Stable Diffusion for Data Augmentation in COCO and Weed Datasets

• URL: http://arxiv.org/abs/2312.03996v3
• Date: Tue, 16 Jan 2024 21:30:52 GMT
• Title: Stable Diffusion for Data Augmentation in COCO and Weed Datasets
• Authors: Boyang Deng
• Abstract summary: This study utilized seven common categories and three widespread weed species to evaluate the efficiency of a stable diffusion model. Three techniques (i.e., Image-to-image translation, Dreambooth, and ControlNet) based on stable diffusion were leveraged for image generation with different focuses. Then, classification and detection tasks were conducted based on these synthetic images, whose performance was compared to the models trained on original images.
• Score: 5.81198182644659
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Generative models have increasingly impacted relative tasks, from computer vision to interior design and other fields. Stable diffusion is an outstanding diffusion model that paves the way for producing high-resolution images with thorough details from text prompts or reference images. It will be an interesting topic about gaining improvements for small datasets with image-sparse categories. This study utilized seven common categories and three widespread weed species to evaluate the efficiency of a stable diffusion model. In detail, Stable diffusion was used to generate synthetic images belonging to these classes; three techniques (i.e., Image-to-image translation, Dreambooth, and ControlNet) based on stable diffusion were leveraged for image generation with different focuses. Then, classification and detection tasks were conducted based on these synthetic images, whose performance was compared to the models trained on original images. Promising results have been achieved in some classes. This seminal study may expedite the adaption of stable diffusion models to different fields.

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