The learning curve is steep: Setting up and using Caffe requires a certain level of deep learning knowledge and experience, so beginners may need to dedicate more time and effort to learn and get started.
Lack of flexibility: Caffe is mainly used for training and inference of convolutional neural networks. It lacks versatility as it does not fully support other types of neural network structures and tasks.
Lack of intuitiveness: Setting up and using Caffe can be relatively complex, requiring more time and effort to complete simple tasks or experiments.
Community support is relatively lacking: Compared to other deep learning frameworks such as TensorFlow and PyTorch, Caffe has a smaller user base and limited community support.
High resource consumption: Caffe requires a high demand for hardware resources, potentially needing more GPUs and memory when training large-scale models.