{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/11"}],"enrichment":{"capability":"Converts ONNX model input tensor dimensions and channel ordering (NCHW \u2194 NHWC, RGB \u2194 BGR) by rewriting input operations and inserting compensating transposes.","skillfed_tags":["onnx-tools","model-conversion","tensor-layout"],"use_cases":["Convert a computer vision model from NCHW to NHWC format to match a different inference framework's input convention.","Swap RGB to BGR channel ordering on model inputs to align with preprocessing pipelines that use different color spaces.","Adapt stereo or multi-input models by reordering dimensions on multiple named inputs simultaneously.","Prepare ONNX models for deployment on hardware or frameworks that expect specific tensor layouts.","Batch-process multiple ONNX models via CLI to standardize input formats across a model zoo."],"what_it_does":"scc4onnx is a command-line and Python library tool for modifying ONNX model input specifications. It rewrites the dimension order of named input operations and optionally swaps RGB/BGR channel ordering, then inserts Transpose operations after the input to preserve downstream model behavior. The tool works by accepting a source ONNX file, applying the specified transformations, and writing a modified model file\u2014or by accepting an in-memory ONNX graph object for programmatic use.\n\nThe package supports arbitrary tensor dimensions (not limited to 4D), allowing flexible reordering of any axis. It is typically used when adapting pre-trained models to different input conventions\u2014for example, converting a model trained on NCHW-format images to accept NHWC input, or swapping color channel order to match a different preprocessing pipeline. The transformations are non-destructive: the model's internal computation graph remains unchanged, with the transpose operations ensuring outputs remain semantically identical.","worth_installing":"Yes, if you work with ONNX models and need to adapt input tensor layouts or channel ordering. The package is lightweight, has no runtime dependencies, and carries permissive licensing. The aging maintenance status (last release 487 days ago) is a minor concern for a stable utility tool, but check whether your ONNX and onnx_graphsurgeon versions are compatible before relying on it in production."},"id":"scc4onnx","links":{"html":"https://skillfed.io/packages/scc4onnx","md":"https://skillfed.io/packages/scc4onnx.md","pypi":"https://pypi.org/project/scc4onnx/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-04-14","license_spdx":null,"license_treatment":"permissive","name":"scc4onnx","python_support":"supports_current","summary":"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. Simple Channel Conversion for ONNX."},"popularity":{"monthly_downloads":82968,"position":14114,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.0.7"}