Finetuning Moondream2 for Computer Vision Tasks

The guide examines the fine-tuning of Moondream2, a small open-source vision language model, using a computer vision dataset to enhance its performance in counting tasks, which larger models like GPT-4V struggle with. Moondream2, despite not being state-of-the-art, offers advantages due to its ability to run locally with reasonable speed and accuracy, and it outperforms larger models like GPT-4o on certain benchmarks. The process involves using an object detection dataset from Roboflow to fine-tune Moondream2, demonstrating the challenges and steps in adapting VLMs for specific tasks. Initial tests showed Moondream2's inconsistent results in counting currency, but after fine-tuning with adjusted hyperparameters, the model achieved significantly improved accuracy. The successful fine-tuning illustrates how VLMs can transition from experimental tools to practical components in production applications, highlighting the potential of open-source models like Moondream2 in computer vision systems.