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Ultralytics YOLO リポジトリにおけるバグ報告のための最小限の再現可能な例の作成

When submitting a bug report for Ultralytics YOLO repositories, it's essential to provide a Minimum Reproducible Example (MRE). An MRE is a small, self-contained piece of code that demonstrates the problem you're experiencing. Providing an MRE helps maintainers and contributors understand the issue and work on a fix more efficiently. This guide explains how to create an MRE when submitting bug reports to Ultralytics YOLO repositories.

1.問題の切り分け

The first step in creating an MRE is to isolate the problem. Remove any unnecessary code or dependencies that are not directly related to the issue. Focus on the specific part of the code that is causing the problem and eliminate any irrelevant sections.

2.公開モデルとデータセットを利用する

When creating an MRE, use publicly available models and datasets to reproduce the issue. For example, use the yolov8n.pt model and the coco8.yaml dataset. This ensures that the maintainers and contributors can easily run your example and investigate the problem without needing access to proprietary data or custom models.

3.必要な依存関係をすべて含める

Ensure all necessary dependencies are included in your MRE. If your code relies on external libraries, specify the required packages and their versions. Ideally, list the dependencies in your bug report using yolo checks if you have ultralytics installed or pip list for other tools.

4.問題の明確な説明を書く

あなたが経験している問題を明確かつ簡潔に説明してください。期待される動作と実際に発生した動作を説明してください。該当する場合は、関連するエラーメッセージやログも含めてください。

5.コードを正しくフォーマットする

Format your code properly using code blocks in the issue description. This makes it easier for others to read and understand your code. In GitHub, you can create a code block by wrapping your code with triple backticks (```) and specifying the language:

```python
# Your Python code goes here
```

6.MREのテスト

MREを提出する前に、問題が正確に再現されているかテストしてください。他の人が問題や修正なしにあなたのサンプルを実行できることを確認してください。

MREの例

以下は、仮定のバグレポートのMREの例です:

バグの説明

When running inference on a 0-channel image, I get an error related to the dimensions of the input tensor.

MRE:

import torch

from ultralytics import YOLO

# Load the model
model = YOLO("yolov8n.pt")

# Load a 0-channel image
image = torch.rand(1, 0, 640, 640)

# Run the model
results = model(image)

エラーメッセージ:

RuntimeError: Expected input[1, 0, 640, 640] to have 3 channels, but got 0 channels instead

依存関係:

  • torch==2.3.0
  • ultralytics==8.2.0

In this example, the MRE demonstrates the issue with a minimal amount of code, uses a public model ("yolov8n.pt"), includes all necessary dependencies, and provides a clear description of the problem along with the error message.

By following these guidelines, you'll help the maintainers and contributors of Ultralytics YOLO repositories to understand and resolve your issue more efficiently.



Created 2023-11-12, Updated 2024-06-09
Authors: IvorZhu331 (1), glenn-jocher (3)

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