---
license: mit
---

### Pre-computed vision-language model image embeddings

Embeddings are stored as [Parquet](https://parquet.apache.org/) files with the following structure:

```python
<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

```bash
$ python make_dataset.py
```

Supported dataset names (see [supported_datasets.txt](supported_datasets.txt)):

* `imagenette` [[dataset](https://github.com/fastai/imagenette)]

Supported model names (see [supported_models.txt](supported_models.txt)):

* `clip:ViT-RN:50` [[model](https://github.com/openai/CLIP)]
* `clip:ViT-B/32` [[model](https://github.com/openai/CLIP)]
* `clip:ViT-L/14` [[model](https://github.com/openai/CLIP)]
* `open_clip:ViT-B-32` [[model](https://huggingface.co/laion/CLIP-ViT-B-32-laion2B-s34B-b79K)]
* `open_clip:ViT-L-14` [[model](https://huggingface.co/laion/CLIP-ViT-L-14-laion2B-s32B-b82K)] 
* `FLAVA` [[model](https://huggingface.co/facebook/flava-full)]
* `ALIGN` [[model](https://huggingface.co/kakaobrain/align-base)]
* `BLIP` [[model](https://huggingface.co/Salesforce/blip-itm-base-coco)]
 
**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},
}
```