{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/10"}],"enrichment":{"capability":"Provides custom neural network layers for detection post-processing that are not available in TensorFlow Keras or PyTorch's standard APIs, enabling integration into pretrained models.","skillfed_tags":["detection-layers","edge-ml","onnx-export"],"use_cases":["Add detection post-processing layers to TensorFlow models for edge inference without writing custom operations.","Export PyTorch detection models to ONNX with custom layers preserved for OnnxRuntime deployment.","Integrate specialized detection post-processing into pretrained models when standard framework layers are insufficient.","Serialize and load detection models with custom layers using TensorFlow's .keras format or ONNX.","Support edge ML pipelines that require detection post-processing not available in mainstream framework APIs."],"what_it_does":"Edge MDT Custom Layers fills a gap in standard deep learning frameworks by providing detection post-processing layers that TensorFlow Keras and PyTorch do not natively support. It allows integration of these custom layers into pretrained models for edge deployment scenarios. The package supports both TensorFlow (with .keras serialization) and PyTorch (with ONNX export and OnnxRuntime support), making it useful for workflows that need specialized detection post-processing without building custom operations from scratch.\n\nThe package depends on numpy and packaging, and installs as a pure Python wheel with optional framework-specific dependencies. It is designed for Python >=3.10, with tested support for TensorFlow 2.14\u20132.15 and PyTorch 2.3\u20132.6. The codebase is permissively licensed under Apache License 2.0, but maintenance activity has slowed\u2014the last release was 200 days ago\u2014so adoption should account for potential delays in bug fixes or compatibility updates.","worth_installing":"Yes, if you need detection post-processing layers not available in standard TensorFlow or PyTorch APIs and are comfortable with a package in aging maintenance status. The low install friction, permissive license, and zero known vulnerabilities make it safe to try. However, verify that the specific layers you need are supported and plan for potential delays in updates or bug fixes."},"id":"edge-mdt-cl","links":{"html":"https://skillfed.io/packages/edge-mdt-cl","md":"https://skillfed.io/packages/edge-mdt-cl.md","pypi":"https://pypi.org/project/edge-mdt-cl/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2026-01-26","license_spdx":null,"license_treatment":"permissive","name":"edge-mdt-cl","python_support":"supports_current","summary":"Edge MDT Custom Layers package"},"popularity":{"monthly_downloads":108417,"position":12559,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.1.1"}