VinAIResearch/Dataset-Diffusion
Dataset Diffusion: Diffusion-based Synthetic Data Generation for Pixel-Level Semantic Segmentation (NeurIPS2023)
This project helps computer vision researchers and AI engineers quickly generate synthetic image datasets for semantic segmentation. Instead of manually drawing pixel-level labels, you provide text descriptions, and the system outputs both synthetic images and their corresponding detailed segmentation masks. This significantly reduces the time and effort needed to prepare training data for deep learning models.
128 stars. No commits in the last 6 months.
Use this if you need large, diverse datasets with pixel-level annotations for semantic segmentation models but want to avoid the tedious and costly process of manual labeling.
Not ideal if your primary goal is generating realistic images without the need for precise, pixel-level semantic segmentation masks, or if you require datasets with specific, highly specialized real-world imagery that cannot be adequately described by text prompts.
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Sep 08, 2024
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