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MM-MVR/
UniViTAR-0.3B

Name: UniViTAR-0.3B
Availability: InStock

transformers

pytorch

feature-extraction

mlfoundations/datacomp_1b

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Unified Vision Transformer with Native Resolution

🌠 Introduction

We present UniViTAR, a family of homogeneous vision foundation models tailored for unified visual modality and native resolution scenario in the era of multimodal. We train our UniViTAR family across multiple model scales from 0.3B to 1.4B exclusively on public accessible image-caption data (14.6B), and observe a trend of performance increasing with parameter scaling. UniViTAR is a Transformer-based encoder model that inherits the original architecture of the conventional Vision Transformer but incorporates the following advanced modifications: Unified Patchify for Native Image and Video Modality, 2D RoPE, SwiGLU, RMSNorm, and QK-Norm.

🛠️ Environment

bash

conda create -n univitar python=3.11 -y
conda activate univitar
pip3 install einops==0.8.0 ninja==1.11.1.1 numpy==1.26.4 pillow==10.4.0 psutil==6.0.0 torch==2.2.2 torchvision==0.17.2 transformers==4.49.0 timm==1.0.14
pip3 install flash-attn==2.6.3

🗝️ Model Usage

python

import torch
import numpy as np
from PIL import Image
from modeling_univitar import UniViTARVisionModel

# Prepare Model
model = UniViTARVisionModel("config.json")
_ = model.load_state_dict(torch.load(f"pytorch_model.bin", map_location="cpu"))
model = model.to(torch.bfloat16).cuda()

# Prepare Data: [(3, H1, W1), ..., (3, Hn, Wn)] --> (N1+...+Nn, P)
images = [Image.open(f"xx1.jpg"), Image.open(f"xx2.jpg")]
data_inputs, grid_shapes = [], []
for image in images:
    data_item = model.image_transform(image)
    input_data, grid_shape = model.data_patchify(data_item)
    data_inputs.append(input_data.to(torch.bfloat16).cuda())
    grid_shapes.append(grid_shape)
data_inputs = torch.concatenate(data_inputs, dim=0)

# Forward: (N1+...+Nn, P) --> [(N1, D), ..., (Nn, D)]
data_embeds = model(pixel_values=data_inputs, grid_shapes=grid_shapes)
data_embeds = data_embeds.split([np.prod(grid_shape) for grid_shape in grid_shapes])
print(data_embeds[0].shape, data_embeds[1].shape)

📈 Evaluation

Model	Size	#Seen	IN1K^ZS	IN1K^LP	Flickr^T2I	Flickr^I2T	K400^ZS	ADE20K
UniViTAR-0.3B	310M	14.6B	81.5	87.7	84.0	95.1	66.0	54.6
UniViTAR-0.6B	637M	14.6B	82.3	88.3	84.1	95.5	68.6	55.1
UniViTAR-1B	1419M	14.6B	82.9	89.2	83.5	95.1	69.0	56.2

ZS: Zero-shot Classification, LP: Linear-Probe Classification, T2I/I2T: Text-to-Image/Image-to-Text Retrieval

✏️ Reference

If you find UniViTAR useful in your research or applications, please consider citing the following BibTeX:

@article{qiao2025univitar,
title={UniViTAR: Unified Vision Transformer with Native Resolution},
author={Qiao, Limeng and Gan, Yiyang and Wang, Bairui and Qin, Jie and Xu, Shuang and Yang, Siqi and Ma, Lin},
journal={arXiv preprint arXiv:2504.01792},
year={2025}
}

MM-MVR/UniViTAR-0.3B