---
license: cc-by-4.0
---

# Model Card for LoTLIP ViT-B/32

## Model Details

### Model Description

LoTLIP ViT-B/32 model pre-trained on 100M scale dataset.



### Direct Use

Zero-shot long text-image retrieval, short text-image retrieval, and image classification, among others.


## How to Get Started with the Model

Use the [code](https://github.com/wuw2019/LoTLIP) to get started with the model.


## Training Details

### Training Data

The models are trained with 100M scale dataset which contains long text-image pairs.


## Evaluation

Please refer to https://github.com/wuw2019/LoTLIP.

### Testing Details

#### Testing Data

The testing is performed with [DCI](https://github.com/facebookresearch/DCI), [IIW](https://github.com/google/imageinwords/) and [ShareGPT4V](https://sharegpt4v.github.io/) for long text-image retrieval and ImageNet1k for classification.


### Results

| Model |Pre-training Data Scale   | DCI I2T | DCI T2I| IIW I2T |IIW T2I| SV-10k I2T | SV-10k T2I |
|  :----: | :----: | :----: | :----: | :----: | :----: | :----: | :----: |
| LoTLIP-ViT-B-32 |  100M | 59.90 | 56.36 | 93.14| 91.83 | 83.76 | 78.97|





## Citation


BibTeX:

```bibtex
@inproceedings{LoTLIP,
  title={LoTLIP: Improving Language-Image Pre-training for Long Text Understanding},
  author={Wu, Wei and Zheng, Kecheng and Ma, Shuailei and Lu, Fan and Guo, Yuxin and Zhang, Yifei and Chen, Wei and Guo, Qingpei and Shen, Yujun and Zheng-Jun, Zha},
  booktitle={arXiv},
  year={2024}
}
```