Counter-Strike Deathmatch Video and Action Labels for Imitation Learning

Aggregating video, action labels, and metadata from the popular video game CS:GO. It was created by TeaPearce and presented in a 2022 IEEE Conference on Games paper that won a Best Paper Award.

Use Cases

• Train imitation learning models using the provided action labels and video frames.
• Apply behavioral cloning techniques to the recorded in-game actions.
• Build world models or video generation models from the sequential video and metadata.

Strengths

• Dataset is associated with a research paper that won a Best Paper Award at the IEEE Conference on Games (CoG) 2022.
• Designed for large-scale applications in imitation learning and behavioral cloning.

Limitations

• The specific size, row count, column structure, and file formats are unknown from the provided input.
• The dataset's temporal coverage and geographic scope are not specified.

Provenance

Source

TeaPearce on Hugging Face.

Collection Method

Collected from the video game CS:GO.

Freshness

The dataset was last updated on 2025-01-08.