# Lightweight ViT

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

A vision Transformer model based on the LeViT-128 architecture, fine-tuned for binary stroke detection tasks

H0-mini is a lightweight histology foundation model jointly developed by Owkin and Bioptimus, based on the Vision Transformer architecture, trained via self-supervised distillation, and suitable for pathological image analysis.

A lightweight image classification model based on ViT architecture, specifically designed to detect differences between AI-generated images and real images

A Vision Transformer (ViT) model trained on the MS1MV3 dataset, specifically designed for face recognition tasks.

A Vision Transformer (ViT) model trained on the MS1MV3 dataset, specifically designed for face recognition tasks

A small vision Transformer model trained using the DINOv2 method, suitable for image feature extraction and classification tasks

This model is a fine-tuned version of apple/deeplabv3-mobilevit-small on the Efferbach/lane_master2 dataset for image segmentation tasks.

PaViT is an image recognition model based on Pathway Vision Transformer, inspired by Google's PaLM, focusing on the application of few-shot learning techniques in image recognition tasks.

This model is a distilled version of the Data-efficient image Transformer (DeiT), pretrained and fine-tuned on ImageNet-1k at 224x224 resolution, efficiently learning from a teacher model through distillation.