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Datasets:
apple
/
DataCompDR-1B

Tasks:
Text-to-Image
Image-to-Text
Modalities:
Image
Text
Formats:
webdataset
Languages:
English
Size:
1B - 10B
ArXiv:
Libraries:
Datasets
WebDataset
License:
Dataset card Viewer Files Community
3
DataCompDR-1B
File size: 4,885 Bytes 
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---
license: other
license_name: custom-apple-license
license_link: https://github.com/apple/ml-mobileclip/blob/main/LICENSE
dataset_info:
  features:
  - name: url.txt
    dtype: string
  - name: syn.json
    struct:
    - name: syn_text
      list:
        dtype: string
  - name: paug.json
    struct:
    - name: param_aug
      dtype: string
  - name: npz
    struct:
    - name: image_emb
      list:
        list: float32
    - name: text_emb
      list:
        list: float32
  - name: json
    struct:
    - name: uid
      dtype: string
    - name: sha256
      dtype: string
task_categories:
  - text-to-image
  - image-to-text
language:
  - en
---

# Dataset Card for DataCompDR-1B

<!-- Provide a quick summary of the dataset. -->

This dataset contains synthetic captions, embeddings, and metadata for DataCompDR-1B.
The metadata has been generated using pretrained image-text models on [DataComp-1B](https://huggingface.co/datasets/mlfoundations/datacomp_1b).
For details on how to use the metadata, please visit our [github repository](https://github.com/apple/ml-mobileclip).

## Dataset Details

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->

DataCompDR is an image-text dataset and an enhancement to the DataComp dataset.
We reinforce the DataComp dataset using our multi-modal dataset reinforcement strategy.
In particular, we create DataCompDR-1B and DataCompDR-12M by reinforcing the DataComp-1B (BestPool filtering) and a uniform subset of 12.8M samples, DataCompDR-12M.
We have a one-time generation process, the cost of which is amortized over multiple architectures and extensive ablations.
We generate 5 synthetic captions per image using the `coca_ViT-L-14` model in OpenCLIP, and strong random image augmentations (10 for DataCompDR-1B and 30 for DataCompDR-12M).
We compute embeddings of an ensemble of two strong teachers (`ViT-L-14` with pretrained weights `datacomp_xl_s13b_b90k` and openai in OpenCLIP) on augmented images as well as real and synthetic captions.
Embeddings are 1536-D concatenations of 2x768-D vectors.
One seen sample for DataCompDR is a triplet of one randomly augmented image, one ground-truth caption, and one randomly picked synthetic caption.

- **Curated by:** Original data by [DataComp](https://www.datacomp.ai/) and metadata by Apple.
- **License:** We distribute our metadata under our [license](https://github.com/apple/ml-mobileclip/blob/main/LICENSE). The original image url-text samples and metadata were released by [DataComp](https://www.datacomp.ai/) under Creative Common CC-BY-4.0 license. The individual images are under their own copyrights.
- **Repository:** [ml-mobileclip GitHub](https://github.com/apple/ml-mobileclip)
- **Paper:** [MobileCLIP paper](https://arxiv.org/abs/2311.17049)
- **Demo:** Coming Soon

## Uses

<!-- Address questions around how the dataset is intended to be used. -->

Training with DataCompDR shows significant learning efficiency improvement compared to the standard CLIP training.
For example, with a single node of 8×A100 GPUs, we achieve 61.7% zero-shot classification on ImageNet-val in approximately one day when training a ViT-B/16 based CLIP from scratch on DataCompDR-12M.
Training with DataCompDR-1B sets new state-of-the-art performance on several metrics (Fig. 2) while still using a fraction of the training compute budget compared to previous works.
Using DataCompDR, we demonstrate 10x-1000x learning efficiency in comparison to DataComp.

## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

```
- <uid>.url.txt: Image URL (string)
- <uid>.syn.json:
  - syn_text: List of synthetic captions (list[string])
- <uid>.paug.json:
  - param_aug: List of augmentation parameters (list[list[Union[int,float]]])
- <uid>.npz
  - image_emb: List of image embeddings for multiple image augmentations (list[list[float]])
  - text_emb: List of text embeddings for ground-truth/synthetic captions (list[list[float]])
- <uid>.json
  - uid: UID of image-text sample in DataComp (string)
  - sha256: SHA256 hash of the image (string)
```


## Citation

**[MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training](https://arxiv.org/pdf/2311.17049.pdf). (CVPR 2024)**
*Pavan Kumar Anasosalu Vasu, Hadi Pouransari, Fartash Faghri, Raviteja Vemulapalli, Oncel Tuzel.*

```bibtex
@InProceedings{mobileclip2024,
  author = {Pavan Kumar Anasosalu Vasu, Hadi Pouransari, Fartash Faghri, Raviteja Vemulapalli, Oncel Tuzel},
  title = {MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2024},
}
```