image_pair_similarity_filter

To filter image pairs by similarity score, you can use a CLIP model to compute scores and apply a minimum and maximum range threshold to your JSONL dataset. This process outputs a new file containing only the image pairs that match your specified similarity criteria.

Computing image similarity for content moderation is best handled by using a CLIP model to evaluate pairs and filter them based on a defined score range. This approach allows you to systematically manage visual content by keeping or discarding pairs through 'any' or 'all' filtering strategies.

Yes, you need the data_juicer library along with a CLIP model to calculate image pair similarity. These dependencies are required to process the JSON/JSONL input files and execute the filtering logic to accurately score and manage your visual content.

The 'any' or 'all' filtering strategy determines whether an image pair is kept based on the computed CLIP similarity scores. The 'any' strategy keeps pairs if any score falls within the range, while the 'all' strategy requires all evaluated scores to meet the minimum and maximum threshold criteria.

Yes, you can set a custom similarity range with specific minimum and maximum scores to organize similar images in a database. By defining these thresholds, the filtering process will isolate and output only the image pairs that fall within your target similarity window.