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license: cc-by-4.0 |
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# Model Card for LoTLIP ViT-B/32 |
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## Model Details |
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### Model Description |
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LoTLIP ViT-B/32 model pre-trained on 100M scale dataset. |
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### Direct Use |
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Zero-shot long text-image retrieval, short text-image retrieval, and image classification, among others. |
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## How to Get Started with the Model |
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Use the [code](https://github.com/wuw2019/LoTLIP) to get started with the model. |
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## Training Details |
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### Training Data |
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The models are trained with 100M scale dataset which contains long text-image pairs. |
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## Evaluation |
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Please refer to https://github.com/wuw2019/LoTLIP. |
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### Testing Details |
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#### Testing Data |
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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. |
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### Results |
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| Model |Pre-training Data Scale | DCI I2T | DCI T2I| IIW I2T |IIW T2I| SV-10k I2T | SV-10k T2I | |
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| :----: | :----: | :----: | :----: | :----: | :----: | :----: | :----: | |
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| LoTLIP-ViT-B-32 | 100M | 59.90 | 56.36 | 93.14| 91.83 | 83.76 | 78.97| |
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## Citation |
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BibTeX: |
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```bibtex |
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@inproceedings{LoTLIP, |
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title={LoTLIP: Improving Language-Image Pre-training for Long Text Understanding}, |
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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}, |
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booktitle={arXiv}, |
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year={2024} |
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} |
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``` |
