OmniCaptioner: One Captioner to Rule Them All

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📰 News

We propose OmniCaptioner, a versatile visual captioning framework for generating fine-grained textual descriptions across a wide variety of visual domains. Unlike prior methods limited to specific image types (e.g., natural images or geometric visuals), our framework provides a unified solution for captioning natural images, visual text (e.g., posters, UIs, textbooks), and structured visuals (e.g., documents, tables, charts). By converting low-level pixel information into semantically rich textual representations, our framework bridges the gap between visual and textual modalities. Our results highlight three key advantages: (i) Enhanced Visual Reasoning with LLMs, where long-context captions of visual modalities empower LLMs, particularly the DeepSeek-R1 series, to reason effectively in multimodal scenarios; (ii) Improved Image Generation, where detailed captions improve tasks like text-to-image generation and image transformation; and (iii) Efficient Supervised Fine-Tuning (SFT), which enables faster convergence with less data. We believe the versatility and adaptability of OmniCaptioner can offer a new perspective for bridging the gap between language and visual modalities.

📊 Quantatitive Performance

💻 Finetuning Code

1. Create a conda environment and install PyTorch

conda create -n OmniCap python=3.9
conda activate OmniCap

2.Install dependencies

pip install -r requirements.txt

3. Install flash-attn

pip install flash-attn --no-build-isolation

4. Prepare data

You can place the links to your data files in ./data/caption_data.yaml.

5. Start finetuning

bash scripts/finetune_caption_slurm.sh

🚀 Inference Code

You can caption the image with AIGC style using the following command:

CUDA_VISIBLE_DEVICES=0 python src/inference_single_image.py \
    --model_path your_model_path \
    --image_path your_image_path  \
    --image_type aigc 

You can caption the image with OCR style using the following command:

CUDA_VISIBLE_DEVICES=0 python src/inference_single_image.py \
    --model_path your_model_path \
    --image_path your_image_path  \
    --image_type ocr 

🚀 Evaluation Code with LLM

cd VLMEvalkit
conda create -n VLMEvalkit python=3.9
conda activate VLMEvalkit
pip install -e .

CUDA_VISIBLE_DEVICES=0  nohup python run.py --data MMMU_DEV_VAL --model Omnicaptioner-qwen2-5-3B --verbose > output_omnicap_qwen2-5-3B_MMMU_DEV_VAL.log 2>&1 &
CUDA_VISIBLE_DEVICES=0,1  nohup python run.py --data MMMU_DEV_VAL --model Omnicaptioner-qwen2-5-7B --verbose > output_omnicap_qwen2-5-7B_MMMU_DEV_VAL.log 2>&1 &
CUDA_VISIBLE_DEVICES=0,1,2,3  nohup python run.py --data MMMU_DEV_VAL --model Omnicaptioner-qwen2-5-32B --verbose > output_omnicap_qwen2-5-32B_MMMU_DEV_VAL.log 2>&1 &

CUDA_VISIBLE_DEVICES=0  nohup python run.py --data MMMU_DEV_VAL --model Omnicaptioner-deepseek-distill-7B --verbose > output_omnicap_deepseek_distill_3B_MMMU_DEV_VAL.log 2>&1 &
CUDA_VISIBLE_DEVICES=0,1  nohup python run.py --data MMMU_DEV_VAL --model Omnicaptioner-deepseek-distill-32B --verbose > output_omnicap_deepseek_distill_32B_MMMU_DEV_VAL.log 2>&1 &
CUDA_VISIBLE_DEVICES=0,1,2,3  nohup python run.py --data MMMU_DEV_VAL --model Omnicaptioner-deepseek-distill-70B --verbose > output_omnicap_deepseek_distill_70B_MMMU_DEV_VAL.log 2>&1 &

Citation

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