tech
Unlocking the potential of vision language models on satellite imagery through fine-tuning
Fine-tuning foundation models is transforming how we apply AI to real-world problems. By adapting pre-trained models to specific domains, we can unlock dramatically better performance on specialized tasks. Today, we’re excited to share how fine-tuning Pixtral-12B on satellite imagery leads to significant improvements over the base model, showcasing the power of domain-specific adaptation.
TL;DR
- Fine-tuning pre-trained AI models on specific domains improves performance on specialized tasks.
- Low-Rank Adaptation (LoRA) offers an efficient method for fine-tuning by modifying only a small number of model weights.
- Fine-tuning is more effective than prompt engineering for complex or nuanced tasks.
- Satellite imagery analysis benefits greatly from fine-tuning, enabling accurate classification of detailed scenes.
- Fine-tuning Pixtral-12B on the Aerial Image Dataset (AID) improved overall accuracy from 0.56 to 0.91, with hallucinations reduced from 5% to 0.1%.
- The fine-tuning process was cost-effective, costing less than $10 and using 8,000 training samples.
- Key hyperparameters for fine-tuning include learning rate, batch size, and epochs.