# Image Feature Extraction

OpenVision-ViT-Tiny is a fully open, cost-effective advanced visual encoder, part of the OpenVision family, focusing on multimodal learning.

OpenVision is a fully open, cost-effective family of advanced vision encoders focused on multimodal learning.

OpenVision is a fully open, cost-effective family of advanced vision encoders focused on multimodal learning.

OpenVision is a fully open, cost-effective advanced visual encoder family focused on multimodal learning.

SAM2 model based on the HieraDet image encoder, focusing on image feature extraction tasks.

SAM2 model based on the HieraDet image encoder, focused on image feature extraction tasks.

SAM2 model weights based on HieraDet image encoder, focused on image feature extraction tasks

SAM2 model based on the HieraDet image encoder, focused on image feature extraction tasks.

The first hybrid computer vision model combining the strengths of Mamba and Transformer, enhancing visual feature modeling through redesigned Mamba formulations and incorporating self-attention modules in the Mamba architecture to improve long-range spatial dependency modeling.

An image classification model based on Vision Transformer, pretrained on ImageNet-21k, suitable for feature extraction and fine-tuning

EVA02 feature/representation model, pretrained on ImageNet-22k via masked image modeling, suitable for image classification and feature extraction tasks.

EVA02 base version visual representation model, pre-trained on ImageNet-22k through masked image modeling, suitable for image classification and feature extraction tasks.

A face classification model fine-tuned based on ResNet-50, achieving an accuracy of 94.16% on the evaluation set

A cartoon face recognition model fine-tuned based on the Google Vision Transformer (ViT) architecture, excelling in image classification tasks

Self-supervised image feature extraction model based on Vision Transformer (ViT), trained using the DINO method

An image classification model based on Vision Transformer (ViT) architecture, pretrained on the ImageNet-21k dataset, suitable for feature extraction and fine-tuning scenarios.

A Vision Transformer (ViT) image feature model trained with self-supervised DINO method, suitable for image classification and feature extraction tasks.

A vision Transformer (ViT) image feature model trained with the self-supervised DINO method, suitable for image classification and feature extraction tasks.

A ResNet-50 model pre-trained using the DINO self-supervised learning method, suitable for visual feature extraction tasks

RegNet is an image classification model designed through neural architecture search, trained on the ImageNet-1k dataset.

RegNet model trained on imagenet-1k, an efficient vision model designed via neural architecture search