SAILViT-Huge-600M-448px

Introduction

[SAILViT Paper] [BibTeX]

We are pleased to announce the release of SAILViT, a series of versatile vision foundation models of our sota SAIL-VL-1d6-8B and SAIL-VL-1d5-2B. The core philosophy is to mitigate parameter initialization conflicts and modality semantic discrepancies among components by progressive learning before MLLMs perform target training. The main contributions are summarized as follows:

1. SAILViT eliminates multimodal perception performance bottlenecks on sophisticated visual understanding for constructing more powerful MLLMs.
2. We carefully design a training pipeline for gradual feature refinement, which accommodates different vision foundation models in a hierarchical and flexible optimization pattern, and achieves cross-modal alignment and integrated reinforcement of visual representations.
3. We prove the robustness and generalizability of SAILViT via multi-dimensional analyses, including different parameter sizes, model architectures, training strategies, and data scales. SAILViT-Large outperforms the same-sized visual backbones by large margins on diverse tasks, while its Huge version assists MLLMs to achieve significant performance gains.
4. Equipped with the SAILViT-Huge, our SAIL-VL-1d5-2B and SAIL-VL-1d6-8B achieve the #1 ranking in the publicly available OpenCompass Benchmark when compared to VLMs of the same size.

Training Recipes

We progressively inculcate hierarchical multimodal knowledge for the vision foundation model through three training stages in order to enhance visual representations and accomplish a seamless modality-semantic transition with LLMs.

<img src="method.png" alt="Training pipeline of SAILViT"/>

Model Zoo

Evaluation

MLLM Benchmarks

We compared SAILViT with other visual backbones connected with various LLMs on OpenCompass and OpenSource. The SAILViT series has significant performance gains on different families of LLMs.

OpenCompass

<!-- | LLM Series | Visual Backbones Series | Avg. | AI2D (test) | HallusionBench | MMBench (val) | MMMU (val) | MMVet | OCRBench | MMStar | MathVista (testmini) | |--------------------|-------------------------------|------|-------------|----------------|----------------|------------|-------|----------|--------|----------------------| | **InternLM2.5 Series** | | | | | | | | | | | | InternLM2.5-1.8B | InternViT-300M-v2.5 | 49.9 | 69.27 | 33.35 | 65.98 | 35.11 | 29.59 | 713 | 47.80 | 47.1 | | InternLM2.5-1.8B | AIMv2-Large (300M) | 51.2 | 71.53 | 33.02 | 66.45 | 35.33 | 32.34 | 729 | 49.93 | 48.0 | | InternLM2.5-1.8B | SAILViT-Large | 52.4 | 73.09 | 35.32 | 67.80 | 36.78 | 34.40 | 716 | 50.80 | 49.3 | | InternLM2.5-1.8B | AIMv2-Huge (600M) | 51.9 | 72.44 | 29.84 | 68.34 | 33.11 | 34.40 | 759 | 51.47 | 49.7 | | InternLM2.5-1.8B | SAILViT-Huge (600M) | 54.4 | 73.19 | 37.62 | 70.16 | 36.89 | 37.16 | 757 | 53.20 | 51.2 | | **Qwen2.5 Series** | | | | | | | | | | | | Qwen2.5-1.5B | InternViT-300M-v2.5 | 52.2 | 64.02 | 33.56 | 69.16 | 41.56 | 38.72 | 725 | 48.33 | 50.0 | | Qwen2.5-1.5B | AIMv2-Large (300M) | 54.6 | 75.29 | 33.73 | 70.94 | 40.56 | 37.16 | 749 | 53.33 | 50.8 | | Qwen2.5-1.5B | SAILViT-Large | 56.9 | 76.20 | 40.93 | 71.94 | 41.89 | 38.99 | 772 | 53.89 | 53.9 | | Qwen2.5-1.5B | AIMv2-Huge (600M) | 56.3 | 77.66 | 35.44 | 72.33 | 42.56 | 39.72 | 769 | 54.13 | 51.4 | | Qwen2.5-1.5B | SAILViT-Huge (600M) | 57.7 | 78.27 | 37.91 | 73.99 | 43.44 | 40.55 | 795 | 55.60 | 52.1 | | Qwen2.5-7B | InternViT-300M-v2.5 | 62.1 | 81.28 | 44.82 | 77.67 | 49.44 | 43.90 | 784 | 59.27 | 62.2 | | Qwen2.5-7B | AIMv2-Large (300M) | 63.7 | 81.74 | 45.36 | 77.78 | 49.00 | 48.07 | 828 | 60.80 | 64.0 | | Qwen2.5-7B | SAILViT-Large | 64.5 | 82.12 | 45.63 | 78.95 | 51.67 | 49.50 | 805 | 60.87 | 67.1 | | Qwen2.5-7B | AIMv2-Huge (600M) | 64.2 | 81.44 | 44.04 | 80.30 | 50.78 | 46.10 | 815 | 62.33 | 67.4 | | Qwen2.5-7B | SAILViT-Huge (600M) | 65.2 | 83.00 | 48.65 | 79.64 | 50.33 | 49.22 | 833 | 62.60 | 65.2 | | **Qwen3 Series** | | | | | | | | | | | | Qwen3-0.6B | AIMv2-Large (300M) | 51.7 | 71.37 | 38.92 | 65.52 | 35.11 | 33.07 | 703 | 49.80 | 49.2 | | Qwen3-0.6B | SAILViT-Large (300M) | 52.9 | 71.05 | 41.05 | 64.86 | 36.67 | 34.77 | 741 | 50.93 | 49.4 | | Qwen3-1.7B | AIMv2-Large (300M) | 56.3 | 77.49 | 39.76 | 71.28 | 42.78 | 39.17 | 751 | 53.40 | 51.4 | | Qwen3-1.7B | SAILViT-Large (300M) | 58.1 | 78.82 | 41.84 | 71.44 | 42.56 | 43.21 | 790 | 54.80 | 52.8 | | Qwen3-1.7B | AIMv2-Huge (600M) | 57.7 | 79.24 | 40.44 | 71.09 | 42.00 | 43.07 | 781 | 54.80 | 52.9 | | Qwen3-1.7B | SAILViT-Huge (600M) | 59.4 | 79.89 | 41.23 | 74.19 | 43.11 | 44.31 | 806 | 56.93 | 54.9 | | Qwen3-8B | AIMv2-Huge (600M) | 66.0 | 83.35 | 43.50 | 80.92 | 52.78 | 50.05 | 839 | 65.60 | 67.8 | | Qwen3-8B | SAILViT-Huge (600M) | 66.6 | 84.17 | 48.23 | 81.89 | 51.78 | 46.83 | 857 | 65.13 | 69.4 | --> <img src="Results of VLM OC.png" />

OpenSource

<img src="Results of VLM OS.png" />

Visual perception tasks

We conducted a comparison of SAILViT against other visual backbones in visual recognition tasks, specifically focusing on image classification. Under strictly fair experimental conditions, our findings show that SAILViT outperforms peer models of similar scale, such as Aimv2 and InternViT. Impressively, SAILViT-huge even matches the performance of InternViT-6B on challenging benchmarks like ImageNet-R and ImageNet-v2.

Usage

import torch
import torchvision.transforms as T
from PIL import Image
import requests
from torchvision.transforms.functional import InterpolationMode
from transformers import AutoModel, AutoTokenizer

IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)
def build_transform(input_size):
    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
    transform = T.Compose([
        T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
        T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
        T.ToTensor(),
        T.Normalize(mean=MEAN, std=STD)
    ])
    return transform

path = "BytedanceDouyinContent/SAILViT-Huge-600M-448px"
model = AutoModel.from_pretrained(
    path,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True).eval().cuda()

url = "https://img-blog.csdnimg.cn/fcc22710385e4edabccf2451d5f64a99.jpeg"
input_size = 448
image = Image.open(requests.get(url, stream=True).raw).convert('RGB')
transform = build_transform(input_size=input_size)
pixel_values = transform(image).unsqueeze(0).to(torch.bfloat16).cuda()
visual_tokens = model(pixel_values=pixel_values)

Acknowledgments

Our model is built upon Aimv2 and Ovis2 and we are grateful for their contributions.

Citation

If you find our work useful, please consider citing us as :

@article{Wei2025SAILViT,
  title={SAILViT: Towards Robust and Generalizable Visual Backbones for MLLMs via Gradual Feature Refinement},
  author={Yin, Weijie and Yang, Dingkang and Dong, Hongyuan and Kang, Zijian and Wang, Jiacong and Liang, Xiao and Feng, Chao and Ran, Jiao},
  journal={arXiv preprint arXiv:2507.01643},
  year={2025}
}

Licence

This project is licensed under Apache License 2.0.

Contact

If you have any question, please feel free to contact us: [email protected]