DevQuasar/Synthetic-Cyclic-Perception_exp1

[<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) 'Make knowledge free for everyone' <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a> # Synthetic-Cyclic-Perception  Synthetic-Cyclic-Perception is a synthetic visual dataset created by iteratively generating and describing images in a cyclic fashion. Each cycle begins with a seed prompt that feeds into a diffusion model to generate an initial image. A vision model then provides a detailed description of the generated image, which becomes the prompt for the next cycle. This process is repeated across multiple cycles and batches, creating a dataset where images and their descriptions evolve progressively. ## Dataset Composition - Seed Prompts: The dataset generation starts with carefully selected, action-oriented prompts (e.g., “A person kayaking on a calm river”). - Cycles: Each batch begins with a unique prompt and progresses through a set number of cycles, updating the image based on the most recent description. - Metadata: Each image generated includes metadata such as the prompt, the vision model's description, filename, and batch details, saved in a centralized metadata JSON file for easy reference and analysis. ## Methodology - Image Generation: Using the [stabilityai/stable-diffusion-3-medium](https://huggingface.co/stabilityai/stable-diffusion-3-medium) model, an image is generated based on the current prompt. - Vision Description: The [meta-llama/Llama-3.2-11B-Vision-Instruct](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct) describes the image, offering a nuanced textual interpretation. - Cyclic Prompt Update: The description is parsed and used as the next prompt in the cycle, thus creating an evolving image-description sequence.Synthetic-Cyclic-Perception is a synthetic visual dataset created by iteratively generating and describing images in a cyclic manner. It starts with carefully selected, action-oriented prompts and goes through a series of cycles, with images being updated based on the latest descriptions. Each generated image includes metadata such as the prompt, the vision models description, filename, and batch details, stored in a centralized metadata JSON file for easy reference and analysis.

搜集汇总

背景与挑战

背景概述

该数据集是通过循环生成图像和描述的方式创建的合成视觉数据集。每个循环从种子提示开始，生成图像后由视觉模型描述，再将描述作为下一轮提示，使图像和描述逐步演化。数据集包含74个样本，每个样本有初始提示、生成的图像列表和对应的描述，适用于研究图像生成与描述循环迭代的演化过程。