BLIP-2 - AI Vision Models Tool

Overview

BLIP-2 (Bootstrapping Language-Image Pre-training, v2) is a multimodal visual-language model family designed to connect powerful frozen image encoders with large pretrained language models (LLMs) to perform zero-shot and few-shot image-to-text tasks. Rather than fine-tuning entire large models, BLIP-2 introduces a lightweight Q-Former (query transformer) that maps vision features into a small set of learned query tokens. These tokens are projected into the LLM’s input space, enabling the LLM to reason over visual content without retraining the full vision or language backbones. The design enables efficient training (only the Q-Former and small projection layers are trained) while leveraging state-of-the-art vision encoders (e.g., ViT variants) and LLMs (e.g., OPT, Flan-T5 variants). BLIP-2 demonstrated strong zero-shot performance on common image-to-text benchmarks and has been used as the basis for follow-on research and practical systems (image captioning, visual question answering, retrieval-augmented visual instruction tuning). The project and model checkpoints are publicly available, with usage examples and pretrained weights provided via model hubs such as Hugging Face (see source).

Key Features

• Q-Former: lightweight query transformer maps visual features into learnable query tokens
• Frozen backbones: uses frozen vision encoders and frozen LLMs to minimize fine-tuning cost
• Modular backbones: supports ViT and multiple LLMs (e.g., OPT, Flan-T5 variants)
• Zero-shot image-to-text: strong zero-shot captioning and VQA without end-to-end retraining
• Efficient training: only Q-Former and projection layers are trained, cutting compute needs
• Open-source checkpoints and examples available on Hugging Face and research repos
• Generates natural-language answers and captions conditioned on visual inputs

Example Usage

Example (python):

from PIL import Image
import requests
import torch
from transformers import Blip2Processor, Blip2ForConditionalGeneration

# Example: image captioning with a pretrained BLIP-2 checkpoint on Hugging Face
processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xl")
model = Blip2ForConditionalGeneration.from_pretrained(
    "Salesforce/blip2-flan-t5-xl",
    torch_dtype=torch.float16,
    device_map="auto"
)

url = "https://huggingface.co/front/thumbnails/what-dog.jpg"  # replace with your image URL
image = Image.open(requests.get(url, stream=True).raw).convert("RGB")

inputs = processor(images=image, text="Describe the image:" , return_tensors="pt").to(model.device)

generated_ids = model.generate(**inputs, max_new_tokens=64)
caption = processor.decode(generated_ids[0], skip_special_tokens=True)
print("Caption:", caption)

Benchmarks

Zero-shot image captioning (MS COCO, relative): Reported as state-of-the-art among frozen-LLM approaches in the BLIP-2 paper and blog (Source: https://huggingface.co/blog/blip-2)

Zero-shot Visual Question Answering (VQA, relative): Strong zero-shot VQA performance compared to prior frozen-backbone methods (see paper/summary) (Source: https://huggingface.co/blog/blip-2)

Training cost / efficiency: Only Q-Former and projection layers are trained; major vision and language backbones remain frozen, reducing compute vs full fine-tuning (Source: https://huggingface.co/blog/blip-2)

Key Information

• Category: Vision Models
• Type: AI Vision Models Tool