You can deploy this model and start earning money today!

facebook/
webssl-dino7b-full8b-224

Name: webssl-dino7b-full8b-224
Availability: InStock

transformers

safetensors

dinov2

image-feature-extraction

2504.01017

cc-by-nc-4.0

Views

No views yet

Web-SSL DINO ViT-7B: 8B MetaCLIP data, 224 Resolution

A 7 billion parameter Vision Transformer (ViT) trained with DINOv2 self-supervised learning on web-scale image data without language supervision. Introduced in "Scaling Language-Free Visual Representation Learning" (Fan et al., 2025).

Model Details

Architecture: ViT (4096 width, 32 depth, 32 heads)
Parameters: 7B
Resolution: 224×224 pixels
Training: Self-supervised Web-DINO on 8B image samples from MetaCLIP web data

Model Descriptions

Web-SSL DINO 7B is a 7 billion parameter Vision Transformer model trained using self-supervised learning on 8 billion web images without language supervision. This model demonstrates that pure visual learning, when scaled appropriately, can match or exceed the performance of language-supervised models like CLIP across various vision tasks. It performs well on both traditional vision benchmarks and multimodal tasks including visual question answering and OCR & chart understanding.

Usage

python

from transformers import AutoImageProcessor, Dinov2Model
import torch
from PIL import Image

processor = AutoImageProcessor.from_pretrained('facebook/webssl-dino7b-full8b-224')
model = Dinov2Model.from_pretrained('facebook/webssl-dino7b-full8b-224')

# Process an image
image = Image.open('path/to/image.jpg')
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
    outputs = model(**inputs)

cls_features = outputs.last_hidden_state[:, 0]  # CLS token features
patch_features = outputs.last_hidden_state[:, 1:] # patch-wise token features

Citation

bibtex

@article{fan2025scaling,
  title={Scaling Language-Free Visual Representation Learning}, 
  author={David Fan and Shengbang Tong and Jiachen Zhu and Koustuv Sinha and Zhuang Liu and Xinlei Chen and Michael Rabbat and Nicolas Ballas and Yann LeCun and Amir Bar and Saining Xie},
  year={2025},
  eprint={2504.01017},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}