license: mit
task_categories:
- image-to-text
- text-to-image
language:
- en
pretty_name: simons ARC (abstraction & reasoning corpus) image version 36
size_categories:
- 10K<n<100K
configs:
- config_name: default
data_files:
- split: train
path: data.jsonl
Version 1
Have dataset items that are somewhat evenly of each type. The LLM learned some of the types fine. However rotated images are causing problems. The image sizes are between 1 and 10 pixels.
Version 2
Here the majority of dataset items are rotated images. Since this is what my LLM is struggling with. Smaller images. Here the image sizes are between 1 and 5 pixels. This helped a lot on the validation loss.
Version 3
Main focus is now on count_same_color_as_center_with_8neighbors_nowrap and image size 1-6.
Which the LLM has struggeld with in the past, maybe due to too big image sizes.
Struggles somewhat with the count_same_color_as_center_with_8neighbors_nowrap.
Version 4
I'm trying smaller images again. Here the image sizes are between 1 and 5 pixels.
Added same_color_inside_3x3_area_nowrap that checks if all surrounding pixels agree on the same color,
maybe that have some synergy with the count_same_color_as_center_with_8neighbors_nowrap.
It helped a little, but it's still not as good at counting neighbors as I would like.
Version 5
I have added a pixels_with_k_matching_neighbors with a k parameter between 1-8.
This may help improve on counting the number of neighboring pixels.
The image size 1-6.
This did indeed help on counting the number of surrounding pixels.
Version 6
Same weight to all the transformations. Image size 1-11.
Version 7
Focus on histogram and k-nearest neighbors. image size 1-12. It seems like the LLM has gotten the hang of it.
Version 8
Focus on histogram and k-nearest neighbors. image size 5-20.
Version 9
Focus on histogram and k-nearest neighbors. image size 10-30.
Version 10
Same weight to all the transformations. image width 10-30. image height 2-5.
Version 11
Same weight to all the transformations. image width 2-5. image height 10-30.
Version 12
Focus on k-nearest neighbors. image width 2-5. image height 10-30.
Version 13
Focus on compres_x, compres_y, compres_xy.
image size is 1-10.
Version 14
Focus on histograms and k-nearest-neighbors. image size 5-20.
Version 15
Focus on histograms and k-nearest-neighbors. image size 10-30.
Version 16
Focus on k-nearest-neighbors. image size 10-25.
Version 17
Disabled k-nearest-neighbors, I suspect this is the reason why it converges so slowly. image size 15-30.
Version 18
Disabled k-nearest-neighbors, and compression. image size 15-25.
Version 19
Translate x/y by plus/minus 1. Disabled rotation and transpose. image size 22-30.
Version 20
Focus on k-nearest-neighbors. image size 5-15.
Version 21
Focus on k-nearest-neighbors. image size 8-18.
Version 22
Same weight to all the transformations. image size 8-20.
Version 23
Same weight to all the transformations. image size 5-30.
The LLM is struggling learning this. I'm going to try with small images.
Version 24
Focus on rotate cw, rotate ccw, transpose. image size 2-10.
The LLM is struggling learning this. Despite being small images. I'm going to try with even small images.
Version 25
Focus on rotate cw, rotate ccw, transpose. image size 2-5.
The LLM is struggling learning this. Despite being small images. I'm going to try with even small images.
Version 26
Focus on rotate cw, rotate ccw, transpose, k-nearest-neighbors. image size 1-3.
The LLM is struggling learning this. Despite being small images.
Version 27
Focus on rotate cw, rotate ccw, transpose. image size 1-4.
The LLM is struggling learning this. Despite being small images.
Version 28
Focus on rotate cw, rotate ccw, transpose. image size 1-5.
Version 29
Focus on rotate cw, rotate ccw, transpose. image size 1-6.
Version 30
Focus on rotate cw, rotate ccw, transpose. image size 1-8.
Version 31
Focus on rotate cw, rotate ccw, transpose. image size 1-10.
Version 32
Focus on rotate cw, rotate ccw, transpose. image size 1-12.
Version 33
Focus on rotate cw, rotate ccw, transpose.
image size 1-14.
The serialize items were using fewer names to identify the dataset, now uses the same names as deserialize.
Version 34
Focus only on rotate cw. All other operations have been disabled.
image size 1-30.
Version 35
Focus only on rotate ccw. All other operations have been disabled.
image size 1-30.
Version 36
Focus only on rotate cw and rotate ccw. All other operations have been disabled.
image size 1-30.