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This work is licensed under a Creative Commons Attribution 4.0 International License.
Countify: A Text-to-Image Generation Model
Prof. Dr.S.S.More, Athrav Raju Ugale, Vaidehi KeshavVaze, Vaishnav Bajrang Patil, Alisha Mubarak Attar, Sanika Appasaheb Patil
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Abstract: This paper presents Countify, a system that integrates text-to-image generation with object detection and an iterative feedback mechanism to ensure precise object counts in generated images. The system utilizes diffusion-based image generation via ClipDrop API and object detection using YOLOv8. A validation loop continuously refines outputs until the detected object count matches the requested count.
Experimental results demonstrate that Countify significantly improves numerical accuracy in generated images, making it suitable for applications requiring precision such as dataset generation, education, and industrial automation.
Keywords: Text-to-Image Generation, Object Counting, YOLOv8, Diffusion Models, Feedback Loop, Generative AI, Computer Vision
Experimental results demonstrate that Countify significantly improves numerical accuracy in generated images, making it suitable for applications requiring precision such as dataset generation, education, and industrial automation.
Keywords: Text-to-Image Generation, Object Counting, YOLOv8, Diffusion Models, Feedback Loop, Generative AI, Computer Vision
How to Cite:
[1] Prof. Dr.S.S.More, Athrav Raju Ugale, Vaidehi KeshavVaze, Vaishnav Bajrang Patil, Alisha Mubarak Attar, Sanika Appasaheb Patil, βCountify: A Text-to-Image Generation Model,β International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2026.155246
This work is licensed under a Creative Commons Attribution 4.0 International License.