Analyze images with vision-language model

Understand images and answer questions about them using frozen vision-language encoders

Zero-shot image understanding tasks where training data is unavailable and frozen backbones reduce compute.

• · JPEG/PNG images
• · Optional text questions about images
• · Text descriptions for classification

• · Image captions
• · Answers to visual questions
• · Image classification scores

• · Salesforce/blip2-opt-2.7b or blip2-flan-t5 pretrained models
• · torch
• · transformers
• · Pillow

• · GPU for inference (8GB+ VRAM)
• · HuggingFace or LAVIS library installed

• · Caption quality depends on model size (small models less coherent)
• · VQA fails on rare objects or abstract concepts
• · Context dependency limits zero-shot capability
• · Q-Former may hallucinate details not in image

• · Salesforce-built Q-Former architecture
• · Only 188M trainable parameters
• · Benchmarks on VQA datasets included
• · Works with frozen vision and language models