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# Use cases

Each page here describes one job: what the flow looks like, which nodes do the work, how to set it up for your data and how teams usually extend it. Every one of them starts from a template in the library, and most have a live example you can open and a case study with real numbers.

#### [Bring data into OvalBee](/use-cases/bring-data-in)

Upload files, pull a public dataset from Dataset Ninja, or import a Supervisely project.

#### [Organize labeling](/use-cases/organize-labeling)

Labeling with review, expert escalation, automated quality checks and rework loops.

#### [Check and clean a dataset](/use-cases/check-and-clean-data)

Profile a dataset, find label errors, duplicates and outliers, and fix them with rules.

#### [Try a pretrained detector](/use-cases/try-a-pretrained-detector)

See what an off-the-shelf model finds on your images before you label anything.

#### [Train a detector](/use-cases/train-a-detector)

Split a labeled dataset and fine-tune YOLO, DEIM or EdgeCrafter on it.

#### [Evaluate and compare models](/use-cases/evaluate-and-compare)

Score a model against ground truth and pick the best of several.

## Templates at a glance

Open the library with **Start from template** on the Home page, or **Templates** in the flow toolbar.

| Template                             | Use case                                                                  |
| ------------------------------------ | ------------------------------------------------------------------------- |
| Import images by drag & drop         | [Bring data in](/use-cases/bring-data-in)                                 |
| Import from Dataset Ninja            | [Bring data in](/use-cases/bring-data-in)                                 |
| Import from Supervisely              | [Bring data in](/use-cases/bring-data-in)                                 |
| Labeling with review loop            | [Organize labeling](/use-cases/organize-labeling)                         |
| Labeling with expert                 | [Organize labeling](/use-cases/organize-labeling)                         |
| Labeling with auto quality check     | [Organize labeling](/use-cases/organize-labeling)                         |
| Supervisely queue labeling           | [Organize labeling](/use-cases/organize-labeling#labeling-in-supervisely) |
| Labeling jobs in Supervisely         | [Organize labeling](/use-cases/organize-labeling#labeling-in-supervisely) |
| Dataset quality control              | [Check and clean a dataset](/use-cases/check-and-clean-data)              |
| Filter-based data cleanup            | [Check and clean a dataset](/use-cases/check-and-clean-data)              |
| Run EdgeCrafter Detector on Images   | [Try a pretrained detector](/use-cases/try-a-pretrained-detector)         |
| Train EdgeCrafter Detector on Images | [Train a detector](/use-cases/train-a-detector)                           |
| Detector evaluation                  | [Evaluate and compare models](/use-cases/evaluate-and-compare)            |
| Detector comparison                  | [Evaluate and compare models](/use-cases/evaluate-and-compare)            |

## Combining them

The use cases are building blocks of one larger process. Imported data feeds a labeling flow, its accepted dataset feeds training, the trained model is evaluated and compared, and its predictions become the starting point for the next round of labeling. Because datasets are versioned and references can follow the latest version, you can connect these pieces in one flow and let each part pick up the newest results of the one before it.

```mermaid
flowchart LR
  bring["Bring data in"] --> label["Organize labeling"] --> clean["Check and clean"] --> train["Train"] --> eval["Evaluate and compare"]
  eval -. "hard cases" .-> label
```

Not sure where to start? Describe your goal to the [AI agent](/build/agent) — it searches the Node Library and assembles a first version of the flow for you.