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TensorFlow Research Models

This directory contains code implementations and pre-trained models of published research papers.

The research models are maintained by their respective authors.

Table of Contents

Modeling Libraries and Models

Directory Name Description Maintainer(s)
object_detection TensorFlow Object Detection API A framework that makes it easy to construct, train and deploy object detection models

A collection of object detection models pre-trained on the COCO dataset, the Kitti dataset, the Open Images dataset, the AVA v2.1 dataset, and the iNaturalist Species Detection Dataset		 jch1, tombstone, pkulzc
slim TensorFlow-Slim Image Classification Model Library A lightweight high-level API of TensorFlow for defining, training and evaluating image classification models
• Inception V1/V2/V3/V4
• Inception-ResNet-v2
• ResNet V1/V2
• VGG 16/19
• MobileNet V1/V2/V3
• NASNet-A_Mobile/Large
• PNASNet-5_Large/Mobile		 sguada, marksandler2

Models and Implementations

Computer Vision

Directory Paper(s) Conference Maintainer(s)
attention_ocr Attention-based Extraction of Structured Information from Street View Imagery ICDAR 2017 xavigibert
autoaugment [1] AutoAugment
[2] Wide Residual Networks
[3] Shake-Shake regularization
[4] ShakeDrop Regularization for Deep Residual Learning		 [1] CVPR 2019
[2] BMVC 2016
[3] ICLR 2017
[4] ICLR 2018		 barretzoph
deeplab [1] DeepLabv1: Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
[2] DeepLabv2: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
[3] DeepLabv3: Rethinking Atrous Convolution for Semantic Image Segmentation
[4] DeepLabv3+: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
 [1] ICLR 2015
[2] TPAMI 2017
[4] ECCV 2018		 aquariusjay, yknzhu
delf [1] DELF (DEep Local Features): Large-Scale Image Retrieval with Attentive Deep Local Features
[2] Detect-to-Retrieve: Efficient Regional Aggregation for Image Search
[3] DELG (DEep Local and Global features): Unifying Deep Local and Global Features for Image Search
[4] GLDv2: Google Landmarks Dataset v2 -- A Large-Scale Benchmark for Instance-Level Recognition and Retrieval		 [1] ICCV 2017
[2] CVPR 2019
[4] CVPR 2020		 andrefaraujo
lstm_object_detection Mobile Video Object Detection with Temporally-Aware Feature Maps CVPR 2018 yinxiaoli, yongzhe2160, lzyuan
marco MARCO: Classification of crystallization outcomes using deep convolutional neural networks vincentvanhoucke
vid2depth Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints CVPR 2018 rezama

Natural Language Processing

Directory Paper(s) Conference Maintainer(s)
adversarial_text [1] Adversarial Training Methods for Semi-Supervised Text Classification
[2] Semi-supervised Sequence Learning		 [1] ICLR 2017
[2] NIPS 2015		 rsepassi, a-dai
cvt_text Semi-Supervised Sequence Modeling with Cross-View Training EMNLP 2018 clarkkev, lmthang

Audio and Speech

Directory Paper(s) Conference Maintainer(s)
audioset [1] Audio Set: An ontology and human-labeled dataset for audio events
[2] CNN Architectures for Large-Scale Audio Classification		 ICASSP 2017 plakal, dpwe
deep_speech Deep Speech 2 ICLR 2016 yhliang2018

Reinforcement Learning

Directory Paper(s) Conference Maintainer(s)
efficient-hrl [1] Data-Efficient Hierarchical Reinforcement Learning
[2] Near-Optimal Representation Learning for Hierarchical Reinforcement Learning		 [1] NIPS 2018
[2] ICLR 2019		 ofirnachum
pcl_rl [1] Improving Policy Gradient by Exploring Under-appreciated Rewards
[2] Bridging the Gap Between Value and Policy Based Reinforcement Learning
[3] Trust-PCL: An Off-Policy Trust Region Method for Continuous Control		 [1] ICLR 2017
[2] NIPS 2017
[3] ICLR 2018		 ofirnachum

Others

Directory Paper(s) Conference Maintainer(s)
lfads LFADS - Latent Factor Analysis via Dynamical Systems jazcollins, sussillo
rebar REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models NIPS 2017 gjtucker

Old Models and Implementations in TensorFlow 1

⚠️ If you are looking for old models, please visit the Archive branch.

Contributions

If you want to contribute, please review the contribution guidelines.