Unsupervised　Domain　Adaptive　Object　Detection　(DAOD)　task　can　relax　the　domain　shift　problem　between　source　and　target　domains,　which　requires　to　train　models　on　labeled　source　and　unlabeled　target　domains　jointly.　However,　due　to　limitations　of　data　privacy　protection,　the　source　domain　data　is　usually　inaccessible,　which　poses　significant　challenges　for　the　DAOD　task.　Hence,　Source-Free　Object　Detection　(SFOD)　task　has　been　developed　that　aims　to　fine-tune　a　pre-trained　source　model　with　only　unlabeled　target　domain　data.　Most　of　the　existing　SFOD　methods　are　based　on　pseudo　labeling　using　the　student-teacher　framework,　where　the　teacher　model　is　the　Exponential　Moving　Average　(EMA)　of　the　student　models　in　different　time　steps.　However,　these　methods　always　exist　a　knowledge　bias　problem　due　to　class　imbalance,　and　therefore,　a　fixed　EMA　update　rate　is　no　longer　suitable　for　different　classes.　For　high-quality　classes,　a　fast　EMA　rate　can　accelerate　knowledge　updating　and　promote　model　convergence,　while　for　low-quality　classes,　a　fast　EMA　rate　can　accelerate　the　accumulation　of　knowledge　bias　and　lead　to　the　collapse　of　such　categories.　To　solve　this　problem,　we　propose　a　novel　SFOD　method　called　Slow-Fast　Adaptation　which　develops　two　different　teacher　models,　a　slow　teacher,　and　a　fast　teacher　model,　to　jointly　guide　the　student　training.　The　slow　and　fast　teacher　models　can　provide　richer　supervision　information　and　complement　each　other.　The　experiments　on　four　benchmark　datasets　show　that　our　method　achieves　state-of-the-art　results　and　even　outperforms　DAOD　methods　in　some　cases,　which　demonstrate　the　effectiveness　of　our　method　on　the　SFOD　task.　©　2024　IEEE.