Comments (3)
@MosbehBarhoumi hello!
YOLOv8 currently doesn't have built-in support specifically tailored for semi-supervised learning directly. Nonetheless, a common approach includes training your model initially with the labeled dataset to establish a baseline and then utilizing this trained model to make predictions on your unlabeled dataset.
You can use these predictions to manually verify or correct the highest confidence outputs, incrementally incorporating them into your training process. This iterative method can create a refined model that leverages both labeled and unlabeled data effectively.
Here's a basic idea on how you might start:
- Train your initial model on your labeled data.
- Use the model to predict on the unlabeled data.
- Manually check high-confidence predictions to use as pseudo labels.
- Re-train your model by combining the original labeled data with the newly labeled data.
For implementation, you can use the predictions from:
from ultralytics import YOLO
model = YOLO('path/to/your/model.pt')
results = model.predict('path/to/unlabeled/images/')
results.save() # save the predictions
You might gradually improve and expand your dataset using the procedure outlined above. While it may initially involve manual effort to verify high-confidence predictions, it can significantly enhance your model with the available unlabeled data.
Feel free to reach out if you need more detailed guidance on any of the steps!
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👋 Hello @MosbehBarhoumi, 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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@glenn-jocher Thanks for your detailed answer. I'm currently doing exactly that, though I thought there might be a more efficient way to save even more time.
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Related Issues (20)
- Segmentation Fault (Core Dumped) Bug in Fine-Tuned YOLOv8 Model with Low Confidence Inference HOT 8
- Optimize parameter HOT 20
- how to trim videos in yolo using cv2 HOT 2
- How YOLO optimize hyperparams HOT 2
- Yolov8 detection model on embedded devices HOT 9
- Is it possible to use additional object attributes in model validation to obtain metrics based on them? HOT 4
- Code for Yolov8 layer HOT 5
- test image resize and count HOT 8
- gpu is stuck HOT 2
- Custom validation output issues HOT 1
- Better evaluation results show. HOT 5
- problem in predict yolov8 HOT 2
- yolo8-n FPS difference yaml vs .pt HOT 4
- postprocess is very slow HOT 4
- A generalized YOLOv8 model with DET, OBB, SEG and POSE tasks. HOT 2
- Number of model parameters and FLOPs based on Ultralytics HOT 1
- Prediction on SAM Model doesn't support specifying classes to predict HOT 4
- LetterBox Bug(ultralytics/data/augment.py) HOT 2
- Yolo-Segmentation doesn't work with different Backgrounds HOT 2
- 오류 ㅠㅠ HOT 2
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