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embedding
sequencelengths 256
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Pre-computed vision-language model image embeddings
Embeddings are stored as Parquet files with the following structure:
<DATASET_NAME>_<OP>_<MODEL_NAME>.parquet
"""
DATASET_NAME: name of the dataset, e.g. "imagenette".
OP: split of the dataset (either "train" or "test").
MODEL_NAME: name of the model, e.g. "clip_vit-l_14".
"""
dataset["embedding"] contains the embeddings
dataset["label"] contains the labels
To generate the dataset, run
$ python make_dataset.py
Supported dataset names (see supported_datasets.txt):
imagenette[dataset]
Supported model names (see supported_models.txt):
clip:ViT-RN:50[model]clip:ViT-B/32[model]clip:ViT-L/14[model]open_clip:ViT-B-32[model]open_clip:ViT-L-14[model]FLAVA[model]ALIGN[model]BLIP[model]
References
@inproceedings{teneggi24testing,
title={Testing Semantic Importance via Betting},
author={Teneggi, Jacopo and Sulam, Jeremias},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
}
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Size of downloaded dataset files:
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Size of the auto-converted Parquet files:
291 MB
Number of rows:
107,152