emo-visual-data

emo-visual-data is a public emoticon visual annotation data set, containing 5329 emoticons. Visual annotation is completed through the glm-4v and step-free-api projects. This dataset is suitable for researchers and developers in the fields of natural language processing and computer vision, especially professionals focusing on multi-modal learning and image annotation.

Demand group

This dataset is suitable for researchers and developers in the fields of natural language processing and computer vision, especially those focusing on multi-modal learning and image annotation. It helps them train smarter models and improve their understanding of image content.

Usage scenario examples

1. Researchers used this dataset to train a deep learning model to improve understanding of memes in social media.

2. Developers use the image and text information in the data set to create applications that can automatically recognize and generate emoticons.

3. Educational institutions use this data set as teaching materials to help students learn about image processing and natural language understanding.

Product features

1. Collected 5329 emoticon packages for visual annotation and multi-modal learning.

2. Use glm-4v api and step-free-api for image analysis and annotation.

3. Can be used to create intelligent agents and improve the accuracy of natural language processing and image recognition.

4. Provides a drawing interface for users to directly call and obtain emoticons.

5. The data set supports multi-modal learning, which helps improve the model’s ability to understand images and text.

6. A complete file download link is provided to facilitate users to obtain and use the data set.

Tutorial

1. Visit the GitHub page of emo-visual-data to learn about the basic information and usage conditions of the data set.

2. Select the appropriate download method according to your needs, such as downloading the complete dataset file through Google Drive.

3. Read the README file to understand the structure of the data set and how to use the files in the data set.

4. Use the glm-free-api drawing interface call to obtain the emoticon package, and pay attention to modifying the model parameters to adapt to different needs.

5. Apply the dataset to your own projects, such as training models or developing applications.

6. Continuously iterate and optimize the method of using data sets based on project progress and needs.