[ACM MM 2025] Official PyTorch Code for "Uni-Layout: Integrating Human Feedback in Unified Layout Generation and Evaluation"
[2025-09-02]: 🚀 CoT data has been released! You can now find it in the "Dataset for Reward Model" link.
[2025-08-04]: 🎯 Our paper is now available on arXiv! Check it out here: https://arxiv.org/abs/2508.02374.
[2025-07-04]: 🎉 Exciting news! Our paper has been accepted to ACM MM 2025! Stay tuned for more updates!
evaluation.pyrequirements.txtconda create -n caig python==3.8.20 -y && conda activate caig
pip install torch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt
python evaluation.py \
--model_path /path/to/model \
--input_data_path /path/to/input.json \
--output_data_path /path/to/output.json
--model_base, --conv_mode, generation args (--temperature, --top_p, --num_beams, --max_new_tokens, --generate_nums), and process args (--save_interval, --batch_size).image field is optional.sku_id: Anonymized sample identifier.image: Path to the image (optional; may be absent for text-only tasks).conversations: List of two messages:<image> tag, canvas size, element types, and layout constraints.value is a string in the form Layout:{...}, where bounding boxes are [x_min, y_min, x_max, y_max].image: Path to the image.conversations: Single-turn QA pair:value is the Ground Truth label (0 or 1).© JD.COM. All rights reserved. The datasets and code provided in this repository are licensed exclusively for academic research purposes. Commercial use, reproduction, or distribution requires express written permission from JD.COM. Unauthorized commercial use constitutes a violation of these terms and is strictly prohibited.
$ claude mcp add Uni-Layout \
-- python -m otcore.mcp_server <graph>