Repository for the Partonomy-Core benchmark introduced in PARTONOMY: Large Multimodal Models with Part-Level Visual Understanding.
Code can be found at https://github.com/anselblume/partonomy.
The images directory contains all images used in the Partonomy-Core evaluation. Evaluation questions, answer choices, and corresponding parts for each answer choice are contained in partonomy_qa_pairs.json. Each question-answer JSON object contains the following fields:
image_path(str): Relative path to the image corresponding to the question.image_label(str): Label for the object in the image.question(str): The LMM prompt used for evaluation of parts.question_type(str): The type of question. These include identification_with_label (identification), positive_with_label (intersection), negative_with_label (difference), whole_to_part (Whole-to-Part), and part_to_whole (Part-to-Whole).answer_choices(list[str]): The multiple choice answer choices to evaluate the model with, using each answer choice individually as the model's response to the question prompt.answer_types(list[str]): The correctness of each answer choice in theanswer_choicesfield.answer_parts(list[list[str]]): List containing each of the part names sampled for each answer choice.segmentations(dict): Segmentation masks for each part, where keys are the part names. Segmentation mask objects for each part can be decoded into binary masks usingpycocotools.mask.decode.object_question(str): Analogue ofquestionLLM prompt, used for evaluation of the object label. Only present for Whole-to-Part and Part-to-Whole questions.object_answer_choices(list[str]): Answer choices for theobject_questionprompt. Only present for Whole-to-Part and Part-to-Whole questions.object_answer_types(list[str]): The correctness of each answer choice in theobject_answer_choicesfield. Only present for Whole-to-Part and Part-to-Whole questions.object_answer_classes(list[str]): The object class used for each answer choice inobject_answer_choices.