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cholpona/pizza-burger-sushi-recipe

pizza-burger-sushi (recipe-only) This repo contains no image data. It contains a single script (recreate.py) and instructions for rebuilding the merged 3-class dataset used to fine-tune cholpona/vit-base-patch16-224-pizza-burger-sushi. The underlying images are not redistributed here because the burger source dataset does not specify a license. Reproducing the data locally from the original sources respects each upstream's terms. What it produces… See the full description on the dataset page: https://huggingface.co/datasets/cholpona/pizza-burger-sushi-recipe.

sourceHugging Faceotherupdated 4mo agoView on Hugging Face
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pizza-burger-sushi (recipe-only)

This repo contains no image data. It contains a single script (recreate.py) and instructions for rebuilding the merged 3-class dataset used to fine-tune `cholpona/vit-base-patch16-224-pizza-burger-sushi`.

The underlying images are not redistributed here because the burger source dataset does not specify a license. Reproducing the data locally from the original sources respects each upstream's terms.

What it produces

<out>/pizza_burger_sushi/data/
  train/{pizza,burger,sushi}/    ~150 images per class  (454 total)
  test/{pizza,burger,sushi}/     46 images per class    (138 total)

Source datasets

SourceProvidesLicense
`Shad0wKillar/pizza_steak_sushi`pizza + sushi (already split into train/test)MIT
`NexaAIalex/Burger`burger (resized + sampled)unspecified — see source dataset card

How to reproduce

bash
pip install huggingface_hub Pillow
python recreate.py --out ./data

Then point your training code at ./data/pizza_burger_sushi/data/{train,test}/<class>/.

The recipe

  1. 1.Download Shad0wKillar/pizza_steak_sushi (already has data/{train,test}/{pizza,steak,sushi}/).
  2. 2.Download NexaAIalex/Burger.
  3. 3.Resize burger images: max side 384 px, RGB, JPEG quality 90.
  4. 4.Shuffle the resized burger images with random.Random(42), take the first 150 for train/, the next 46 for test/.
  5. 5.Assemble the final layout pizza_burger_sushi/data/{train,test}/{pizza,burger,sushi}/, dropping the steak class from the source.

recreate.py does all of the above in one shot and is fully deterministic given SEED=42. It is also idempotent — re-running skips files that already exist.

License

  • —This recipe (script + README): MIT.
  • —The source datasets retain their own licenses (see table above). This repo does not redistribute their data.