Emoticon Visual Dataset Annotated by Multimodal AI

A visual dataset of emoticons annotated using the image parsing capabilities of the glm-4v and step-1v multimodal AI models. The dataset was created by LLM-Red-Team and was last updated on April 27, 2024. The specific number of images, rows, and columns is unknown.

Use Cases

• Train a vision model to classify emoticon types based on visual features extracted by the glm-4v and step-1v annotation pipeline.
• Analyze the visual patterns and styles of emoticons as categorized by the AI-generated labels.
• Use the dataset as a benchmark for evaluating the performance of other vision models on parsing and understanding emoticon imagery.

Strengths

• Annotations are generated by advanced multimodal AI models (glm-4v and step-1v).
• Dataset was updated on April 27, 2024, indicating recent activity.

Limitations

• The dataset size, number of images, and specific annotation columns are unknown.
• The annotation quality is dependent on the performance of the AI models used, which may introduce label noise.
• Lack of metadata such as license, file formats, and sample data limits usability assessment.

Provenance

Source

GitHub, author LLM-Red-Team.

Collection Method

Created using the image parsing capabilities of glm-4v and step-1v AI models.

Freshness

Last updated on 2024-04-27.