DeepImageJ is a user-friendly plugin that enables the generic use in FIJI/ImageJ of pretrained deep learning (DL) models provided by their developers. The plugin acts as a software layer between TensorFlow and FIJI/ImageJ, runs on a standard CPU-based computer and can be used without any DL expertise. Beyond its direct use, we expect DeepImageJ to contribute to the spread and assessment of DL models in life-sciences applications and bioimage informatics.Deep learning (DL) models have a profound impact on a wide range of imaging applications, including life-sciences [1, 2, 3]. Unfortunately, their deployment is often riddled with technical challenges for the non-expert user. Their appropriate use requires insights on sophisticated DL concepts and good programming skills.This situation is in sharp contrast with the philosophy behind ImageJ, the de-facto standard image processing software in life-sciences [4]. This open-source package gives biologists access to a wide variety of user-friendly image analysis tools through third-party plugins and macros.Recent works have aimed at providing a link between TensorFlow 1 and ImageJ 2 . In particular, the CSBDeep team [5], the ImageJ2 team [6], and the Ozcan Research Group [7,8] have pioneered this connection by making their pre-trained TensorFlow models accessible through ImageJ. Unfortunately, this connection effort has remained restricted to their specific applications.We present DeepImageJ 3 , an open-source plugin of ImageJ that runs a variety of third-party TensorFlow models in a generic way. Keras models can also be integrated whenever properly converted to TensorFlow's format SavedModels. DeepImageJ is designed as a standard ImageJ plugin with the technicalities hidden behind a user-friendly interface.