This is a vision transformer model pre-trained on DINOv2, specifically optimized for retinal color fundus photos. The model is trained on a subset of the MEH AlzEye dataset and is provided in a format compatible with Hugging Face Transformers, facilitating retinal image feature extraction and downstream medical imaging tasks.

Computer VisionTransformersOther

This is a license plate detection model fine - tuned based on the DETR architecture and the DINOv3 visual backbone network. It achieved a loss value of 2.7008 on the evaluation set and is specifically designed for license plate recognition tasks.

Computer VisionTransformers

This is a vision Transformer model based on the DINOv3 framework, trained on the LVD-1689M dataset from the DINOv3 ViT-7B model through knowledge distillation technology. This model is specifically designed for image feature encoding and can efficiently extract image feature representations, suitable for various computer vision tasks.

Computer VisionTimm

This is a vision Transformer model based on the DINOv3 architecture, using a small configuration and trained through knowledge distillation on the LVD-1689M dataset. This model is specifically designed for efficient image feature extraction and supports various computer vision tasks such as image classification, feature map extraction, and image embedding.

Computer VisionTimm

This is a Vision Transformer image feature encoder based on the DINOv3 architecture, obtained through knowledge distillation from the 7B-parameter DINOv3 ViT model on the LVD-1689M dataset. This model is specifically designed for image feature extraction tasks and has strong visual representation capabilities.

Computer VisionTimm

DINOv3 is a series of general-purpose visual foundation models that can outperform specialized state-of-the-art techniques on a wide range of visual tasks without fine-tuning. This model can generate high-quality dense features and performs excellently in various visual tasks, significantly surpassing previous self-supervised and weakly-supervised foundation models.

Computer VisionTransformersEnglish

DINOv3 is a series of general visual foundation models developed by Meta AI. Without fine-tuning, it can outperform specialized state-of-the-art models in a wide range of visual tasks. This model uses self-supervised learning to generate high-quality dense features and performs excellently in various tasks such as image classification, segmentation, and depth estimation.

Computer VisionTransformersEnglish

DINOv3 is a versatile visual foundation model developed by Meta AI. It can outperform specialized models in a wide range of visual tasks without fine-tuning. This model can generate high-quality dense features and performs excellently in various visual tasks, significantly surpassing previous self-supervised and weakly supervised foundation models.

Computer VisionTransformersEnglish

DINOv3 is a series of general visual foundation models developed by Meta AI. It can outperform specialized advanced models in various visual tasks without fine-tuning. The model adopts the Vision Transformer architecture and is pre-trained on 1.689 billion web images. It can generate high-quality dense features and performs excellently in tasks such as image classification, segmentation, and retrieval.

Computer VisionTransformersEnglish

DINOv3 is a series of general-purpose visual foundation models that can outperform specialized state-of-the-art models on a wide range of visual tasks without fine-tuning. The model uses self-supervised learning to generate high-quality dense features and performs excellently in various visual tasks, significantly surpassing previous self-supervised and weakly supervised foundation models.

Computer VisionTransformersEnglish

DINOv3 is a series of general visual foundation models that can outperform specialized state-of-the-art techniques in a wide range of visual tasks without fine-tuning. The model generates high-quality dense features through self-supervised learning and performs excellently in various visual tasks, significantly surpassing previous self-supervised and weakly supervised foundation models.

Computer VisionTransformersEnglish

This model is a fine-tuned version based on facebook/dinov2-large, which has been optimized and trained on specific tasks.

Computer VisionTransformers

ONNX format version of DINOv2-small, suitable for vision tasks

Computer VisionTransformers

A 7-billion-parameter visual Transformer model trained on 8 billion MetaCLIP data using the DINOv2 self-supervised learning framework, requiring no language supervision

Computer VisionTransformers

This is a 1-billion-parameter vision Transformer model trained on 2 billion web images through DINOv2 self-supervised learning, capable of learning visual representations without language supervision.

Computer VisionTransformers

A 2-billion-parameter vision Transformer model trained using the DINOv2 self-supervised learning framework on lightly filtered web-scale image data (without language supervision).

Computer VisionTransformers

This is a 3-billion parameter vision Transformer model trained on 2 billion web images through DINOv2 self-supervised learning, capable of learning powerful visual representations without language supervision.

Computer VisionTransformers

A 224-resolution Vision Transformer model based on 2 billion MetaCLIP data, trained using DINOv2 self-supervised learning method

Computer VisionTransformers

A 3-billion parameter Vision Transformer model trained using DINOv2 self-supervised learning on lightly filtered web-scale image data, without language supervision.

Computer VisionTransformers

A 3-billion parameter vision Transformer model trained on 2 billion carefully curated MetaCLIP data using DINOv2 self-supervised learning framework

Computer VisionTransformers