title: PinPoint
emoji: 😻
colorFrom: gray
colorTo: pink
sdk: static
pinned: false
license: mit
short_description: code for the submission 1386
Pinpoint Counterfactuals: localized gender counterfactual generation (NeurIPS 2025 Datasets and Benchmarks track. Submission 1386)
Getting started
To generate PinPoint Counterfactuals, take the following steps.
Download the data
First, download the FACET and CC3M dataset. Unpack them in the directory of your choice.
Generating PP masks
Use the Color-Invariant-Skin-Segmentation module to generate masks, following the methodology outlined in the main submission manuscript.
In-paint the images
Use the BrushNet module to in-paint the images from FACET and/or CC3M (see the respective scripts in BrushNet/examples/brushnet/inapaint_*.py.
Zero-shot classification
Use the zero_shot_classification.py script to test the occupation classification accuracy of different CLIP models (for different in-painting setups, i.e. PP, PP*, WB, etc.). To run it, first install PyTorch and the following dependencies:
pip install open_clip
pip install git+https://github.com/openai/CLIP.git
pip install tqdm
pip install numpy
pip install pandas
pip install pillow