ChatGPT and general-purpose AI count fruits in pictures surprisingly
  well
Konlavach Mengsuwan Juan Camilo Rivera Palacio Masahiro Ryo
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.