X²-VLM with a modular architecture performs the best on base and large scale for both image-text and video-text tasks, making a good trade-off between performance and model scale. We also show that the modular design of X²-VLM results in high transferability for X²-VLM to be utilized in any language or domain. For example, by simply replacing the text encoder with XLM-R, X-VLM outperforms state-of-the-art multilingual multi-modal pre-trained models without any multilingual pre-training.

• Jun 2023: Release official PyTorch implementation and checkpoints
• Nov 2022: Release preprint in arxiv.

X²-VLM (large, 593M params):

• Support several backbones
  • vision encoder: beit / clip-vit / swin-transformer
  • text encoder: bert / roberta
• Support apex O1 / O2 for pre-training
• Read from and write to HDFS
• Distributed training across nodes for both pre-training and fine-tuning

Please read the code for more details.

• Install python3 environment

pip3 install -r requirements.txt

• Download raw images from corresponding websites
• Download the json files we provided, which contains image read paths and captions and/or bbox annotations
• If running pre-training scripts:
  • install Apex
  • download pre-trained models for parameter initialization
    • image encoder: beit2
    • text encoder: bert

# X-VLM pretrain 
python3 run.py --task "pretrain_DIY" --dist "all" --config "configs/pretrain/x2vlm_base_4m.yaml"  --output_dir "output/tmp"

# CCLM multilingual multimodal pretrain 
python3 run.py --task "pretrain_DIY" --dist "all" --config "configs/pretrain/multilingual_cclm_x2vlm_base.yaml" --checkpoint "path/to/x2vlm_base_1b.th"  --output_dir "output/tmp"

See run.py and configs/pretrain for more details.

All datasets we utilized are public available. Please prepare the pre-training data by yourself. Read the code dataset/pretrain_dataset.py (more specifically ImageTextJsonDataset & RegionTextJsonDataset) to see what format is needed.

The processed COCO & VG annotations can be downloaded here.

Please make sure all parameters are loaded correctly.
X2VLM-base (4M)
X2VLM-large (4M)
X2VLM-base (1B)
CCLM-X2VLM-base

All datasets are publicly available. Some datasets can be downloaded here.

We have released all codes. However, now we only provide parts of fine-tuned ckpts (and training configs and logs).
vqa-base
vqa-large
refcoco-bbox-large
It takes time for us to retrieve our previous training logs. If you need more, please submit a Github issue and we will return to your request later.
coco-retrieval-base-rerun
coco-retrieval-large-rerun

# train

python3 run.py --task "vqa" --dist "all" --config "configs/finetune/vqa2_large.yaml" --checkpoint "x2vlm_ckpts_2release/x2vlm_large_1b.th"  --output_dir "output/tmp"

python3 run.py --task "refcoco_bbox" --dist "all" --config "configs/finetune/refcoco_grounding_large.yaml" --checkpoint "x2vlm_ckpts_2release/x2vlm_large_1b.th"  --output_dir "output/tmp"

python3 run.py --task "coco_captioning_mlm" --dist "all" --config "configs/finetune/coco_captioning_large.yaml" --checkpoint "x2vlm_ckpts_2release/x2vlm_large_1b.th"  --output_dir "output/tmp"

We release all training codes. Specify "--task" and "--config" to finetune on other tasks. See run.py for details.

If you find this repository useful, please considering giving ⭐ or citing:

@article{zeng2022x,
  title={X $\^{} 2$-VLM: All-In-One Pre-trained Model For Vision-Language Tasks},
  author={Zeng, Yan and Zhang, Xinsong and Li, Hang and Wang, Jiawei and Zhang, Jipeng and Zhou, Wangchunshu},
  journal={arXiv preprint arXiv:2211.12402},
  year={2022}
}

@article{zeng2022cross,
  title={Cross-view language modeling: Towards unified cross-lingual cross-modal pre-training},
  author={Zeng, Yan and Zhou, Wangchunshu and Luo, Ao and Zhang, Xinsong},
  journal={arXiv preprint arXiv:2206.00621},
  year={2022}
}

@article{xvlm,
  title={Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts},
  author={Zeng, Yan and Zhang, Xinsong and Li, Hang},
  journal={arXiv preprint arXiv:2111.08276},
  year={2021}
}

For issues using this code, please submit a GitHub issue.