# High-resolution Processing

This model employs an improved VNet architecture for 2D image processing, focusing on image-to-image translation tasks with adversarial and aesthetic optimization features.

C-RADIOv2 is a visual feature extraction model developed by NVIDIA, offering multiple specification versions suitable for image understanding and dense processing tasks.

Vision Transformer model based on SigLIP, utilizing global average pooling for image features

Vision Transformer model based on SigLIP architecture, containing only the image encoder part, using original attention pooling mechanism

ViT-B-16 vision Transformer model based on SigLIP, containing only the image encoder, utilizing raw attention pooling

ConvNeXt-Large image encoder based on CLIP architecture, fine-tuned on the LAION-2B dataset, supporting 320x320 resolution image feature extraction

DUSt3R is a model for easily achieving geometric 3D vision from images, capable of reconstructing 3D scenes from single or multiple images.

Large-scale vision-language model based on Vision Transformer architecture, supporting zero-shot image classification tasks

This model is a fine-tuned version based on Facebook's ConvNeXtV2 architecture, specifically trained for multi-label classification tasks on Pixiv ranking images

Large-scale vision-language model based on EVA02 architecture, supporting zero-shot image classification tasks

EVA02 is a large-scale vision-language model based on the CLIP architecture, supporting zero-shot image classification tasks.

Large-scale image classification model based on Transformer architecture, pre-trained and fine-tuned on ImageNet-21k and ImageNet-1k datasets