Yes, TensorFlow Lite can be integrated with other deep learning frameworks. TensorFlow Lite is designed to be flexible and can be used alongside other frameworks to optimize and run deep learning models on resource-constrained devices.

  There are several ways to integrate TensorFlow Lite with other frameworks:

  1. Converting models: TensorFlow Lite provides tools to convert models from popular deep learning frameworks like TensorFlow, Keras, PyTorch, and Caffe into TensorFlow Lite format. This conversion process ensures that the model can be deployed and run efficiently on mobile and embedded devices.

  2. Inference integration: You can integrate TensorFlow Lite for running inference on a model that was trained using a different deep learning framework. This allows you to leverage the optimized inference performance of TensorFlow Lite on mobile and embedded devices.

  3. Custom integrations: TensorFlow Lite also provides APIs that allow you to implement custom integrations with other deep learning frameworks. This gives you the flexibility to construct a tailored solution that meets your specific requirements.

  It's important to note that while TensorFlow Lite can be integrated with other deep learning frameworks, it is specifically designed for deployment on mobile and embedded devices. If you are working on a different platform, there may be other specialized frameworks that better suit your needs.