Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.
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Updated
Sep 6, 2024 - Python
Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.
Self-Created Tools to convert ONNX files (NCHW) to TensorFlow/TFLite/Keras format (NHWC). The purpose of this tool is to solve the massive Transpose extrapolation problem in onnx-tensorflow (onnx-tf). I don't need a Star, but give me a pull request.
TinyNeuralNetwork is an efficient and easy-to-use deep learning model compression framework.
Pytorch to Keras/Tensorflow/TFLite conversion made intuitive
Deep learning model converter for PaddlePaddle. (『飞桨』深度学习模型转换工具)
OpenMMLab Model Deployment Framework
PyTorch to TensorFlow Lite converter
Export Hugging Face models to Core ML and TensorFlow Lite
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.
A very simple script that only initializes the batch size of ONNX. Simple Batchsize Initialization for ONNX.
Very simple NCHW and NHWC conversion tool for ONNX. Change to the specified input order for each and every input OP. Also, change the channel order of RGB and BGR. Simple Channel Converter for ONNX.
A very simple tool to rewrite parameters such as attributes and constants for OPs in ONNX models. Simple Attribute and Constant Modifier for ONNX.
A very simple tool for situations where optimization with onnx-simplifier would exceed the Protocol Buffers upper file size limit of 2GB, or simply to separate onnx files to any size you want.
A simple tool that automatically generates and assigns an OP name to each OP in an old format ONNX file.
Simple ONNX operation generator. Simple Operation Generator for ONNX.
Simple tool to combine(merge) onnx models. Simple Network Combine Tool for ONNX.
A set of simple tools for splitting, merging, OP deletion, size compression, rewriting attributes and constants, OP generation, change opset, change to the specified input order, addition of OP, RGB to BGR conversion, change batch size, batch rename of OP, and JSON convertion for ONNX models.
Convert TensorFlow Lite models (*.tflite) to ONNX.
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