Computer Vision
expert
~55 hours
Vision-Language Model Fine-Tuning Pipeline
Fine-tune a vision-language model (LLaVA-style) for a specific domain (e.g., medical imaging, satellite imagery). Build the full pipeline from data curation to evaluation.
Skills Demonstrated
Multimodal model fine-tuning
Vision-language alignment
Domain-specific data curation
Evaluation methodology
Implementation Steps
- Select base VLM (LLaVA, InternVL, or Qwen-VL)
- Curate domain-specific image-text pairs with quality filters
- Implement LoRA fine-tuning for the projection layer
- Build custom evaluation: accuracy, hallucination rate, grounding
- Compare fine-tuned vs base model on domain benchmarks
- Deploy with vLLM for efficient multi-modal serving
Interview Relevance
Why this project matters for interviews
Vision-language models are the frontier of CV/NLP intersection. Companies like Anthropic, Google, and Apple are hiring specifically for multimodal expertise — this project directly demonstrates it.