Duplicate from UCSC-VLAA/Recap-DataComp-1B

Co-authored-by: Cihang Xie <cihangxie@users.noreply.huggingface.co>

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README.md

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+---
+dataset_info:
+  features:
+  - name: url
+    dtype: string
+  - name: re_caption
+    dtype: string
+  - name: org_caption
+    dtype: string
+  - name: sha256
+    dtype: string
+  - name: key
+    dtype: string
+  - name: re_clip_score
+    dtype: float64
+  - name: org_clip_score
+    dtype: float64
+  - name: re_length
+    dtype: int64
+  - name: org_length
+    dtype: int64
+  - name: re_gpt4v_score
+    dtype: int64
+  - name: org_gpt4v_score
+    dtype: int64
+  splits:
+  - name: preview
+    num_bytes: 583351
+    num_examples: 1000
+  - name: train
+    num_bytes: 709865275729
+    num_examples: 1237983779
+  download_size: 432778063056
+  dataset_size: 709865859080
+configs:
+- config_name: default
+  data_files:
+  - split: preview
+    path: data/preview_data/preview-*
+  - split: train
+    path: data/train_data/train-*
+license: cc-by-4.0
+task_categories:
+- zero-shot-classification
+- text-retrieval
+- image-to-text
+- text-to-image
+---
+# Dataset Card for Recap-DataComp-1B
+
+<!-- Provide a quick summary of the dataset. -->
+
+Recap-DataComp-1B is a large-scale image-text dataset that has been recaptioned using an advanced LLaVA-1.5-LLaMA3-8B model to enhance the alignment and detail of textual descriptions.
+
+## Dataset Details
+
+### Dataset Description
+
+<!-- Provide a longer summary of what this dataset is. -->
+Our paper aims to bridge this community effort, leveraging the powerful and open-sourced LLaMA-3, a GPT-4 level LLM.
+Our recaptioning pipeline is simple: first, we fine-tune a LLaMA-3-8B powered LLaVA-1.5 and then employ it to recaption 1.3 billion images from the DataComp-1B dataset. Our empirical results confirm that this enhanced dataset, Recap-DataComp-1B, offers substantial benefits in training advanced vision-language models.  For discriminative models like CLIP, we observe enhanced zero-shot performance in cross-modal retrieval tasks.  For generative models like text-to-image Diffusion Transformers, the generated images exhibit a significant improvement in alignment with users' text instructions, especially in following complex queries.
+
+- **Curated by:** Xianhang Li, Haoqin Tu, Mude Hui, Zeyu Wang, Bingchen Zhao, Junfei Xiao, Sucheng Ren, Jieru Mei, Qing Liu, Huangjie Zheng, Yuyin Zhou, Cihang Xie
+- **License:** cc-by-4.0
+
+### Dataset Sources
+
+<!-- Provide the basic links for the dataset. -->
+
+- **Repository:** [https://github.com/UCSC-VLAA/Recap-DataComp-1B](https://github.com/UCSC-VLAA/Recap-DataComp-1B)
+- **Paper:** [https://arxiv.org/abs/2406.08478](https://arxiv.org/abs/2406.08478)
+
+## Uses
+
+<!-- Address questions around how the dataset is intended to be used. -->
+
+### Direct Use
+
+<!-- This section describes suitable use cases for the dataset. -->
+
+Recap-DataComp-1B is intended for training advanced vision-language models, including discriminative models like CLIP and generative models such as text-to-image Diffusion Transformers. It can be used for tasks such as zero-shot classification, cross-modal retrieval, and text-to-image generation.
+
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
+
+The dataset is not suitable for applications requiring highly accurate and sensitive personal data, as the recaptioned data may still contain noise and inaccuracies from the original web-crawled data.
+
+## 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. -->
+
+The dataset contains fields for image URLs, original captions, recaptioned text, and other metadata such as sha256 hashes. It is structured to facilitate easy access and use for training vision-language models.
+
+## Dataset Creation
+
+### Curation Rationale
+
+<!-- Motivation for the creation of this dataset. -->
+
+The dataset was created to address the noise and misalignment issues present in web-crawled image-text pairs, aiming to improve the performance of vision-language models by providing more semantically rich and well-aligned captions.
+
+### Source Data
+
+<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
+
+The source data is web-crawled image-text pairs from the DataComp-1B dataset, which has been curated from a larger collection of 12.8 billion image-text pairs.
+
+
+#### Data Collection and Processing
+
+<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
+
+Data was collected through web crawling and subjected to rigorous preprocessing, including safety checks, deduplication, and filtering based on CLIP scores and image-based criteria. The recaptioning was done using a fine-tuned LLaMA-3-8B powered LLaVA-1.5 model.
+
+
+
+
+### Annotations
+
+<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
+
+#### Annotation process
+
+<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
+Annotations in the form of recaptioned text were generated using an advanced language model, LLaVA-1.5-LLaMA3-8B. The recaptioning process involved auto-regressive generation with greedy decoding, aimed at producing detailed and semantically rich captions.
+
+
+#### Who are the annotators?
+
+<!-- This section describes the people or systems who created the annotations. -->
+
+The annotations were generated by the LLaVA-1.5-LLaMA3-8B model.
+
+#### Personal and Sensitive Information
+
+<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
+
+The dataset has undergone safety checks to filter out harmful content, but users should still exercise caution as some personal or sensitive information may be present due to the nature of web-crawled data.
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+While the recaptioned dataset aims to improve data quality, it may still contain biases and inaccuracies inherent in the original web-crawled data. Users should be aware of these limitations and the potential for misalignment or noise in the captions.
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
+
+## Citation
+
+<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+
+```
+@article{li2024recaption,
+      title={What If We Recaption Billions of Web Images with LLaMA-3?}, 
+      author={Xianhang Li and Haoqin Tu and Mude Hui and Zeyu Wang and Bingchen Zhao and Junfei Xiao and Sucheng Ren and Jieru Mei and Qing Liu and Huangjie Zheng and Yuyin Zhou and Cihang Xie},
+      journal={arXiv preprint arXiv:2406.08478},
+      year={2024}
+}
+```
+
+
+## Dataset Card Authors
+
+Xianhang Li, Haoqin Tu, Mude Hui, Zeyu Wang, Bingchen Zhao, Junfei Xiao, Sucheng Ren, Jieru Mei, Qing Liu, Huangjie Zheng, Yuyin Zhou, Cihang Xie
+
+## Dataset Card Contact
+
+xli421@ucsc.edu

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