# Image-text retrieval

FG-CLIP is a fine-grained vision and text alignment model that achieves global and region-level image-text alignment through two-stage training, enhancing fine-grained visual understanding ability.

SigLIP 2 is a vision-language model based on the SigLIP pre-training objective, integrating multiple technologies to enhance semantic understanding, localization, and dense feature extraction capabilities.

SigLIP 2 is an improved multilingual vision-language encoder based on SigLIP, enhancing semantic understanding, localization, and dense feature extraction capabilities.

SigLIP 2 is an improved model based on SigLIP, integrating multiple technologies to enhance semantic understanding, localization, and dense feature extraction capabilities.

A Japanese CLIP model trained based on the OpenCLIP framework, trained on a dataset of 1.45 billion Japanese image-text pairs, supporting zero-shot image classification and image-text retrieval tasks

Japanese CLIP model trained on OpenCLIP framework, supporting zero-shot image classification tasks

SigLIP is an improved multimodal model based on CLIP, employing a superior Sigmoid loss function, pre-trained on the WebLI dataset, and suitable for tasks such as zero-shot image classification and image-text retrieval.

SigLIP is a vision-language model pre-trained on the WebLi dataset, employing an improved sigmoid loss function to optimize image-text matching tasks.

SigLIP is a multimodal model pretrained on the WebLi dataset, utilizing an improved Sigmoid loss function, suitable for zero-shot image classification and image-text retrieval tasks.

SigLIP is a vision-language model pre-trained on the WebLi dataset, utilizing an improved sigmoid loss function to enhance performance

SigLIP is a vision-language model pretrained on the WebLi dataset, utilizing an improved sigmoid loss function, excelling in image classification and image-text retrieval tasks.

SigLIP is a multimodal model pre-trained on the WebLi dataset, employing an improved sigmoid loss function, suitable for zero-shot image classification and image-text retrieval tasks.

SigLIP is a vision-language model pre-trained on the WebLi dataset, employing an improved Sigmoid loss function, excelling in image classification and image-text retrieval tasks.

A visual-language generation model fine-tuned for image-text retrieval tasks, improved based on google/flan-t5-xl

A vision-language generation model fine-tuned based on google/flan-t5-xxl, specifically designed for image-text retrieval tasks

SigLIP is a vision-language model pretrained on the WebLi dataset, utilizing an improved Sigmoid loss function to optimize image-text matching tasks

A CLIP ConvNeXt-XXLarge model trained on the LAION-2B dataset, implemented using the OpenCLIP framework, focusing on zero-shot image classification tasks.