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25 changes: 8 additions & 17 deletions docs/data-ai/train/capture-annotate-images.md
Original file line number Diff line number Diff line change
Expand Up @@ -286,17 +286,15 @@ You can either manually add annotations through the Viam web UI, or add annotati

## Annotate images

### Classify images with tags
You must annotate images in order to train an ML model on them.
Viam supports two ways to annotate an image:

Classification determines a descriptive tag or set of tags for an image.
For example, you could use classification to answer the following questions:
- Add tags to whole images (classification)
- Label bounding boxes around objects within images (object detection)

- does an image of a food display appear `full`, `empty`, or `average`?
- the quality of manufacturing output `good` or `bad`?
- what combination of toppings exists on a pizza: `pepperoni`, `sausage`, and `pepper`? or `pineapple`, `ham`, and `mushroom`?
### Add tags to an image

Viam supports single and multiple classification.
To create a training dataset for classification, annotate tags to describe your images.
Use tags to add metadata about an entire image, for example if the quality of a manufacturing output is `good` or `bad`.

{{< alert title="Tip" color="tip" >}}

Expand Down Expand Up @@ -509,16 +507,9 @@ await dataClient.addTagsToBinaryDataByIds(tags, myIds);

Once you've annotated your dataset, you can [train](/data-ai/train/train-tflite/) an ML model to make inferences.

### Detect objects with bounding boxes
### Label objects within an image

Object detection identifies and determines the location of certain objects in an image.
For example, object detection could help you identify:

- how many `pizza` objects appear on a counter
- the number of `bicycle` and `pedestrian` objects on a greenway
- which `plant` objects are popular with `deer` in your garden

To create a training set for object detection, annotate bounding boxes to teach your model to identify objects that you want to detect in future images.
Use labels to add metadata about objects within an image, for example by drawing bounding boxes around each bicycle in a street scene and adding the bicycle label.

{{< alert title="Tip" color="tip" >}}

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