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.

物体计数是深度学习应用中的一个流行任务，通过对比传统方法和ChatGPT以及基于少样本学习的通用AI模型(T-Rex)，作者发现少样本学习的基础模型表现优异，并且ChatGPT在人类反馈下也显示出潜力，同时实现结果所需时间明显少于传统方法，这些结果为实际应用领域的深度学习用户带来了两个惊喜：基于少样本学习的基础模型能够大大节约时间和精力，而ChatGPT也展现出相对良好的性能。这两种方法均无需编程技能，能够促进人工智能教育和传播。

ChatGPT和通用AI在图片中出人意料地很好地计算水果数量