Create README.md

README.md

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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},
+}
+```