Buckets:
| license: mit | |
| task_categories: | |
| - image-to-text | |
| language: | |
| - en | |
| tags: | |
| - video-games | |
| pretty_name: VideoGameBunny Dataset | |
| size_categories: | |
| - 100K<n<1M | |
| # VideoGameBunny Instruction Following Dataset | |
| [[Website](https://videogamebunny.github.io/)] | |
| ## Overview | |
| We present a comprehensive dataset of 185,259 high-resolution images from 413 video games, sourced from YouTube videos. This dataset addresses the lack of game-specific instruction-following data and aims to improve the ability of open-source models to understand and respond to video game content. | |
|  | |
| ## Dataset Composition | |
| Our dataset includes various types of instructions generated for these images using different large multimodal models: | |
| 1. Short captions | |
| 2. Long captions | |
| 3. Image-to-JSON conversions | |
| 4. Image-based question-answering pairs | |
| ## Dataset Statistics | |
| | Task | Generator | Samples | | |
| |------|-----------|---------| | |
| | Short Captions | Gemini-1.0-Pro-Vision | 70,673 | | |
| | Long Captions | GPT-4V | 70,799 | | |
| | Image-to-JSON | Gemini-1.5-Pro | 136,974 | | |
| | Question Answering | Llama-3, GPT-4o | 81,122 | |
Xet Storage Details
- Size:
- 1.19 kB
- Xet hash:
- 5d64f37897605a76819b43288a190dca016657e6be66bc5d175b4ffdb496a694
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