# Image Captioning

An image captioning model based on the ViT-GPT2 architecture, capable of generating natural language descriptions for input images.

Idefics3 is an open-source multimodal model capable of processing arbitrary sequences of image and text inputs to generate text outputs. It shows significant improvements in OCR, document understanding, and visual reasoning.

This model is an image-to-text model, focusing on generating captions for images.

Kosmos-2 is a multimodal large language model capable of integrating visual information with language understanding to achieve image-to-text conversion and visual grounding tasks.

Kosmos-2 is a multimodal large language model capable of understanding and generating text descriptions related to images, and establishing associations between text and image regions.

BLIP-2 is a vision-language model based on OPT-2.7b, which achieves image-to-text generation by freezing the image encoder and large language model while training a query transformer.

Kosmos-2 is a multimodal large language model capable of grounding language models to real-world visual elements, supporting various vision-language tasks.

BLIP-2 is a vision-language model that combines an image encoder with a large language model for image-to-text tasks.

An image captioning model based on Swin Transformer and AraGPT2 architecture, capable of generating textual descriptions for input images.

This is a sharded version of the BLIP-2 model implemented with Flan T5-xl for image-to-text tasks such as image captioning and visual question answering. Sharding allows it to be loaded in low-memory environments.

BLIP-2 is a vision-language model based on OPT-6.7b, pretrained by freezing the image encoder and large language model, supporting tasks such as image-to-text generation and visual question answering.

BLIP-2 is a vision-language model based on Flan T5-xl, pre-trained by freezing the image encoder and large language model, supporting tasks such as image captioning and visual question answering.

GIT is a Transformer-based image-to-text generation model trained on large-scale image-text pairs, capable of performing tasks such as image captioning and visual question answering.

GIT is a dual-conditional Transformer decoder based on CLIP image tokens and text tokens for image-to-text generation tasks

GIT is a dual-conditional Transformer decoder based on CLIP image tokens and text tokens, designed for image-to-text generation tasks.