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A comprehensive toolset for converting, visualizing, and managing deep learning models across multiple frameworks like TensorFlow, PyTorch, and Caffe.
Convert Caffe deep learning models to TensorFlow format for deployment and inference.
An abstraction layer over MetalPerformanceShaders for crafting and running fast neural networks on iOS using TensorFlow models.
Convert PyTorch models to Keras (TensorFlow backend) for deployment and interoperability.
Convert Torch7 neural network models to Apple CoreML format for deployment on iOS/macOS devices.
A collection of Jupyter notebooks demonstrating TensorFlow Lite model quantization, conversion, and optimization techniques for deep neural networks.
An experimental library that converts TensorFlow functions and graphs into JAX functions for reuse and fine-tuning within JAX codebases.
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