models/community

TensorFlow Community Models

This repository provides a curated list of the GitHub repositories with machine learning models and implementations powered by TensorFlow 2.

Note: Contributing companies or individuals are responsible for maintaining their repositories.

Computer Vision

Image Recognition

Model	Paper	Features	Maintainer
DenseNet 169	Densely Connected Convolutional Networks	• FP32 Inference	Intel
Inception V3	Rethinking the Inception Architecture for Computer Vision	• Int8 Inference • FP32 Inference	Intel
Inception V4	Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning	• Int8 Inference • FP32 Inference	Intel
MobileNet V1	MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications	• Int8 Inference • FP32 Inference	Intel
ResNet 101	Deep Residual Learning for Image Recognition	• Int8 Inference • FP32 Inference	Intel
ResNet 50	Deep Residual Learning for Image Recognition	• Int8 Inference • FP32 Inference	Intel
ResNet 50v1.5	Deep Residual Learning for Image Recognition	• Int8 Inference • FP32 Inference • FP32 Training	Intel
EfficientNet v1 v2	EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks	• Automatic mixed precision • Horovod Multi-GPU training (NCCL) • Multi-node training on a Pyxis/Enroot Slurm cluster • XLA	NVIDIA

Object Detection

Model	Paper	Features	Maintainer
R-FCN	R-FCN: Object Detection via Region-based Fully Convolutional Networks	• Int8 Inference • FP32 Inference	Intel
SSD-MobileNet	MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications	• Int8 Inference • FP32 Inference	Intel
SSD-ResNet34	SSD: Single Shot MultiBox Detector	• Int8 Inference • FP32 Inference • FP32 Training	Intel

Segmentation

Model	Paper	Features	Maintainer
Mask R-CNN	Mask R-CNN	• Automatic Mixed Precision • Multi-GPU training support with Horovod • TensorRT	NVIDIA
U-Net Medical Image Segmentation	U-Net: Convolutional Networks for Biomedical Image Segmentation	• Automatic Mixed Precision • Multi-GPU training support with Horovod • TensorRT	NVIDIA

Natural Language Processing

Model	Paper	Features	Maintainer
BERT	BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding	• FP32 Inference • FP32 Training	Intel
BERT	BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding	• Horovod Multi-GPU • Multi-node with Horovod and Pyxis/Enroot Slurm cluster • XLA • Automatic mixed precision • LAMB	NVIDIA
ELECTRA	ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators	• Automatic Mixed Precision • Multi-GPU training support with Horovod • Multi-node training on a Pyxis/Enroot Slurm cluster	NVIDIA
GNMT	Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation	• FP32 Inference	Intel
Transformer-LT (Official)	Attention Is All You Need	• FP32 Inference	Intel
Transformer-LT (MLPerf)	Attention Is All You Need	• FP32 Training	Intel

Recommendation Systems

Model	Paper	Features	Maintainer
Wide & Deep	Wide & Deep Learning for Recommender Systems	• FP32 Inference • FP32 Training	Intel
Wide & Deep	Wide & Deep Learning for Recommender Systems	• Automatic mixed precision • Multi-GPU training support with Horovod • XLA	NVIDIA
DLRM	Deep Learning Recommendation Model for Personalization and Recommendation Systems	• Automatic Mixed Precision • Hybrid-parallel multiGPU training using Horovod all2all • Multinode training for Pyxis/Enroot Slurm clusters • XLA • Criteo dataset preprocessing with Spark on GPU	NVIDIA

Contributions

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