
- 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
- Exporting after training on YoloV10 raise a ValueError with MultiGPU HOT 7
- Yolov8 classifier training: impossible to disable some augmentation options HOT 5
- Decoupled Head in YOLOv8 HOT 5
- How to increase the weight of segmentation loss in a segmentation task? HOT 11
- Why is the performance of detection task better than segmentation task? HOT 8
- Permission Denied Error in the middle/end of training. HOT 5
- Show the true label HOT 1
- The confidence difference of pt and onnx model on yolov9. HOT 3
- About Detection Speed YOLOV8 HOT 5
- why YOLO cannot load my dataset HOT 2
- How to read continuous image frames for training? HOT 12
- how to train v8 with ALL CPU HOT 2
- What are the plans for supporting Python 3.13 free-threading? HOT 3
- Prediction model setup: expected scalar type Half but found Float HOT 4
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