🤗 SMILE

Learning Descriptive Image Captioning via Semipermeable Maximum Likelihood Estimation

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News 📢

• [2023.09.30] We now provide the code and our trained checkpoints (of BLIP) for quick deploying and easy reproduction. The previous demonstrative codes are now available at demonstrative.md.
• [2023.06.26] We provide the demonstrative codes to show how to implement SMILE in your codebase, including a pseudocode, a BLIP version, and a transformers version.

Demo

Try out our online demo integrated into Huggingface Spaces 🤗 using Gradio!

Usage

git clone https://github.com/yuezih/SMILE
cd SMILE/BLIP

Installation

pip install -r requirements.txt

The code has been tested on PyTorch 2.0.0.

Data Preparation

The data configs are in SMILE/BLIP/configs/caption_coco.yaml.

• Set the image_root to your MSCOCO image root.
• MSCOCO annotation files will be automatically downloaded.

Checkpoints

The pre-trained and MLE-finetuned checkpoints are available at the original BLIP repo.

We provide our two checkpoints finetuned on MSCOCO with SMILE:

• blip_smile_base.pth: The vanilla SMILE-optimized BLIP.
• blip_mle_smile_base.pth: BLIP finetuned with MLE+SMILE (0.01:0.99), with a compromise between descriptiveness and accuracy.

Model	Cap. Len.	Lex. Div.	R@1	R@5	CLIPScore	PPL
blip_smile_base.pth	22.3	4.5	10.0	24.5	75.0	95.6
blip_mle_smile_base.pth	19.8	3.6	10.9	25.1	76.2	79.4

They are available at our Huggingface Spaces. You can clone the entire space with the following commands, and then the checkpoints can be found in BLIP-SMILE/model.

# Make sure you have git-lfs installed (https://git-lfs.com)
git lfs install
git clone https://huggingface.co/spaces/yuezih/BLIP-SMILE

We also provide the link to download the checkpoints from OneDrive.

After preparing the checkpoint, Set the checkpoint path in SMILE/BLIP/configs/caption_coco.yaml.

Training & Inference

bash scripts/train.sh

bash scripts/eval.sh

Kind reminders:

• Please use transformers==4.15.0 rather than a higher version.
• For torch<2.0.0, replace torchrun with python -m torch.distributed.run in the training and inference scripts.

Citation

If you find this repo to be helpful for your research, please consider citing our paper:

@misc{yue2023learning,
      title={Learning Descriptive Image Captioning via Semipermeable Maximum Likelihood Estimation}, 
      author={Zihao Yue and Anwen Hu and Liang Zhang and Qin Jin},
      year={2023},
      eprint={2306.13460},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Acknowledgement

Our work relies on resources from BLIP and HuggingFace transformers. Many thanks to them for their amazing efforts.