# Image Understanding

Qwen2-VL-2B is a vision-language model that provides a quantized version in GGUF format, suitable for various scenarios.

This is a transformers-compatible vision-language model with image understanding and text generation capabilities

LLaVA-Saiga-8b is a vision-language model (VLM) developed based on the IlyaGusev/saiga_llama3_8b model, primarily optimized for Russian tasks while retaining English processing capabilities.

llava-calm2-siglip is an experimental vision-language model capable of answering questions about images in Japanese and English.

A multimodal dialogue model fine-tuned based on google/paligemma-3b-mix-448, optimized for multi-turn conversation scenarios

This model is a fine-tuned version of google/paligemma-3b-pt-224 on a subset of the VQAv2 dataset, specializing in visual question answering tasks.

LLaVA model fine-tuned based on Meta-Llama-3-8B-Instruct and CLIP-ViT-Large-patch14-336, supporting image-to-text tasks

LLaVA model fine-tuned based on Phi-3-mini-4k-instruct and CLIP-ViT-Large-patch14-336, supporting image-to-text tasks

This model is a visual question answering model fine-tuned from Salesforce/blip-vqa-base, specializing in the fashion domain

Eris Prime V2 is a 7B-parameter multimodal language model with vision capabilities, requiring Koboldcpp for operation.

LLaVA is a vision-language model capable of understanding and generating text related to images.

TeCoA is a vision-language model initialized from OpenAI CLIP, enhanced with supervised adversarial fine-tuning for improved robustness

LLaVA is an open-source multimodal chatbot based on the Transformer architecture, offering various model versions that balance size and quality through quantization techniques.

LLaVA is a vision-language model capable of understanding and generating text related to images.

This is a visual question answering model based on the Apache-2.0 license, supporting the English language, and focusing on handling vision-related question answering tasks.

Llava v1.5 13B is a multimodal model developed by Haotian Liu, combining visual and language capabilities to understand and generate content based on images and text.

mPLUG-Owl is a multimodal large language model based on the LLaMA-7B architecture, supporting image understanding and text generation tasks.

A model focused on converting image content into text descriptions, supporting Chinese processing.

BEiT base model fine-tuned on an unknown dataset, specific use cases and performance details are currently unavailable

UperNet is a semantic segmentation framework combined with the ConvNeXt large backbone network for pixel-level semantic label prediction.