{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/3"}],"enrichment":{"capability":"Converts ONNX model files to LiteRT, TensorFlow, PyTorch, TorchScript, and other formats, with support for direct conversion from LiteRT back to PyTorch.","skillfed_tags":["model-conversion","ml-deployment","edge-inference"],"use_cases":["Deploy a PyTorch model to mobile/edge devices by converting to LiteRT or TFLite format.","Migrate a trained TensorFlow model to PyTorch for retraining or fine-tuning in a different framework.","Convert ONNX models (from any framework) to TensorFlow for production serving.","Optimize and simplify ONNX graphs before deployment using the built-in simplification and optimization tools.","Export PyTorch models through ONNX as an intermediate step to reach non-PyTorch runtimes.","Validate model compatibility across frameworks by round-tripping through ONNX and LiteRT."],"what_it_does":"onnx2tf is a model format converter that takes ONNX (Open Neural Network Exchange) files and translates them into multiple target frameworks: LiteRT (Google's edge ML runtime), TensorFlow/TFLite, PyTorch (as native nn.Module code or TorchScript), and other formats. It also works in reverse, converting LiteRT models back to PyTorch. The package uses two execution paths: flatbuffer_direct (the current default, optimized for speed and success rate) and tf_converter (a legacy path supporting a large set of ONNX operators). The tool is designed for developers who need to move trained models between frameworks\u2014for instance, to deploy a PyTorch model on mobile via TFLite, or to run a TensorFlow model in PyTorch training pipelines.\n\nThe package depends on 18 runtime libraries covering ONNX tooling (onnx, onnxruntime, onnxsim, onnxoptimizer, onnxscript), ML frameworks (ai-edge-litert, flatbuffers), and utilities (numpy, opencv-python, protobuf, h5py). It requires Python 3.12 or later and is actively maintained. The conversion success depends on whether your model's layers are in the supported operator list; the documentation lists hundreds of ONNX operators with full or partial support status.","worth_installing":"Yes, if you need to convert ONNX models to LiteRT, TensorFlow, or PyTorch. The package is actively maintained, has low install friction, and covers a broad set of ONNX operators. Check the supported operator list against your model's layers first\u2014partial or missing support for specific ops may require workarounds. MIT license poses no restrictions. Not necessary if you work exclusively within one framework."},"id":"onnx2tf","links":{"html":"https://skillfed.io/packages/onnx2tf","md":"https://skillfed.io/packages/onnx2tf.md","pypi":"https://pypi.org/project/onnx2tf/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-01","license_spdx":"MIT","license_treatment":"permissive","name":"onnx2tf","python_support":"supports_current","summary":"A tool for converting ONNX files to LiteRT/TFLite/TensorFlow, PyTorch native code (nn.Module), TorchScript (.pt), state_dict (.pt), Exported Program (.pt2), and Dynamo ONNX. It also supports direct conversion from LiteRT to PyTorch."},"popularity":{"monthly_downloads":1629877,"position":3706,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"2.6.8"}