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---
license: mit
task_categories:
- image-to-text
- text-to-image
language:
- en
pretty_name: simons ARC (abstraction & reasoning corpus) lab imagepair version 14
size_categories:
- 10K<n<100K
configs:
- config_name: default
data_files:
- split: train
path: data.jsonl
---
# Version 1
Image-size 1-10.
Compare histograms between 2 images.
# Version 2
Image-size 1-20.
Histogram.remove_other_colors() exclude colors between two histograms.
These bigger images are causing problems for the model to learn.
# Version 3
Smaller image sizes: width 1-20. height 1-5.
This is training much better.
# Version 4
Smaller image sizes: width 1-5. height 1-20.
# Version 5
Slightly bigger image sizes: width 1-10. height 1-20.
# Version 6
Slightly bigger image sizes: width 1-15. height 10-30.
This was too hard for the LLM to learn.
# Version 7
Slightly smaller image sizes: width 1-15. height 10-20.
# Version 8
image size 10-20.
This was too hard for the LLM to learn.
# Version 9
image width 1-20.
image height 1-5.
This was easy for the LLM to learn.
# Version 10
I want to try just adding 1 more row to the height, and see how that impacts the training loss.
image width 1-20.
image height 1-6.
Training with that extra row, it was easy for the LLM to learn.
# Version 11
I want to try just adding 1 more row to the height, and see how that impacts the training loss.
image width 1-20.
image height 1-7.
Training with that extra row, it was easy for the LLM to learn.
# Version 12
I want to try just adding 1 more row to the height, and see how that impacts the training loss.
image width 1-20.
image height 1-8.
Training with that extra row, it was easy for the LLM to learn.
# Version 13
I want to try just adding 1 more row to the height, and see how that impacts the training loss.
image width 1-20.
image height 1-9.
Training with that extra row, it was took quite some time for the LLM to learn.
# Version 14
I want to try just adding 1 more row to the height, and see how that impacts the training loss.
image width 1-20.
image height 1-10.
Added a `benchmark` column to the dataset.
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