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78 lines
3.6 KiB
78 lines
3.6 KiB
--- |
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comments: true |
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description: Learn how to create minimum reproducible examples (MRE) for efficient bug reporting in Ultralytics YOLO repositories with this step-by-step guide. |
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keywords: Ultralytics, YOLO, minimum reproducible example, MRE, bug reports, guide, dependencies, code, troubleshooting |
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--- |
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# Creating a Minimum Reproducible Example for Bug Reports in Ultralytics YOLO Repositories |
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When submitting a bug report for Ultralytics YOLO repositories, it's essential to provide a [minimum reproducible example](https://docs.ultralytics.com/help/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. |
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## 1. Isolate the Problem |
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The first step in creating an MRE is to isolate the problem. This means removing 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 remove any irrelevant code. |
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## 2. Use Public Models and Datasets |
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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. |
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## 3. Include All Necessary Dependencies |
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Make sure to include all the necessary dependencies in your MRE. If your code relies on external libraries, specify the required packages and their versions. Ideally, provide a `requirements.txt` file or list the dependencies in your bug report. |
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## 4. Write a Clear Description of the Issue |
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Provide a clear and concise description of the issue you're experiencing. Explain the expected behavior and the actual behavior you're encountering. If applicable, include any relevant error messages or logs. |
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## 5. Format Your Code Properly |
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When submitting an MRE, 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: |
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<pre> |
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```python |
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# Your Python code goes here |
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``` |
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</pre> |
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## 6. Test Your MRE |
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Before submitting your MRE, test it to ensure that it accurately reproduces the issue. Make sure that others can run your example without any issues or modifications. |
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## Example of an MRE |
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Here's an example of an MRE for a hypothetical bug report: |
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**Bug description:** |
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When running the `detect.py` script on the sample image from the 'coco8.yaml' dataset, I get an error related to the dimensions of the input tensor. |
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**MRE:** |
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```python |
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import torch |
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from ultralytics import YOLO |
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# Load the model |
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model = YOLO("yolov8n.pt") |
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# Load a 0-channel image |
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image = torch.rand(1, 0, 640, 640) |
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# Run the model |
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results = model(image) |
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``` |
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**Error message:** |
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``` |
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RuntimeError: Expected input[1, 0, 640, 640] to have 3 channels, but got 0 channels instead |
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``` |
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**Dependencies:** |
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- torch==2.0.0 |
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- ultralytics==8.0.90 |
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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. |
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By following these guidelines, you'll help the maintainers and contributors of Ultralytics YOLO repositories to understand and resolve your issue more efficiently.
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