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---
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license: mit
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---
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### Pre-computed vision-language model image embeddings
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Embeddings are stored as [Parquet](https://parquet.apache.org/) files with the following structure:
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```python
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<DATASET_NAME>_<OP>_<MODEL_NAME>.parquet
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"""
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DATASET_NAME: name of the dataset, e.g. "imagenette".
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OP: split of the dataset (either "train" or "test").
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MODEL_NAME: name of the model, e.g. "clip_vit-l_14".
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"""
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dataset["embedding"] contains the embeddings
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dataset["label"] contains the labels
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```
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To generate the dataset, run
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```bash
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$ python make_dataset.py
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```
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Supported dataset names (see [supported_datasets.txt](supported_datasets.txt)):
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* `imagenette` [[dataset](https://github.com/fastai/imagenette)]
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Supported model names (see [supported_models.txt](supported_models.txt)):
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* `clip:ViT-RN:50` [[model](https://github.com/openai/CLIP)]
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* `clip:ViT-B/32` [[model](https://github.com/openai/CLIP)]
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* `clip:ViT-L/14` [[model](https://github.com/openai/CLIP)]
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* `open_clip:ViT-B-32` [[model](https://huggingface.co/laion/CLIP-ViT-B-32-laion2B-s34B-b79K)]
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* `open_clip:ViT-L-14` [[model](https://huggingface.co/laion/CLIP-ViT-L-14-laion2B-s32B-b82K)]
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* `FLAVA` [[model](https://huggingface.co/facebook/flava-full)]
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* `ALIGN` [[model](https://huggingface.co/kakaobrain/align-base)]
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* `BLIP` [[model](https://huggingface.co/Salesforce/blip-itm-base-coco)]
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**References**
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```
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@inproceedings{teneggi24testing,
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title={Testing Semantic Importance via Betting},
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author={Teneggi, Jacopo and Sulam, Jeremias},
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booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
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year={2024},
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}
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``` |
