Sbloccare il potenziale dei vision language model sulle immagini satellitari tramite fine-tuning

Fine-tuning foundation models can dramatically improve performance on specialized tasks like satellite image analysis. Using efficient techniques such as Low-Rank Adaptation (LoRA), a generalist model like Pixtral-12B can be adapted for specific domains without requiring a full retraining. A case study using the Aerial Image Dataset shows that fine-tuning improved classification accuracy from 0.56 to 0.91. The adapted model also became more consistent across different classes and significantly reduced hallucinations, demonstrating the power of domain-specific adaptation for real-world AI applications.