confidence factor in detected result
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 25/100
- Issue type
- Feature
- Clarity
- Needs clarification
- Activity status
- Stale
- Domain
- api, computer-vision
Research direction
Start by reviewing the shape-detection API proposal and the detected-result definition, then trace how face detections are represented. Determine whether a confidence value can be specified consistently for the two described use cases; done means the proposal has an agreed result shape and behavior for exposing that information.
Written by the indexing model from the issue text.
Description
I havn't found any way to estimate the accuracy of the result provided by the API. It may be interesting for the application to have a confidence factor from the underlying technology.
the browser tell me there is a face here, but is it sure at 50% or 99% ? this may be useful for the application to know this information.
usecase 1: for example the app previously detected a face close to this location in a previous frame, so if the new detection is 50%, it is good enougth.
usecase 2: the application is doing a initial detection wihtout previous knowledge. So the API has to be real sure. So, for example, the app would require a 90% confidence.
- Dominant language
- Bikeshed
- Stars
- 318
- Forks
- 39
- PR merge metrics
- No merged PRs in 30d
Getting set up
- No Dockerfile or Docker Compose file
- No pull request template
- Read the contributing 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.
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