ssc4onnx
Checker with simple ONNX model structure. Simple Structure Checker for ONNX.
Decision gist · record as of 2026-08-14
Yes, if you need to inspect large ONNX models and Netron is insufficient. The low install friction and permissive license make it a low-risk addition. However, dormant maintenance (last update over 1055 days ago) means you should verify compatibility with your ONNX opset version before relying on it in production pipelines.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.6 or later; ONNX file must be a valid ONNX model.
- Low install friction; pure Python wheel with no runtime dependencies.
- Maintenance is dormant—last release was over 1055 days ago, though the repository remains active and unarchived.
License · maintenance · safety
MIT License (permissive) — MIT License permits commercial and private use with minimal restrictions, making it safe to adopt in most projects.
last release 2023-09-24 (1055 days) · last repo commit 2023-09-24 · 8 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 82,848 downloads/mo, #14,126 on PyPI
Alternatives
Verify before relying
pip install ssc4onnx
from ssc4onnx import structure_check
op_num, model_size = structure_check(
input_onnx_file_path="model.onnx"
)- Whether the package works correctly with recent ONNX opset versions given the dormant maintenance status.
- Performance characteristics when analyzing very large models (exact size thresholds not specified).
What it is and what it does
ssc4onnx is a command-line and Python library tool for inspecting the structure of ONNX neural network models. It addresses a specific gap: when models are too large or complex for visual inspection tools like Netron, ssc4onnx provides a text-based breakdown of the model's operations and total byte size. The tool works both as a CLI utility (via `ssc4onnx -if model.onnx`) and as a Python function that accepts either a file path or an in-memory ONNX ModelProto object.
The package has no runtime dependencies beyond Python itself, making installation straightforward. It returns operation counts by type and the model's byte size, useful for understanding model complexity and identifying bottlenecks before deployment. The codebase is part of a larger ONNX processing toolkit maintained on GitHub, though the package itself has not been updated in over 1055 days.
Use it for
- Inspect the operation breakdown of large ONNX models that cannot be visualized with Netron.
- Verify model structure and operation counts programmatically before deploying to inference engines.
- Analyze model size and complexity as part of model optimization workflows.
- Debug ONNX graph structure issues by examining operation types and counts in text form.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need to inspect large ONNX models and Netron is insufficient.
The low install friction and permissive license make it a low-risk addition. However, dormant maintenance (last update over 1055 days ago) means you should verify compatibility with your ONNX opset version before relying on it in production pipelines.
Install
ssc4onnx on PyPI
Before you install
Low install friction; pure Python wheel with no runtime dependencies. Maintenance is dormant—last release was over 1055 days ago, though the repository remains active and unarchived.
Requires Python 3.6 or later; ONNX file must be a valid ONNX model.
License in practice
MIT License permits commercial and private use with minimal restrictions, making it safe to adopt in most projects.
Quickstart
pip install ssc4onnx
from ssc4onnx import structure_check
op_num, model_size = structure_check(
input_onnx_file_path="model.onnx"
)Verify before relying
- Whether the package works correctly with recent ONNX opset versions given the dormant maintenance status.
- Performance characteristics when analyzing very large models (exact size thresholds not specified).
Package facts
| License | MIT License permissive |
| Python support | Supports the current Python release >=3.6 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Dormant 1,055 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 82,848 / month, #14,126 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: ssc4onnx-1.0.8-py3-none-any.whl
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See also ssi4onnx · snd4onnx · sne4onnx · sit4onnx · scs4onnx · soa4onnx · sng4onnx · sod4onnx · sbi4onnx · onnx2json