task_categories:image-to-text task_categories:text-to-image language:en license:mit size_categories:1K<n<10K format:json modality:text library:datasets library:pandas library:mlcroissant library:polars region:us
---
license: mit
task_categories:
- image-to-text
- text-to-image
language:
- en
pretty_name: simons ARC (abstraction & reasoning corpus) solve rotate version 12
size_categories:
- 1K<n<10K
configs:
- config_name: default
  data_files:
  - split: train
    path: data.jsonl
---

# Version 1

ARC-AGI Tasks where the image gets rotated cw/ccw/180 and transposed.

The image sizes are between 1 and 4 pixels.

Predict the number of rows in the output image.

# Version 2

image size: 1-5.

# Version 3

image size: 1-5.

Added `flipx` and `flipy` transformations.

# Version 4

image size: 1-5.

number of tests: 1-2. Previously there were always just 1 test.

Added `flipa` and `flipb` transformations, that flips over the diagonal.

# Version 5

image size: 1-5.

number of tests: 1-2.

# Version 6

image size: 1-13.

# Version 7

Earlier predictions added to some of the rows.

# Version 8

Earlier predictions with focus on repair 1 bad pixel.

# Version 9

Added fields: `arc_task`, `test_index`, `earlier_output`.

# Version 10

Replaced RLE compressed response with raw pixel response.

# Version 11

image size: 1-16.

# Version 12

I think the image sizes was too big for the model to make sense of the data. Trying with smaller images.

image size: 1-5.
