it/modes/train/

[giscus[bot]]

it/modes/train/

Scopri come addestrare in modo efficiente i modelli di rilevamento degli oggetti utilizzando YOLO11 con istruzioni complete sulle impostazioni, l'incremento e l'utilizzo dell'hardware.

https://docs.ultralytics.com/it/modes/train/

[davidetedd9203]

is it possible to train yolo on temporal data? stacking more frames together?

[glenn-jocher]

@davidetedd9203 yes, training YOLO on temporal data by stacking frames is possible. You can treat the stacked frames, combined into a single input tensor, as channels or additional dimensions for training. Modifications to the data loading and possibly the model architecture may be necessary to accommodate this input format. For advanced use cases, you might explore integration with recurrent structures like LSTMs for temporal dependencies. Let me know if you'd like further guidance on this!

[UltralyticsAssistant]

👋 Hello, thank you for your interest in Ultralytics 🚀! It's great to see you're diving into training object detection models using YOLO11. We suggest checking out the Documentation where you'll find comprehensive guidance on various modes and tasks, including training instructions.

If this is a 🐛 Bug Report, please include a minimum reproducible example to assist us in diagnosing the issue.

For custom training inquiries ❓, please share detailed information, including dataset samples, training logs, and confirm that you're following our Tips for Best Training Results.

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Upgrade

Ensure you have the latest ultralytics package version—with all requirements—in a Python>=3.8 environment with PyTorch>=1.8:

pip install -U ultralytics

Environments

You can run YOLO in any of these up-to-date verified environments (pre-installed with all dependencies, including CUDA/CUDNN, Python, and PyTorch):

• 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 passing. CI tests check the functionality of all YOLO Modes and Tasks on macOS, Windows, and Ubuntu every 24 hours and after every commit.

This is an automated response. An Ultralytics engineer will also assist you soon! 😊

[davidetedd9203]

it would be awesome if you could provide some guides on it! thanks a lot Il giorno dom 24 nov 2024 alle ore 02:44 Glenn Jocher < ***@***.***> ha scritto:

…

@davidetedd9203 <https://github.com/davidetedd9203> yes, training YOLO on temporal data by stacking frames is possible. You can treat the stacked frames, combined into a single input tensor, as channels or additional dimensions for training. Modifications to the data loading and possibly the model architecture may be necessary to accommodate this input format. For advanced use cases, you might explore integration with recurrent structures like LSTMs for temporal dependencies. Let me know if you'd like further guidance on this! — Reply to this email directly, view it on GitHub <#17698 (reply in thread)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ARILVQASDD5MFBE6FHDPB532CEVOPAVCNFSM6AAAAABSHGIS2SVHI2DSMVQWIX3LMV43URDJONRXK43TNFXW4Q3PNVWWK3TUHMYTCMZWGA2DSNQ> . You are receiving this because you were mentioned.Message ID: ***@***.*** com>

[glenn-jocher]

@davidetedd9203 thank you for the suggestion! A comprehensive training guide is already available for YOLO11 at YOLO11 Training Documentation. It includes detailed instructions, use-case examples, and advanced training techniques. Let me know if you have specific needs or additional questions!