Deep learning (DL) is becoming increasingly important and widely used in our society. DL projects are mainly built upon DL frameworks, which frequently evolve due to the introduction of new features or bug fixing. Consequently, compatibility issues are commonly seen in DL projects. The compatible framework versions may differ across DL projects, i.e., for a specific framework version, one project runs normally while the other crashes, even if the client code uses the same framework API. Existing studies mainly focus on analyzing the API evolution of Python libraries and the related compatibility issues. However, the difference in framework version compatibility (DFVC) among DL projects has rarely been systematically studied. In this paper, we conduct an empirical study on 90 PyTorch and 50 TensorFlow projects collected from GitHub. By upgrading and downgrading the framework versions, we obtain compatible versions for each project and further investigate the root causes of the different compatible framework versions across projects. We summarize seven root causes: Python version, absence of using the same breaking API, import path, parameter, third-party library, resource, and API usage constraint. We further present six implications based on our empirical findings. Our study can facilitate DL practitioners to gain a better understanding of the DFVC among DL projects.