The TensorFlow Model Garden provides implementations of many state-of-the-art machine learning (ML) models for vision and natural language processing (NLP), as well as workflow tools to let you quickly configure and run those models on standard datasets. Whether you are looking to benchmark performance for a well-known model, verify the results of recently released research, or extend existing models, the Model Garden can help you drive your ML research and applications forward.
The Model Garden includes the following resources for machine learning developers:
- Official models for vision and NLP, maintained by Google engineers
- Research models published as part of ML research papers
- Training experiment framework for fast, declarative training configuration of official models
- Specialized ML operations for vision and natural language processing (NLP)
- Model training loop management with Orbit
These resources are built to be used with the TensorFlow Core framework and integrate with your existing TensorFlow development projects. Model Garden resources are also provided under an open source license, so you can freely extend and distribute the models and tools.
Practical ML models are computationally intensive to train and run, and may require accelerators such as Graphical Processing Units (GPUs) and Tensor Processing Units (TPUs). Most of the models in Model Garden were trained on large datasets using TPUs. However, you can also train and run these models on GPU and CPU processors.
Model Garden models
The machine learning models in the Model Garden include full code so you can test, train, or re-train them for research and experimentation. The Model Garden includes two primary categories of models: official models and research models.
Official models
The Official Models repository is a collection of state-of-the-art models, with a focus on vision and natural language processing (NLP). These models are implemented using current TensorFlow 2.x high-level APIs. Model libraries in this repository are optimized for fast performance and actively maintained by Google engineers. The official models include additional metadata you can use to quickly configure experiments using the Model Garden training experiment framework.
Research models
The Research Models repository is a collection of models published as code resources for research papers. These models are implemented using both TensorFlow 1.x and 2.x. Model libraries in the research folder are supported by the code owners and the research community.
Training experiment framework
The Model Garden training experiment framework lets you quickly assemble and run training experiments using its official models and standard datasets. The training framework uses additional metadata included with the Model Garden's official models to allow you to configure models quickly using a declarative programming model. You can define a training experiment using Python commands in the TensorFlow Model library or configure training using a YAML configuration file, like this example.
The training framework uses
tfm.core.base_trainer.ExperimentConfig
as the configuration object, which contains the following top-level
configuration objects:
runtime: Defines the processing hardware, distribution strategy, and other performance optimizations