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PixelProse

Large-scale image description dataset, providing more than 16M synthetic image descriptions.

#Dataset
#Image description
#Vision-Language Model
PixelProse

Product Details

PixelProse is a large-scale dataset created by tomg-group-umd that leverages the advanced visual-language model Gemini 1.0 Pro Vision to generate more than 16 million detailed image descriptions. This dataset is of great significance for developing and improving image-to-text conversion technology, which can be used for image description generation, visual question answering and other tasks.

Main Features

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Provides over 16M image-text pairs.
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Supports multiple tasks such as image to text and text to image.
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Contains multiple modalities including tables and text.
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The data format is parquet, which is easy to process by machine learning models.
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Contains detailed image descriptions, suitable for training complex visual-language models.
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The data set is divided into three parts: CommonPool, CC12M and RedCaps.
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Provides the EXIF ​​information and SHA256 hash value of the image to ensure data integrity.

How to Use

1
Step 1: Visit the Hugging Face website and search for the PixelProse dataset.
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Step 2: Choose the appropriate download method, such as downloading the parquet file through Git LFS, Huggingface API or direct link.
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Step 3: Download the corresponding image using the URL in the parquet file.
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Step 4: Load the data set and perform preprocessing according to research or development needs.
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Step 5: Use the dataset to train or test the visual-language model.
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Step 6: Evaluate model performance and adjust model parameters as needed.
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Step 7: Apply the trained model to practical problems or further research.

Target Users

The target audience is researchers and developers in the fields of machine learning and artificial intelligence, particularly those focusing on image recognition, image description generation, and visual question answering systems. The size and diversity of this dataset make it an ideal resource for training and testing these systems.

Examples

The researchers used the PixelProse dataset to train an image description generation model to automatically generate descriptions for images on social media.

Developers used the data set to develop a visual question and answer application that can answer users' questions about the content of images.

Educational institutions use PixelProse as a teaching resource to help students understand the basic principles of image recognition and natural language processing.

Quick Access

Visit Website →

Categories

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› AI image detection and recognition
› AI dataset

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