tensorflow/models
Models and examples built with TensorFlow
How models is put together
The TensorFlow Model Garden is a large collection of state-of-the-art machine learning model implementations written primarily in Python on top of TensorFlow 2 (with some legacy TensorFlow 1 code in research/). It is organized into four top-level areas: 'official' (maintained, optimized models with a shared training framework), 'research' (researcher-maintained model implementations), 'community' (curated links to external repos), and 'orbit' (a lightweight, forkable custom training-loop library integrating with tf.distribute). Data flows from datasets (local file patterns, TFDS, or MovieLens-style preprocessing) through input pipelines into Keras/Estimator-based training loops, producing checkpoints and exported SavedModels for inference. The repo is distributed as the tf-models-official / tf-models-nightly PyPI packages via official/pip_package/setup.py.
Languages
Frameworks
Infrastructure
Major components
official/core
Shared training framework: experiment configs (DataConfig/RuntimeConfig), registry/task factory, base trainer/task classes, train_lib orchestration, checkpoint managers, and TF Example builders.
official/modeling + official/common
Reusable modeling utilities (gradient utils, performance tuning, hyperparameter flags) and common infrastructure for distribution strategies and dataset functions.
official/nlp
Natural language processing models and training entry points, including optimization schedules and continuous finetuning libraries.
official/vision
Computer vision model implementations with train.py entry points, including spatial partitioning variants.
official/recommendation
NCF/NeuMF recommendation model: MovieLens data preprocessing, input pipelines, Keras training loop, and shell-based run scripts.
official/projects
Experimental/project-level models (e.g., movinet, unified_detector) each with their own requirements and tooling.
research/
Researcher-maintained model implementations in TF1/TF2 (object detection, deeplab, deep speech, adversarial text, autoaugment, cognitive planning, etc.), often self-contained with own configs and scripts.
orbit/
Standalone lightweight custom training loop library with runners, controllers, actions (checkpointing/export), and summary managers that integrates with tf.distribute.
A quiet week focused on internal housekeeping, including suppressing type-checker errors.
3 weeksWeek of 2026-08-17
Week of 2026-08-17latest
A quiet week focused on internal housekeeping, including suppressing type-checker errors.
Week of 2026-08-10
Light activity with four maintenance-only commits that had no public descriptions.
Week of 2026-07-20
The standout of this stretch was a security patch fixing a tracked vulnerability.
The last few weeks of activity on tensorflow/models were mostly routine maintenance, with most commits lacking public descriptions. The most notable visible change was a security fix addressing vulnerability V-001 in late July. The most recent week also included some code-quality cleanup around type-checking errors.
Week by week
2026-08-17A quiet week focused on internal housekeeping, including suppressing type-checker errors.latest2 changes
Chore
Type-checking cleanup
Errors reported by the pyrefly static analysis tool were suppressed to keep the codebase's automated checks clean.
Chore
Routine maintenance commits
Seven additional commits landed without public descriptions, likely covering small internal updates and upkeep.
2026-08-10Light activity with four maintenance-only commits that had no public descriptions.1 change
Chore
Internal updates
Four commits were merged during the week, all without publicly described changes, suggesting routine background work.
2026-07-20The standout of this stretch was a security patch fixing a tracked vulnerability.2 changes
Fix
Security vulnerability V-001 fixed
A security issue tracked as V-001 was patched in pull request #13650, closing a potential weakness in the project.
Chore
Routine maintenance commits
Five additional commits landed without public descriptions, indicating ongoing internal upkeep alongside the security fix.
Dependencies and code review
Dependency advisories
Security Watch
No known advisories across 0 scanned dependencies.
No known advisories in the scanned dependencies.
Code review
No concrete code-level issues confirmed.
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