EditCLEVR Phase 1 EditCLEVR is a synthetic benchmark for evaluating intervention faithfulness in object centric representations. Each example is a paired before/after scene where exactly one object level factor may change. Dataset summary ~20k paired edits across six evaluation splits Suites: atomic single factor edits, no edit controls, hard distractors, and CoGenT OOD combinations Per object factors: color , material , size , shape Artifacts per pair: before/after RGB images, instance masks ( .npz ), scene JSON, object attributes, edit metadata, and difficulty tags Splits Split Purpose train Probe training val Validation test id In distribution atomic edits test noop No edit control pairs test hard Hard distractor edits test cogent CoGenT OOD edits Download The dataset ships as a small set of .tar.gz archives (one per suite plus a splits bundle). The helpers below download and extract them into the original directory layout automatically. Or from Python: Evaluate File layout splits.json stores relative paths to images, masks, and scene JSON files so the dataset can be moved across machines. License Dataset: CC BY 4.0 Code: MIT Citation If you use this dataset, please cite: Anuraa…
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