# Sigmoid loss function

SigLIP 2 vision-language model trained on the WebLI dataset, suitable for 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 an improved CLIP model pre-trained on the WebLi dataset, optimized for image-text matching tasks using a Sigmoid loss function

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.

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