models
Auto-generated from tensorflow/models by Mutable.ai Auto WikiRevise
models | |
---|---|
GitHub Repository | |
Developer | tensorflow |
Written in | Python |
Stars | 77k |
Watchers | 2.7k |
Created | 02/05/2016 |
Last updated | 04/03/2024 |
License | Other |
Repository | tensorflow/models |
Auto Wiki | |
Revision | |
Software Version | 0.0.8Basic |
Generated from | Commit 6ebb11 |
Generated at | 04/04/2024 |
The TensorFlow Models repository is a comprehensive collection of well-maintained, tested, and optimized machine learning models implemented using TensorFlow's high-level APIs. This repository serves as a central hub for various state-of-the-art models across different domains, including natural language processing (NLP), computer vision, and recommendation systems.
The most important parts of the repository are the official
and research
directories. The official
directory contains a diverse set of computer vision models, such as image classification, object detection, instance segmentation, and semantic segmentation, as well as NLP models, including text classification, pretraining, and question answering. The research
directory provides a wide range of specialized machine learning models and research projects, covering tasks like adversarial text models, attention-based optical character recognition, audio processing, data augmentation, hierarchical reinforcement learning, and more.
The repository also includes the orbit
directory, which provides a flexible and lightweight framework for writing custom training loops in TensorFlow 2. This framework simplifies the process of building and training machine learning models by offering a modular and extensible design, with abstract base classes for trainers and evaluators, as well as a Controller
class for managing the outer training and evaluation loop.
The key algorithms and technologies used in this repository include:
- Transformer-based models for NLP tasks, leveraging attention mechanisms and pre-training techniques
- Convolutional neural networks (CNNs) and region-based models (e.g., Faster R-CNN, Mask R-CNN) for computer vision tasks
- Recommendation models, such as Deep Learning Recommendation Model (DLRM) and Deep Cross Network (DCN) v2, for click-through rate prediction and other recommendation tasks
- Hierarchical reinforcement learning algorithms, including the HIRO (Hierarchical Inverse Reinforcement Learning) method
- Adversarial training techniques for text classification models
- Attention-based models for optical character recognition
The key design choices of the repository include:
- Modular and extensible architecture, allowing for easy integration and customization of the various components
- Extensive use of Keras-based primitives for building and training models, promoting flexibility and interoperability
- Centralized configuration management, with separate directories for model configurations, data pipelines, and task-specific implementations
- Comprehensive documentation and unit tests to ensure the correctness and robustness of the implementations
Official ModelsRevise
References: official
The official
directory is a comprehensive collection of well-maintained, tested, and optimized machine learning models implemented using TensorFlow's high-level APIs. This directory serves as a central repository for various state-of-the-art models across different domains, including natural language processing (NLP), computer vision, and recommendation systems.
Vision ModelsRevise
References: official/vision
The …/modeling
directory contains the core functionality for building various computer vision models, including image classification, object detection, instance segmentation, and video classification.
Natural Language Processing (NLP) ModelsRevise
References: official/nlp
The …/modeling
directory provides a comprehensive library of Keras primitives for building transformer-based natural language processing (NLP) models, including pretraining, fine-tuning, and serving functionality.
Recommendation ModelsRevise
References: official/recommendation
The …/recommendation
directory contains the implementation of various recommendation models and associated data preprocessing and training pipelines. The key components in this directory include:
Edge TPU ModelsRevise
References: official/projects/edgetpu
The …/nlp
directory contains the implementation of the MobileBERT-EdgeTPU model, which is a version of the MobileBERT language model optimized for deployment on the Edge TPU hardware. The key components in this directory include:
Video ModelsRevise
The …/movinet
directory contains the implementation of the MoViNet (Mobile Video Transformer) video classification model, which is part of the TensorFlow official models repository. The directory includes the following key components:
3D Object Detection ModelsRevise
References: official/projects/pointpillars
The …/pointpillars
directory contains the implementation of the PointPillars object detection model, which is a 3D object detection model for autonomous driving applications.
Self-Supervised Learning ModelsRevise
References: official/projects/simclr
, official/projects/const_cl
The …/simclr
directory contains the core functionality and implementation of the Simple Contrastive Learning of Visual Representations (SimCLR) model. The key components in this directory include:
Panoptic Segmentation ModelsRevise
References: official/projects/panoptic
The …/modeling
directory contains the core implementation of the Panoptic Deeplab and Panoptic Mask R-CNN models for panoptic segmentation.
YOLO Object Detection ModelsRevise
References: official/projects/yolo
The …/modeling
directory contains the core implementation of the YOLO (You Only Look Once) and YOLOv7 object detection models. The key components in this directory include:
DeepMAC Mask R-CNNRevise
References: official/projects/deepmac_maskrcnn
The …/modeling
directory contains the core implementation of the DeepMAC-MaskRCNN model, a deep learning model for object detection and instance segmentation.
MOSAIC Semantic SegmentationRevise
References: official/projects/mosaic
The …/mosaic
directory contains the implementation of the MOSAIC semantic segmentation model, a deep learning model designed for efficient image segmentation, particularly on mobile devices.
Research ModelsRevise
References: research
The research
directory contains a diverse collection of machine learning models and research projects, each with its own specialized functionality and implementation details.
Object DetectionRevise
References: research/object_detection
The …/object_detection
directory contains the core functionality of the TensorFlow Object Detection API, which is an open-source framework for building, training, and deploying object detection models.
Semantic SegmentationRevise
References: research/deeplab
The …/deeplab
directory contains the core functionality for the DeepLab semantic segmentation model, including custom layers, network architectures, preprocessing utilities, and various other supporting components.
Image RetrievalRevise
References: research/delf
The …/python
directory contains the core functionality of the DELF (DEep Local Features) project, including the implementation of the DELG (Discriminative Embedding Learning with Geometry) image retrieval system.
Sequence-to-Sequence ModelsRevise
References: research/seq_flow_lite
The …/seq_flow_lite
directory contains the implementation of various neural network models and custom TensorFlow operations that are part of the Sequence Flow Lite (SeqFlow Lite) framework. The key components in this directory include:
Video Object DetectionRevise
References: research/lstm_object_detection
The …/lstm_object_detection
directory contains the core functionality for an LSTM-based object detection model. The main components include:
Adversarial Text ModelsRevise
References: research/adversarial_text
The …/adversarial_text
directory contains code for training text classification models using adversarial training methods. The main functionality includes:
Hierarchical Reinforcement LearningRevise
References: research/efficient-hrl
The …/
directory contains the implementation of a Hierarchical Reinforcement Learning (HRL) framework, including the HIRO (Hierarchical Inverse Reinforcement Learning) algorithm and its variants.
Cross-View Text ClassificationRevise
References: research/cvt_text
The …/
directory contains the core functionality for the Cross-View Training (CVT) text model. The key components in this directory are:
Attention-based OCRRevise
References: research/attention_ocr
The …/python
directory contains the core implementation of the Attention OCR (Optical Character Recognition) model. The key components are:
Audio ProcessingRevise
References: research/audioset
The …/audioset
directory contains implementations of two pre-trained deep learning models for audio processing: VGGish
and YAMNet
. These models are designed to work with the AudioSet dataset, a large-scale dataset of over 2 million human-labeled 10-second YouTube video soundtracks with labels from an ontology of more than 600 audio event classes.
Latent Factor AnalysisRevise
References: research/lfads
The …/lfads
directory contains the implementation of the LFADS (Latent Factor Analysis via Dynamical Systems) model, a sequential variational autoencoder designed for analyzing time-series data, particularly in neuroscience applications.
Automatic Speech RecognitionRevise
References: research/deep_speech
The …/
directory contains the implementation of the DeepSpeech2 model, a deep learning-based automatic speech recognition (ASR) system. The key components in this directory are:
Data AugmentationRevise
References: research/autoaugment
The …/autoaugment
directory contains a comprehensive implementation of various deep learning models, data augmentation techniques, and training utilities for image classification tasks on the CIFAR-10 and CIFAR-100 datasets, including the AutoAugment data augmentation technique.
Orbit Training FrameworkRevise
References: orbit
The Orbit Training Framework provides a flexible and lightweight library for writing custom training loops in TensorFlow 2. The key components of this framework include:
ActionsRevise
References: orbit/actions
The …/actions
directory provides a set of abstractions and utilities for defining and applying "actions" within the Orbit framework. These actions can be used to perform various tasks during the training or evaluation of a machine learning model, such as reporting metrics, exporting models, or saving checkpoints.
ControllerRevise
References: orbit/controller.py
The Controller
class in the …/controller.py
file is responsible for managing the outer training and evaluation loop for machine learning models. It coordinates the execution of actions, such as saving checkpoints, running evaluations, and writing summaries, in addition to handling checkpoint restoration and saving.
AbstractionsRevise
References: orbit/runner.py
The models/orbit/runner.py
file provides two abstract base classes, AbstractTrainer
and AbstractEvaluator
, that define the APIs for training and evaluating machine learning models.
Standard ImplementationsRevise
References: orbit/standard_runner.py
The standard_runner.py
file in the orbit
directory provides two main classes, StandardTrainer
and StandardEvaluator
, which extend the AbstractTrainer
and AbstractEvaluator
APIs, respectively. These classes add additional structure and functionality on top of the bare APIs, making it easier to implement common training and evaluation workflows.
UtilitiesRevise
References: orbit/utils
The …/
directory provides a rich set of utility functions and classes that are essential for building and training machine learning models using the Orbit framework. The key functionalities include:
Community ModelsRevise
References: community
The community
directory contains a curated collection of pre-trained machine learning models and implementations powered by TensorFlow 2. The models are organized into three main categories: Computer Vision, Natural Language Processing, and Recommendation Systems.
TensorFlow ModelsRevise
References: tensorflow_models
The TensorFlow Models repository serves as a central hub for various machine learning models and utilities within the TensorFlow ecosystem. It is divided into two main sub-directories: …/nlp
and …/vision
, each focusing on natural language processing (NLP) and computer vision tasks, respectively.
Natural Language Processing (NLP) ModelsRevise
References: tensorflow_models/nlp
The …/nlp
directory provides a comprehensive collection of natural language processing (NLP) related components, models, and functionalities within the TensorFlow Models repository.
Computer Vision ModelsRevise
References: tensorflow_models/vision
The …/vision
directory contains the core components and functionality for computer vision tasks within the TensorFlow Models repository.