模型 92

We introduce the AIMv2 family of vision models pre-trained with a multimodal autoregressive objective. AIMv2 pre-training is simple and stra…

We introduce the AIMv2 family of vision models pre-trained with a multimodal autoregressive objective. AIMv2 pre-training is simple and stra…

We introduce the AIMv2 family of vision models pre-trained with a multimodal autoregressive objective. AIMv2 pre-training is simple and stra…

BEiT model pre-trained in a self-supervised fashion on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tune…

BioViL-T is a domain-specific vision-language model designed to analyze chest X-rays (CXRs) and radiology reports. It was trained using a te…

[**Github**] [**CVPR 2025**] [**CVPR 2024**]

python=3.10 torch==2.7.1 # may work with more recent version torchvision==0.22.1 transformers==4.40.0 opencv-python albumentations accelerat…

Disclaimer: The model card is taken and modified from the official CLIP repository, it can be found here.

Disclaimer: The model card is taken and modified from the official CLIP repository, it can be found here.

ConvNeXt V2 model pretrained using the FCMAE framework and fine-tuned on the ImageNet-22K dataset at resolution 512x512. It was introduced i…

ConvNeXt V2 model pretrained using the FCMAE framework and fine-tuned on the ImageNet-22K dataset at resolution 224x224. It was introduced i…

Depth Anything V2 is trained from 595K synthetic labeled images and 62M+ real unlabeled images, providing the most capable monocular depth e…

Depth Anything V2 is trained from 595K synthetic labeled images and 62M+ real unlabeled images, providing the most capable monocular depth e…

Accept the access terms above and log in once with `huggingface-cli login`. Then clone the Derm1M repository: git clone git@github.com:Siyua…

Accept the access terms above and log in once with `huggingface-cli login`. Then clone the Derm1M repository: git clone git@github.com:Siyua…

Vision Transformer (ViT) model trained using the DINOv2 method. It was introduced in the paper DINOv2: Learning Robust Visual Features witho…

Vision Transformer (ViT) model trained using the DINOv2 method. It was introduced in the paper DINOv2: Learning Robust Visual Features witho…

Vision Transformer (ViT) model trained using the DINOv2 method. It was introduced in the paper DINOv2: Learning Robust Visual Features witho…

Vision Transformer (ViT) model introduced in the paper Vision Transformers Need Registers by Darcet et al. and first released in this reposi…

Vision Transformer (ViT) model introduced in the paper Vision Transformers Need Registers by Darcet et al. and first released in this reposi…

Vision Transformer (ViT) model introduced in the paper Vision Transformers Need Registers by Darcet et al. and first released in this reposi…

Vision Transformer (ViT) model introduced in the paper Vision Transformers Need Registers by Darcet et al. and first released in this reposi…

Vision Transformer (ViT) model trained using the DINOv2 method. It was introduced in the paper DINOv2: Learning Robust Visual Features witho…

AIMI FMs: A Collection of Foundation Models in Radiology

DINOv3 is a family of versatile vision foundation models that outperforms the specialized state of the art across a broad range of settings,…

DINOv3 is a family of versatile vision foundation models that outperforms the specialized state of the art across a broad range of settings,…

DINOv3 is a family of versatile vision foundation models that outperforms the specialized state of the art across a broad range of settings,…

DINOv3 is a family of versatile vision foundation models that outperforms the specialized state of the art across a broad range of settings,…

DINOv3 is a family of versatile vision foundation models that outperforms the specialized state of the art across a broad range of settings,…

DINOv3 is a family of versatile vision foundation models that outperforms the specialized state of the art across a broad range of settings,…