blip-2-vision-language

Generate image captions by running inference with BLIP-2 processors and conditional generation. It connects frozen image encoders to large language models via a Q-Former, producing natural-language descriptions directly from your images.

Visual question answering provides answers to questions about a given image. BLIP-2 handles this by using its Q-Former architecture to bridge frozen image encoders and large language models, processing both the image and text query for accurate responses.

Yes, you need PyTorch and Transformers to perform image-text retrieval. The workflow requires installing compatible versions of Transformers, PyTorch, and Pillow to run inference with BLIP-2 processors and extract matching image-text embeddings.

Yes, you can extract projected image and text embeddings using BLIP-2. These embeddings are designed for downstream retrieval pipelines, allowing you to score and match images with text queries for custom search and recommendation systems.

The best way is to use BLIP-2 for image captioning and image-text retrieval. Automatically caption the photos using the Q-Former, then retrieve the best matching images for queries by computing image-text similarity scores.