ja/yolov5/tutorials/model_ensembling/

[giscus[bot]]

ja/yolov5/tutorials/model_ensembling/

より正確な予測のためにmAPとRecallを向上させるために、テストと推論中にYOLOv5 モデルアンサンブルを使用する方法を学びます。

https://docs.ultralytics.com/ja/yolov5/tutorials/model_ensembling/

[UltralyticsAssistant]

👋 Hello, thank you for your interest in Ultralytics 🚀! We recommend exploring the Docs for an extensive range of resources, including this specific Model Ensembling Tutorial in Japanese. This guide is a great place to learn how to use ensemble methods during testing and inference to improve mAP and Recall 📈.

If this is a 🐛 Bug Report or you experience any issues following the tutorial, please provide a minimum reproducible example to help us debug effectively. For custom ❓ Questions, sharing your use case details, logs, and any related dataset examples will assist in providing the best guidance.

Join the Ultralytics community where you feel most comfortable! For real-time support, check out our Discord 🎧. Prefer detailed discussions? Visit Discourse. Or join our Subreddit to connect with like-minded users.

Upgrade

Verify you are using the latest version of the ultralytics package by upgrading it alongside all requirements in a Python>=3.8 environment with PyTorch>=1.8. Run:

pip install -U ultralytics

Environments

For a seamless experience, YOLO can be run within up-to-date, verified environments with all dependencies preinstalled (CUDA, Python, PyTorch, etc.). Try any of these:

• 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

You can check the latest CI status here: . A green badge indicates that all Ultralytics CI tests are passing across macOS, Windows, and Ubuntu.

This is an automated response 🤖, but rest assured that an Ultralytics engineer will assist you soon! 😊