ReFocus: Visual Editing as a Chain of Thought for Structured Image Understanding
Paper • 2501.05452 • Published • 15
How to use from
SGLangUse Docker images
docker run --gpus all \
--shm-size 32g \
-p 30000:30000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HF_TOKEN=<secret>" \
--ipc=host \
lmsysorg/sglang:latest \
python3 -m sglang.launch_server \
--model-path "ReFocus/Trained_Model" \
--host 0.0.0.0 \
--port 30000# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "ReFocus/Trained_Model",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
ReFocus
This repo contains the model for the paper "ReFocus: Visual Editing as a Chain of Thought for Structured Image Understanding"
Introduction
ReFocus Finetuning
We follow the Phi-3 Cookbook for the supervised finetuning experiments.
Inference with the Finetuned Model
We release our best finetuned ReFocus model with full chain-of-thought data in this HuggingFace Link.
This model is finetuned based on Phi-3.5-vision, and we used the following prompt during evaluation
<|image|>\n{question}\nThought:
To enforce the model to generate bounding box coordinates to refocus, you could try this prompt:
<|image_1|>\n{question}\nThought: The areas to focus on in the image have bounding box coordinates:
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support
Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "ReFocus/Trained_Model" \ --host 0.0.0.0 \ --port 30000# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ReFocus/Trained_Model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'