{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/15"},{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/9"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/7"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/18"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/3"}],"enrichment":{"capability":"TensorFlow-Slim provides high-level layers, variable management, and training utilities that simplify defining, training, and evaluating neural network models in TensorFlow.","skillfed_tags":["tensorflow","neural-networks","computer-vision"],"use_cases":["Define convolutional neural networks compactly using slim layers instead of native operations","Reuse pre-trained VGG or AlexNet models as starting points or feature extractors for transfer learning","Manage model variables separately from training variables for easier checkpoint saving and loading","Apply regularization and common loss functions without writing custom code","Train models with built-in learning routines and evaluate using standard metrics"],"what_it_does":"TensorFlow-Slim is a lightweight library that sits on top of TensorFlow to reduce boilerplate when building neural networks. It provides argument scoping to set default parameters across operations, high-level layer definitions, variable management utilities that distinguish between model and non-model variables, and pre-built implementations of popular computer vision architectures like VGG and AlexNet. It also includes training and evaluation routines, loss functions, metrics, regularizers, and data-loading utilities.\n\nYou use it by importing as `tf_slim` and calling its layers and utilities instead of writing raw TensorFlow operations. It is designed to mix freely with native TensorFlow code, so you can adopt it incrementally. The library targets developers building convolutional neural networks and other deep learning models who want to reduce repetitive code and improve readability.","worth_installing":"Yes, if you are working with TensorFlow 1.x or early TensorFlow 2.x and need to reduce model-definition boilerplate. No, if you are starting a new project\u2014modern TensorFlow and Keras provide equivalent or superior APIs natively. The package is stable and permissively licensed, but has not been updated since 2020 and may not be compatible with the latest TensorFlow versions."},"id":"tf-slim","links":{"html":"https://skillfed.io/packages/tf-slim","md":"https://skillfed.io/packages/tf-slim.md","pypi":"https://pypi.org/project/tf-slim/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2020-05-07","license_spdx":null,"license_treatment":"permissive","name":"tf-slim","python_support":"unspecified","summary":"TensorFlow-Slim: A lightweight library for defining, training and evaluating complex models in TensorFlow"},"popularity":{"monthly_downloads":132995,"position":11532,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.1.0"}