The structure of the ShareGPT-4o-Image dataset is designed to make it ideal for reviewing and training multimodal models. The dataset adheres to a strictly standardized format, with each sample containing a complete text cue and corresponding image output, which can be directly fed into the model for end-to-end training. 45K text-only-to-image samples and 46K text-plus-image-to-image samples are balanced to ensure that the model learns both the core competencies of idea generation and accurate editing.
The dataset provides detailed documentation and code examples to support developers to quickly integrate into existing training processes. Typical applications include fine-tuning diffusion models to improve generation quality, verifying the alignment of models with human intent, and testing model performance under complex cues. The standardized features of the dataset enable it to be used as a benchmark test set in multimodal domains for a fair comparison of the performance differences between different models.
This answer comes from the articleShareGPT-4o-Image: an open source multimodal image generation datasetThe
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