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--- |
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license: other |
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license_name: custom-apple-license |
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license_link: https://github.com/apple/ml-mobileclip/blob/main/LICENSE |
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task_categories: |
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- text-to-image |
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- image-to-text |
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language: |
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- en |
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--- |
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# Dataset Card for DataComp-12M |
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<!-- Provide a quick summary of the dataset. --> |
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This dataset contains UIDs of DataComp-12M that is a 12M subset of [DataComp-1B-BestPool](https://huggingface.co/datasets/mlfoundations/datacomp_1b). |
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Image-text models trained on DataComp-12M are significantly better than on CC-12M/YFCC-15M as well as DataComp-Small/Medium. |
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For details on this dataset and the improved [DataCompDR-12M](https://huggingface.co/datasets/apple/DataCompDR-12M), |
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please visit our [MobileCLIP paper](https://arxiv.org/abs/2311.17049). |
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The dataset with the original captions is now available at [mlfoundations/DataComp-12M](https://huggingface.co/datasets/mlfoundations/DataComp-12M). |
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The UIDs per shards match between [mlfoundations/DataComp-12M](https://huggingface.co/datasets/mlfoundations/DataComp-12M) and [apple/DataCompDR-12M](https://huggingface.co/datasets/apple/DataCompDR-12M). |
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## Dataset Details |
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### Dataset Description |
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<!-- Provide a longer summary of what this dataset is. --> |
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DataCompDR is an image-text dataset and an enhancement to the DataComp dataset. |
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We reinforce the DataComp dataset using our multi-modal dataset reinforcement strategy. |
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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. |
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We have a one-time generation process, the cost of which is amortized over multiple architectures and extensive ablations. |
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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). |
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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. |
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Embeddings are 1536-D concatenations of 2x768-D vectors. |
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One seen sample for DataCompDR is a triplet of one randomly augmented image, one ground-truth caption, and one randomly picked synthetic caption. |
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- **Curated by:** Original data by [DataComp](https://www.datacomp.ai/) and metadata by Apple. |
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- **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. |
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- **Repository:** [ml-mobileclip GitHub](https://github.com/apple/ml-mobileclip) |
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- **Paper:** [MobileCLIP paper](https://arxiv.org/abs/2311.17049) |
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- **Demo:** Coming Soon |
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## Uses |
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<!-- Address questions around how the dataset is intended to be used. --> |
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Training with DataCompDR shows significant learning efficiency improvement compared to the standard CLIP training. |
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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. |
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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. |
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Using DataCompDR, we demonstrate 10x-1000x learning efficiency in comparison to DataComp. |
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## Dataset Structure |
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<!-- 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. --> |
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``` |
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- uids.txt: List of 12779520 (65536*195) UIDs, one UID per line. |
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- uids.npy: List of 12779520 (65536*195) UIDs as a NumPy array of type `numpy.dtype("u8,u8")`. |
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``` |
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## Citation |
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**[MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training](https://arxiv.org/pdf/2311.17049.pdf). (CVPR 2024)** |
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*Pavan Kumar Anasosalu Vasu, Hadi Pouransari, Fartash Faghri, Raviteja Vemulapalli, Oncel Tuzel.* |
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```bibtex |
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@InProceedings{mobileclip2024, |
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author = {Pavan Kumar Anasosalu Vasu, Hadi Pouransari, Fartash Faghri, Raviteja Vemulapalli, Oncel Tuzel}, |
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title = {MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training}, |
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booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, |
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month = {June}, |
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year = {2024}, |
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} |
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``` |
