The massive dataset aims to address what Apple describes as a gap in current AI image editing training. While systems like GPT-4o can make impressive edits, the researchers say progress has been limited by inadequate training data built from real photographs. Apple’s new dataset aims to improve the situation.
Pico-Banana-400K features images organized into 35 different edit types across eight categories, from basic adjustments like color changes to complex transformations such as converting people into Pixar-style characters or LEGO figures. Each image went through Apple’s AI-powered quality control system, with Google’s Gemini-2.5-Pro being used to evaluate the results based on instruction compliance and technical quality.
The dataset also includes three specialized subsets: 258,000 single-edit examples for basic training, 56,000 preference pairs comparing successful and failed edits, and 72,000 multi-turn sequences showing how images evolve through multiple consecutive edits.
Apple built the dataset using Google’s Gemini-2.5-Flash-Image (aka Nano-Banana) editing model, which was released just a few months ago. However, Apple’s research revealed its limitations. While global style changes succeeded 93% of the time, precise tasks like relocating objects or editing text seriously struggled, with success rates below 60%.
Despite the limitations, researchers say their aim with Pico-Banana-400K is to establish “a robust foundation for training and benchmarking the next generation of text-guided image editing models.” The complete dataset is freely available for non-commercial research use on GitHub, so developers can use it to train more capable image editing AI.
This article, “Apple’s New AI Dataset Aims to Improve Photo Editing Models” first appeared on MacRumors.com
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