Comments (4)
@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!
from ultralytics.
Hi @MosbehBarhoumi! 👋
Thanks for your feedback! If you're looking for further efficiency, you might consider automating parts of the pseudo-labeling process using confidence thresholds to automatically accept high-confidence predictions. This can reduce manual verification work. Here's a quick snippet on how you might filter these predictions:
high_confidence_results = [result for result in results if result.confidence > 0.9] # adjust threshold as needed
This approach can help streamline the process, allowing you to focus on reviewing and correcting only the lower-confidence or more ambiguous cases. Let us know how it goes!
from ultralytics.
👋 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.
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.
@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.
from ultralytics.
Related Issues (20)
- Tensors misshaped when loading yolo8X.engine files HOT 5
- fReE** MoNoPoLy gO DiCe gEnErAtOr 2024 FrEe uNlImItEd dIcE RoLlS HOT 2
- FrEe@~ cAsH ApP MoNeY GeNeRaToR 2024-2025_GeT FrEe cAsH ApP CoDeS_No sUrVeY [srt+w] HOT 2
- ##(mOnOpOlY Go)** UnLiMiTeD DiCe rOlLs aNd mOnEy gEnErAtOr cHeAtS 2024 (FrEsH StRaTeGy) HOT 2
- nEW.eDITION@!~ mONOPOLY go dICE GENERATOR 2024-2025- nO hUMAN vERIFICATION HOT 2
- GeT@~! mOnOpOlY Go dIcE GeNeRaToR LiNkS WoRkInG (2024-2025) No vErIfIcAtIoN [uD5M] HOT 2
- (MoNoPoLy.gO)!** GeNeRaToR FrEe dIcE RoLlS AnD MoNeY 2024-2025_No vErIfIcAtIoN (aNdRoId iOs mOd) HOT 2
- wOrKiNg@~EdItIoN_$750 cAsH ApP MoNeY - FrEe cAsH ApP MoNeY GeNeRaToR 2024-2025 WiTh pErFeCt rEvIeW [ser5] HOT 2
- (ToDaY'S.UpDaTe)!~ UlTiMaTe fReE CaSh aPp mOnEy gEnErAtOr-2024-2025_[WiThOuT-HuMaN-VeRiFiCaTiOn] [huw] HOT 2
- gEt**[nEw-cOdEs~]~@ fReE CaSh aPp mOnEy gEnErAtOr 2024-2025 cAsH-ApP-CoDeS-GeNeRaToR [d+e4] HOT 2
- fReE.EaRnInG** GeT FrEe $750 CaSh aPp mOnEy gEnErAtOr 2024-2025~ WiThOuT HuMaN VeRiFiCaTiOn [df69] HOT 2
- (nEw.uPdAtEd)@~ FrEe mOnOpOlY Go dIcE GeNeRaToR 2024-2025 GeT FrEe nOw HOT 2
- @![FrEe-uNlImItEd]!~ GeT MoNoPoLy gO DiCe gEnErAtOr 2024-2025_fReE UnLiMiTeD MoNoPoLy gO DiCe [Jr6l] HOT 2
- mOnOpOlY FrEe dIcE GeNeRaToR 2024_uNlImItEd rOlLs oN OuR FrEe dIcE GeNeRaToR HOT 2
- HoW To gEt@!~ FrEe dIcE On mOnOpOlY Go dIcE GeNeRaToR 2024-2025_KeEp rOlLiNg HOT 2
- wOrKiNg@~ mOnOpOlY Go dIcE GeNeRaToR 2024-2025- nO HuMaN VeRiFiCaTiOn HOT 2
- OBB task do not support `save_crop` HOT 6
- Cache is on, but param cache=False (by default) HOT 6
- How to improve and modify the ZH documentation HOT 1
- Unexplained behavior of the Validation of a Classification model HOT 4
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from ultralytics.