Document Details
Tutorial Deploy Custom Vision
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This page contains Windows bias
About This Page
This page is part of the Azure documentation. It contains code examples and configuration instructions for working with Azure services.
Bias Analysis
Bias Types:
⚠️
windows_first
⚠️
windows_tools
⚠️
missing_linux_example
Summary:
The documentation shows mild Windows bias. While the main tutorial flow is Linux-focused (Linux containers, Linux device quickstart), there are several signs of Windows bias: (1) The sample images are sourced from a repository named 'Cognitive-CustomVision-Windows', with no mention of a Linux equivalent or alternative; (2) The device setup section lists 'Linux device' before 'Windows device', but only provides a link to a Windows quickstart as an alternative, not as a parallel path; (3) There are no explicit PowerShell or Windows command-line examples, but the documentation does not provide any Linux-specific troubleshooting or alternative flows for common Windows-only issues (e.g., file paths, permissions); (4) The use of Visual Studio Code and Azure IoT Edge Dev Tool is cross-platform, but the documentation does not clarify any Linux-specific nuances or provide Linux-first examples for all steps.
Recommendations:
- Provide sample images from a neutral or Linux-named repository, or clarify that the 'Cognitive-CustomVision-Windows' repo is cross-platform.
- Include explicit Linux and Windows command-line examples where relevant, especially for file paths and Docker commands.
- Add troubleshooting notes for common Linux-specific issues (e.g., permissions, Docker group membership, file system case sensitivity).
- Ensure that all tool references (e.g., Visual Studio Code, Docker) clarify cross-platform compatibility and provide links to both Linux and Windows installation guides.
- Where device setup is discussed, offer parallel quickstart links and instructions for both Linux and Windows, rather than listing Windows as an alternative.
- If referencing Windows tools or repositories, provide Linux equivalents or clarify their cross-platform usage.
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| 2025-08-27 00:01 | #93 | in_progress | ✅ Clean |
| 2025-08-22 00:01 | #88 | completed | ✅ Clean |
| 2025-08-17 00:01 | #83 | in_progress | ✅ Clean |
| 2025-07-13 21:37 | #48 | completed | ❌ Biased |
| 2025-07-12 23:44 | #41 | in_progress | ❌ Biased |
| 2025-07-09 13:09 | #3 | cancelled | ✅ Clean |
| 2025-07-08 04:23 | #2 | cancelled | ❌ Biased |
Flagged Code Snippets
7. Save the **requirements.txt** file.
### Add a test image to the container
Instead of using a real camera to provide an image feed for this scenario, we're going to use a single test image. A test image is included in the GitHub repo that you downloaded for the training images earlier in this tutorial.
1. Navigate to the test image, located at **Cognitive-CustomVision-Windows** / **Samples** / **Images** / **Test**.
2. Copy **test_image.jpg**
3. Browse to your IoT Edge solution directory and paste the test image in the **modules** / **cameracapture** folder. The image should be in the same folder as the main.py file that you edited in the previous section.
4. In Visual Studio Code, open the **Dockerfile.amd64** file for the cameracapture module.
5. After the line that establishes the working directory, `WORKDIR /app`, add the following line of code: