# Inference for Classifications
Classification models label each sample (or frame) with a [`Classification`](../../api/fiftyone.core.labels.md#fiftyone.core.labels.Classification),
which you generate by passing a model to
[`apply_model()`](../../api/fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection.apply_model).
With a zoo model, this includes zero-shot classifiers like CLIP, which
require no training data at all.
For a custom model, you can either construct [`Classification`](../../api/fiftyone.core.labels.md#fiftyone.core.labels.Classification) instances
yourself in a loop, or configure a
[`TorchImageModel`](../../api/fiftyone.utils.torch.md#fiftyone.utils.torch.TorchImageModel) with a
[`ClassifierOutputProcessor`](../../api/fiftyone.utils.torch.md#fiftyone.utils.torch.ClassifierOutputProcessor)
so it works with `apply_model()` directly, as described in
[Inference with custom models](index.md#running-inference-custom).