task_categories:text-to-image language:en license:cc-by-4.0 size_categories:n<1K format:parquet modality:image modality:text library:datasets library:pandas library:mlcroissant library:polars region:us climate
---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': broadleaved_indigenous_hardwood
          '1': deciduous_hardwood
          '2': grose_broom
          '3': harvested_forest
          '4': herbaceous_freshwater_vege
          '5': high_producing_grassland
          '6': indigenous_forest
          '7': lake_pond
          '8': low_producing_grassland
          '9': manuka_kanuka
          '10': shortrotation_cropland
          '11': urban_build_up
          '12': urban_parkland
  - name: caption
    dtype: string
  - name: token
    dtype: string
  splits:
  - name: train
    num_bytes: 21868272.0
    num_examples: 260
  download_size: 21854979
  dataset_size: 21868272.0
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
task_categories:
- text-to-image
language:
- en
tags:
- climate
pretty_name: Waikato Aerial 2017 Sample with Blip Captions
size_categories:
- n<1K
---

This is a re-upload of a random sample of 260 images + curresponding blip captions obtained from the original `waikato_aerial_imagery_2017` classification dataset residing at https://datasets.cms.waikato.ac.nz/taiao/waikato_aerial_imagery_2017/ under the same license. You can find additional dataset information using the provided URL.  

The BLIP model used for captioning: [Salesforce/blip-image-captioning-large](https://huggingface.co/Salesforce/blip-image-captioning-large)  

The images belong to 13 unique categories and each caption contains a unique token for each class. These are useful for fine-tuning `text-to-image` models like Stable Diffusion.