README.md

metadata

license: other
tags:
  - image-classification
  - text-to-image
  - diffusers
pretty_name: CatDataset1k
language:
  - en

CatDataset1k

Dataset of 1000 images of cats (domestic cats, Felis catus) for training models, experiments and fine-tuning (image generation, classification, etc.).

• Query: cat
• Caption / label for every image: cat
• Files: cat_0000.jpg ... cat_0999.jpg (JPEG)
• Sources: Wikimedia Commons + Flickr (via Openverse), open licenses

How to download / use

1. Load directly with the datasets library (recommended)

from datasets import load_dataset

ds = load_dataset("debugdll/DataCat1k")
# ds["train"][0]["image"]  -> PIL image
# ds["train"][0]["text"]   -> "cat"

No token required — the dataset is public. Total size ~ a few hundred MB.

Streaming (no full download, images loaded on demand):

ds = load_dataset("debugdll/DataCat1k", streaming=True)
row = next(iter(ds["train"]))

2. Download the files with the CLI

pip install huggingface_hub
huggingface-cli download debugdll/DataCat1k

3. Download with git

git clone https://huggingface.co/datasets/debugdll/DataCat1k

4. Download individual images (browser / direct link)

https://huggingface.co/datasets/debugdll/DataCat1k/resolve/main/cat_0000.jpg

Change the filename cat_0000.jpg in the link to get any other image.

Format

• metadata.csv — columns file_name,text
• The CSV + images use the standard Hugging Face imagefolder layout, so load_dataset("debugdll/DataCat1k") is inferred automatically.

Training usage (diffusers LoRA / text-to-image)

from datasets import load_dataset

ds = load_dataset("debugdll/DataCat1k", split="train")  # column: image, text="cat"

Notes

• Caption for every image: cat
• All images are public-domain / openly licensed photos