tensorflow/models

Analysis updated 2026-06-20

★ 77,667PythonAudience · researcherComplexity · 4/5LicenseSetup · moderate

Mindmap

mindmap
  root((TF Model Garden))
    Directories
      official models
      research models
      community list
      orbit library
    Model Types
      Image classification
      NLP
      Object detection
      Embedding models
    Hardware
      CPU
      GPU
      TPU
    Install
      pip tf-models-official
      Clone and set Python path

mindmap root((TF Model Garden)) Directories official models research models community list orbit library Model Types Image classification NLP Object detection Embedding models Hardware CPU GPU TPU Install pip tf-models-official Clone and set Python path

Click or tap to explore — scroll the page freely

What do people build with it?

USE CASE 1

Fine-tune an official TensorFlow model on your own dataset without writing training code from scratch.

USE CASE 2

Use a research directory implementation as a verified starting point for reproducing a published paper's results.

USE CASE 3

Install the tf-models-official pip package to get an optimized, maintained model implementation with GPU and TPU support.

USE CASE 4

Use the orbit library to write a custom TensorFlow 2 training loop that integrates with tf.distribute for multi-device training.

What is it built with?

PythonTensorFlow

How does it compare?

	tensorflow/models	d2l-ai/d2l-zh	swisskyrepo/payloadsallthethings
Stars	77,667	77,689	77,510
Language	Python	Python	Python
Setup difficulty	moderate	moderate	easy
Complexity	4/5	2/5	1/5
Audience	researcher	researcher	developer

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · moderate Time to first run · 30min

Requires TensorFlow 2 and compatible GPU drivers, research models may have additional per-model dependencies.

Use freely for any purpose including commercial, as long as you include the Apache-2.0 license notice.

In plain English

The TensorFlow Model Garden is a collection of model implementations and examples built with TensorFlow. Its stated goal is to demonstrate best practices so TensorFlow users can take full advantage of the framework for research and product development. It does not provide a single model, but a curated set of reference implementations contributors can read, run, modify, and use as starting points. Inside the repository the code is split into a few directories. The official directory holds example implementations of state-of-the-art models written with TensorFlow 2's high-level APIs, these are officially maintained, kept up to date, and "reasonably optimized for fast performance while still being easy to read." The research directory contains model implementations contributed by researchers in TensorFlow 1 or 2, maintained by those researchers rather than the core team. The community directory is a curated list of GitHub repositories outside this one that hold models powered by TensorFlow 2. Finally, orbit is a flexible, lightweight library for writing customized training loops in TensorFlow 2.x, it integrates with tf.distribute and supports running on CPU, GPU, and TPU. Two installation paths are described. The first is a pip package, tf-models-official, that installs all models and their dependencies, a tf-models-nightly package tracks the master branch. The second is cloning the repository and adding it to the Python path. Someone would use the Model Garden when they want a known-good TensorFlow implementation of a published model to study, fine-tune, or build a product on top of, instead of writing one from scratch. The code is in Python and licensed under Apache-2.0.

Copy-paste prompts

Prompt 1

Help me set up the TensorFlow Model Garden's official image classification model and fine-tune it on a custom dataset using a GPU.

Prompt 2

Show me how to use the orbit library from tensorflow/models to write a custom training loop with tf.distribute for multi-GPU training.

Prompt 3

I want to run a model from the research directory of tensorflow/models, walk me through cloning the repo, adding it to my Python path, and launching a training script.

Prompt 4

Help me install the tf-models-nightly package and run one of the official NLP examples on a new text classification dataset.

Frequently asked questions

What is models?

The TensorFlow Model Garden is a curated collection of official and research model implementations in TensorFlow, ready-to-run reference code for training state-of-the-art models on CPU, GPU, or TPU.

What language is models written in?

Mainly Python. The stack also includes Python, TensorFlow.

What license does models use?

Use freely for any purpose including commercial, as long as you include the Apache-2.0 license notice.

How hard is models to set up?

Setup difficulty is rated moderate, with roughly 30min to a first successful run.

Who is models for?

Mainly researcher.

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