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Code Issues Actions 2 Packages Projects Releases Wiki Activity

Implement Wizard for Creating Classification Models (#20622)

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* Implement extraction of images for classification state models

* Add object classification dataset preparation

* Add first step wizard

* Update i18n

* Add state classification image selection step

* Improve box handling

* Add object selector

* Improve object cropping implementation

* Fix state classification selection

* Finalize training and image selection step

* Cleanup

* Design optimizations

* Cleanup mobile styling

* Update no models screen

* Cleanups and fixes

* Fix bugs

* Improve model training and creation process

* Cleanup

* Dynamically add metrics for new model

* Add loading when hitting continue

* Improve image selection mechanism

* Remove unused translation keys

* Adjust wording

* Add retry button for image generation

* Make no models view more specific

* Adjust plus icon

* Adjust form label

* Start with correct type selected

* Cleanup sizing and more font colors

* Small tweaks

* Add tips and more info

* Cleanup dialog sizing

* Add cursor rule for frontend

* Cleanup

* remove underline

* Lazy loading
This commit is contained in:
Nicolas Mowen
2025-10-23 13:27:28 -06:00
committed by GitHub
parent 4df7793587
commit f5a57edcc9
18 changed files with 2450 additions and 79 deletions
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docs/docs/configuration/custom_classification/object_classification.md
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@@ -12,7 +12,18 @@ Object classification models are lightweight and run very fast on CPU. Inference
Training the model does briefly use a high amount of system resources for about 1–3 minutes per training run. On lower-power devices, training may take longer.
When running the `-tensorrt` image, Nvidia GPUs will automatically be used to accelerate training.
### Sub label vs Attribute
## Classes
Classes are the categories your model will learn to distinguish between. Each class represents a distinct visual category that the model will predict.
For object classification:
- Define classes that represent different types or attributes of the detected object
- Examples: For `person` objects, classes might be `delivery_person`, `resident`, `stranger`
- Include a `none` class for objects that don't fit any specific category
- Keep classes visually distinct to improve accuracy
### Classification Type
- **Sub label**: