weecology / weecology/DeepForest
Stronger CI for maintaining model consistency
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 774
- Forks
- 265
- PR merge metrics
- No merged PRs in 30d
Description
The unit tests largely ensure that the modules run and create reasonable results. They are sensitive to silent failures in model performance. For example, I could write a poor pull request that only includes a portion of the predictions and the current tests wouldn't be able to spot that. We need both visual cues that can be visualized for changes as well as better understanding on expected results given a particular image.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reviewing the existing unit tests and how they validate model predictions for a particular image. Define the expected results and visual cues needed to detect incomplete or degraded predictions, then use those criteria to determine what stronger CI coverage would consider done.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning, testing-qa
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100