emo-visual-data
E
Emo Visual Data
Overview :
emo-visual-data is a publicly available emoji visual annotation dataset. It collects 5329 emojis through visual annotation completed using the glm-4v and step-free-api projects. This dataset can be used to train and test multimodal large models and is crucial for understanding the relationship between image content and textual descriptions.
Target Users :
This dataset is suitable for researchers and developers in the fields of natural language processing and computer vision, especially those focused on multimodal learning and image annotation. It can help them train smarter models and improve their understanding of image content.
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Use Cases
Researchers use this dataset to train deep learning models to improve understanding of emojis in social media.
Developers leverage the dataset's image and text information to create applications that can automatically recognize and generate emojis.
Educational institutions use this dataset as teaching material to help students learn about image processing and natural language understanding.
Features
Collects 5329 emojis for visual annotation and multimodal learning.
Uses glm-4v api and step-free-api for image parsing and annotation.
Can be used to create intelligent agents and improve the accuracy of natural language processing and image recognition.
Provides a drawing interface for users to directly call and retrieve emojis.
Supports multimodal learning, helping models better understand images and text.
Provides complete file download links for easy access and use of the dataset.
How to Use
Visit the emo-visual-data GitHub page to learn about the basic information and usage conditions of the dataset.
Choose the appropriate download method based on your needs, such as downloading the complete dataset file via Google Drive.
Read the README file to understand the dataset structure and how to use the files within the dataset.
Use the glm-free-api drawing interface to call and retrieve emojis, noting that the model parameter needs to be adjusted to meet specific requirements.
Apply the dataset to your own projects, such as training models or developing applications.
Continuously iterate and optimize the use of the dataset based on project progress and needs.
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