# Inference for Keypoints
Pose and landmark models label each sample with a [`Keypoints`](../../api/fiftyone.core.labels.md#fiftyone.core.labels.Keypoints) field, which
you generate by passing a model to
[`apply_model()`](../../api/fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection.apply_model).
The [Ultralytics integration](../../integrations/ultralytics.md#ultralytics-integration) shows this in
practice: a YOLO pose model can be passed directly to `apply_model()` with no
wrapping required, producing keypoints that render with FiftyOne’s built-in
skeleton support.
For a fully custom model, configure a
[`TorchImageModel`](../../api/fiftyone.utils.torch.md#fiftyone.utils.torch.TorchImageModel) with a
[`KeypointDetectorOutputProcessor`](../../api/fiftyone.utils.torch.md#fiftyone.utils.torch.KeypointDetectorOutputProcessor)
so it works with `apply_model()` directly, as described in
[Inference with custom models](index.md#running-inference-custom).