---
license: mit
tags:
- dataset
- architecture
- image-depth
---

# Architecture Image Depth Data Notes

## Dataset summary

This repository contains a preparation pipeline and a small metadata sample for **Architecture** work with **Image Depth** inputs. It does not claim to be a complete benchmark release; the loader documents how source data is normalized and validated.

## Included material

- `dataloader.py` — loading, cleaning, and split preparation code.
- `dataset_infos.json` — schema and split metadata.
- `metadata_sample.jsonl` — small, human-readable records for checking the schema.
- `README.md` — data card and usage notes.

## Processing choices

| Stage | Setting |
|---|---|
| Storage format | tfrecord |
| Preprocessing | aggressive |
| Augmentation | autoaugment |
| Split strategy | temporal |
| Sampling | stratified |
| Quality checks | strict |
| Labeling | pseudo label |

## Validation checklist

Before using the prepared data, verify source licenses, duplicates across splits, missing values, label balance, and modality-specific corruption. Record the source version and every filtering rule so a later run can reproduce the same rows.

## Intended use

The repository is suitable for testing the data pipeline, adapting it to a documented source, and preparing controlled research splits. Release status: **metadata sample; full source data not bundled**. The sample is for schema inspection only and should not be reported as a full training corpus.

## Risks and limitations

The loader cannot guarantee that an external source is representative, correctly licensed, or free of sensitive information. Users remain responsible for source review, privacy checks, and bias analysis before training or redistribution.

## Files

- `dataloader.py` — primary artifact
- `README.md` — this documentation
- `dataset_infos.json` — schema metadata
- `metadata_sample.jsonl` — schema sample

## License

Released under **mit**. Review the source-data terms separately when this repository is used with external datasets.