# Multimodal Image Captioning

Qwen2.5-VL-7B-Captioner-Relaxed is a multimodal vision-language model based on the Qwen2.5 architecture, focusing on image-to-text generation tasks.

A multimodal large language model fine-tuned based on Qwen2.5-VL-7B-Instruct, specifically optimized for text-to-image generation, capable of producing more detailed image descriptions

This is an 8-bit quantized version of the Qwen2.5-VL-3B-Instruct model, optimized for the MLX framework and supports image-text generation tasks.

An instruction-tuned version based on Qwen2-VL-7B-Instruct, focusing on generating more detailed image descriptions, optimized for text-to-image dataset creation.

BLIP is an advanced vision-language pretrained model, excelling in image captioning tasks, capable of generating accurate natural language descriptions based on image content.

BLIP is a unified vision-language pretraining framework, excelling in image caption generation and understanding tasks, efficiently utilizing web data through guided annotation strategies

Microsoft's VinVL foundational pre-trained model, specifically designed for image captioning tasks, with strong visual-language understanding capabilities.