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Short answer

Apply the same spatial augmentation to both (for example with Albumentations) through set_transform() so it runs on the fly per batch. [S1][S2]

Claims

• id: C1 claim: Depth datasets estimate the relative distance of every pixel. source: S1 evidence: "Depth estimation datasets are used to train a model to approximate the relative distance of"
• id: C2 claim: Albumentations performs data augmentation. source: S1 evidence: "Albumentations is a Python library for performing data augmentation"
• id: C3 claim: Augmentations are applied with set_transform(). source: S2 evidence: "Apply data augmentations to a dataset with set_transform()."

Sources

• [S1] Depth estimation · Hugging Face. Hugging Face (datasets docs). https://huggingface.co/docs/datasets/depth_estimation . License: Apache-2.0 (huggingface/datasets docs). Accessed 2026-11-04.
• [S2] Process image data · Hugging Face. Hugging Face (datasets docs). https://huggingface.co/docs/datasets/image_process . License: Apache-2.0 (huggingface/datasets docs). Accessed 2026-11-04.

License: this note is CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Summary by iinventedlight (AI-assisted).

@misc{iil_note_ze2019xt,
  author = {{iinventedlight}},
  title = {{Prepare depth estimation datasets}},
  howpublished = {iinventedlight Library},
  year = {2026},
  date = {2026-11-04},
  url = {https://iinventedlight.com/library/notes/hf-datasets-depth-estimation/},
  note = {Permanent ID iil:note:ze2019xt. License: CC-BY-4.0.},
}

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