Weeks 9–10: Refinement, Documentation, and Usability

After spending the previous weeks building and testing an active learning pipeline (mostly inside Kaggle notebooks), I dedicated Weeks 9–10 to refinement and usability. This meant transforming rough experiments into something maintainable, reusable, and user-friendly.

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From Notebooks to Scripts

Up until now, most of my work lived inside Kaggle notebooks. While this was great for rapid prototyping, it wasn’t sustainable for integration with DeepForest or for other users to run easily. However I might include those notebooks as a part of documentation.

I migrated the pipeline into a proper Python module:

• Created a new file src/deepforest/active_learning.py.
• Encapsulated all the active learning logic inside a class-based structure.
• I also created a new empty file src/deepforest/label_studio.py.
• Replaced many independent code blocks with functions that rely on DeepForest’s existing functionality (model loading, training, evaluation).

This step made the code portable and aligned with DeepForest’s development style.

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Code Refinement

I focused heavily on maintainability and clarity:

• Refactoring: Broke down long functions into smaller, testable units.
• Error Handling: Added checks for missing files, empty datasets, or misconfigured parameters, with clear ValueError messages.
• Edge Cases: Covered scenarios like empty “uncertain” pools, mislabeled annotations, or zero predictions.

This significantly reduced “silent failures” and made debugging easier.

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Documentation and Examples

To make the module approachable, I wrote:

1. User-facing documentation describing each function, its arguments, and expected behavior.
2. Example Jupyter notebooks showing:
   • How to fine-tune RetinaNet50 on a custom dataset.
   • How to run an active learning loop with random vs. uncertainty sampling.
3. Walkthroughs explaining common pitfalls (dataset formatting, mAP evaluation, GPU constraints).

These examples not only help future users but also clarified my own understanding of the workflow.

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Reflection

These weeks felt like moving from hacky experiments to library features. Writing documentation and examples forced me to clarify assumptions, while refactoring made the code robust enough for others to build on.

Most importantly, transforming Kaggle notebooks into a maintainable active_learning.py script means the project is no longer just my experiments — it’s a reusable tool that fits into DeepForest’s ecosystem.

Next steps:

• Prepare final deliverables with polished documentation.
• Add visual summaries (e.g., uncertainty histograms, mAP curves).
• Plan for Label Studio integration in the coming phase.