ChatGPT and general-purpose AI count fruits in pictures surprisingly well
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| Format: | Preprint |
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2024
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| author | Mengsuwan, Konlavach Palacio, Juan Camilo Rivera Ryo, Masahiro |
| author_facet | Mengsuwan, Konlavach Palacio, Juan Camilo Rivera Ryo, Masahiro |
| contents | Object counting is a popular task in deep learning applications in various domains, including agriculture. A conventional deep learning approach requires a large amount of training data, often a logistic problem in a real-world application. To address this issue, we examined how well ChatGPT (GPT4V) and a general-purpose AI (foundation model for object counting, T-Rex) can count the number of fruit bodies (coffee cherries) in 100 images. The foundation model with few-shot learning outperformed the trained YOLOv8 model (R2 = 0.923 and 0.900, respectively). ChatGPT also showed some interesting potential, especially when few-shot learning with human feedback was applied (R2 = 0.360 and 0.460, respectively). Moreover, we examined the time required for implementation as a practical question. Obtaining the results with the foundation model and ChatGPT were much shorter than the YOLOv8 model (0.83 hrs, 1.75 hrs, and 161 hrs). We interpret these results as two surprises for deep learning users in applied domains: a foundation model with few-shot domain-specific learning can drastically save time and effort compared to the conventional approach, and ChatGPT can reveal a relatively good performance. Both approaches do not need coding skills, which can foster AI education and dissemination. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2404_08515 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ChatGPT and general-purpose AI count fruits in pictures surprisingly well Mengsuwan, Konlavach Palacio, Juan Camilo Rivera Ryo, Masahiro Computer Vision and Pattern Recognition Image and Video Processing Object counting is a popular task in deep learning applications in various domains, including agriculture. A conventional deep learning approach requires a large amount of training data, often a logistic problem in a real-world application. To address this issue, we examined how well ChatGPT (GPT4V) and a general-purpose AI (foundation model for object counting, T-Rex) can count the number of fruit bodies (coffee cherries) in 100 images. The foundation model with few-shot learning outperformed the trained YOLOv8 model (R2 = 0.923 and 0.900, respectively). ChatGPT also showed some interesting potential, especially when few-shot learning with human feedback was applied (R2 = 0.360 and 0.460, respectively). Moreover, we examined the time required for implementation as a practical question. Obtaining the results with the foundation model and ChatGPT were much shorter than the YOLOv8 model (0.83 hrs, 1.75 hrs, and 161 hrs). We interpret these results as two surprises for deep learning users in applied domains: a foundation model with few-shot domain-specific learning can drastically save time and effort compared to the conventional approach, and ChatGPT can reveal a relatively good performance. Both approaches do not need coding skills, which can foster AI education and dissemination. |
| title | ChatGPT and general-purpose AI count fruits in pictures surprisingly well |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2404.08515 |