---
annotations_creators:
- crowdsourced
language:
- en
multilinguality:
- monolingual
pretty_name: KiloGram
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- tangrams
- reference-games
- vision-language
viewer: false
---

Preprocessed training and evaluation data from KiloGram.

KiloGram dataset and code repo: https://github.com/lil-lab/kilogram

---
# File Formats
## Training Set
Texts: `train_*.json` are all in the format of `{tangramName: list(annotations)}`.

Images: Colored images with parts (under `/color`) are named in the format of `tangramName_{idx}.png`, where `idx` corresponds to the index of the annotation in the text file.

## Validation, Development, Heldout Set

Texts: `{whole, part}_{black, color}.json` are in the format of `{"targets": list(imageFileNames), "images": list(imageFileNames), "texts": list(annotations)}`. We flattened all the contexts and concatenated them into one list for each entry. 

E.g. the first 10 elements in `"targets"` are the image file name of the target of the first context repeated 10 times; the first 10 of `"images"` are the image file names in that context; and the first 10 of `"texts"` are the corresponding 10 annotations in that context.

`/controlled` contains experiments with constrained contexts controlled for number of parts, and `/random` contains ones without. (See Appendix A.8 in paper)

`/development/texts/augmented/aug_dev.json` and `images/augmented.tar.bz2` are experiments in the same format as above used to evaluate the effect of adding part information. 


Intermediate files:

`*/text/controlled/eval_batch_data.json` are in the format of 
`{tangramName: {numOfParts: list({"target": [tangramName_{idx}, annotation], "distractors": list(list([tangramName_{idx}, annotation]))})}}`, used to generate controlled experiment jsons. Note: annotations are descriptions concatenated by "#" instead of in natural English.