# 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](https://github.com/IntelAI/models/tree/master/benchmarks/image_recognition/tensorflow/densenet169) | [Densely Connected Convolutional Networks](https://arxiv.org/pdf/1608.06993) | • FP32 Inference | [Intel](https://github.com/IntelAI) |
| [Inception V3](https://github.com/IntelAI/models/tree/master/benchmarks/image_recognition/tensorflow/inceptionv3) | [Rethinking the Inception Architecture
for Computer Vision](https://arxiv.org/pdf/1512.00567.pdf) | • Int8 Inference
• FP32 Inference | [Intel](https://github.com/IntelAI) |
| [Inception V4](https://github.com/IntelAI/models/tree/master/benchmarks/image_recognition/tensorflow/inceptionv4) | [Inception-v4, Inception-ResNet and the Impact
of Residual Connections on Learning](https://arxiv.org/pdf/1602.07261) | • Int8 Inference
• FP32 Inference | [Intel](https://github.com/IntelAI) |
| [MobileNet V1](https://github.com/IntelAI/models/tree/master/benchmarks/image_recognition/tensorflow/mobilenet_v1) | [MobileNets: Efficient Convolutional Neural Networks
for Mobile Vision Applications](https://arxiv.org/pdf/1704.04861) | • Int8 Inference
• FP32 Inference | [Intel](https://github.com/IntelAI) |
| [ResNet 101](https://github.com/IntelAI/models/tree/master/benchmarks/image_recognition/tensorflow/resnet101) | [Deep Residual Learning for Image Recognition](https://arxiv.org/pdf/1512.03385) | • Int8 Inference
• FP32 Inference | [Intel](https://github.com/IntelAI) |
| [ResNet 50](https://github.com/IntelAI/models/tree/master/benchmarks/image_recognition/tensorflow/resnet50) | [Deep Residual Learning for Image Recognition](https://arxiv.org/pdf/1512.03385) | • Int8 Inference
• FP32 Inference | [Intel](https://github.com/IntelAI) |
| [ResNet 50v1.5](https://github.com/IntelAI/models/tree/master/benchmarks/image_recognition/tensorflow/resnet50v1_5) | [Deep Residual Learning for Image Recognition](https://arxiv.org/pdf/1512.03385) | • Int8 Inference
• FP32 Inference
• FP32 Training | [Intel](https://github.com/IntelAI) |
### Object Detection
| Model | Paper | Features | Maintainer |
|-------|-------|----------|------------|
| [R-FCN](https://github.com/IntelAI/models/tree/master/benchmarks/object_detection/tensorflow/rfcn) | [R-FCN: Object Detection
via Region-based Fully Convolutional Networks](https://arxiv.org/pdf/1605.06409) | • Int8 Inference
• FP32 Inference | [Intel](https://github.com/IntelAI) |
| [SSD-MobileNet](https://github.com/IntelAI/models/tree/master/benchmarks/object_detection/tensorflow/ssd-mobilenet) | [MobileNets: Efficient Convolutional Neural Networks
for Mobile Vision Applications](https://arxiv.org/pdf/1704.04861) | • Int8 Inference
• FP32 Inference | [Intel](https://github.com/IntelAI) |
| [SSD-ResNet34](https://github.com/IntelAI/models/tree/master/benchmarks/object_detection/tensorflow/ssd-resnet34) | [SSD: Single Shot MultiBox Detector](https://arxiv.org/pdf/1512.02325) | • Int8 Inference
• FP32 Inference
• FP32 Training | [Intel](https://github.com/IntelAI) |
### Segmentation
| Model | Paper | Features | Maintainer |
|-------|-------|----------|------------|
| [Mask R-CNN](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/Segmentation/MaskRCNN) | [Mask R-CNN](https://arxiv.org/abs/1703.06870) | • Automatic Mixed Precision
• Multi-GPU training support with Horovod
• TensorRT | [NVIDIA](https://github.com/NVIDIA) |
| [U-Net Medical Image Segmentation](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/Segmentation/UNet_Medical) | [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/abs/1505.04597) | • Automatic Mixed Precision
• Multi-GPU training support with Horovod
• TensorRT | [NVIDIA](https://github.com/NVIDIA) |
## Natural Language Processing
| Model | Paper | Features | Maintainer |
|-------|-------|----------|------------|
| [BERT](https://github.com/IntelAI/models/tree/master/benchmarks/language_modeling/tensorflow/bert_large) | [BERT: Pre-training of Deep Bidirectional Transformers
for Language Understanding](https://arxiv.org/pdf/1810.04805) | • FP32 Inference
• FP32 Training | [Intel](https://github.com/IntelAI) |
| [GNMT](https://github.com/IntelAI/models/tree/master/benchmarks/language_translation/tensorflow/mlperf_gnmt) | [Google’s Neural Machine Translation System:
Bridging the Gap between Human and Machine Translation](https://arxiv.org/pdf/1609.08144) | • FP32 Inference | [Intel](https://github.com/IntelAI) |
| [Transformer-LT](https://github.com/IntelAI/models/tree/master/benchmarks/language_translation/tensorflow/transformer_mlperf) | [Attention Is All You Need](https://arxiv.org/pdf/1706.03762) | • FP32 Training | [Intel](https://github.com/IntelAI) |
| [ELECTRA](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/LanguageModeling/ELECTRA) | [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://openreview.net/forum?id=r1xMH1BtvB) | • Automatic Mixed Precision
• Multi-GPU training support with Horovod
• Multi-node training on a Pyxis/Enroot Slurm cluster | [NVIDIA](https://github.com/NVIDIA) |
## Recommendation Systems
| Model | Paper | Features | Maintainer |
|-------|-------|----------|------------|
| [Wide & Deep](https://github.com/IntelAI/models/tree/master/benchmarks/recommendation/tensorflow/wide_deep_large_ds) | [Wide & Deep Learning for Recommender Systems](https://arxiv.org/pdf/1606.07792) | • Int8 Inference
• FP32 Inference
• FP32 Training | [Intel](https://github.com/IntelAI) |
| [Wide & Deep](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/Recommendation/WideAndDeep) | [Wide & Deep Learning for Recommender Systems](https://arxiv.org/pdf/1606.07792) | • Automatic mixed precision
• Multi-GPU training support with Horovod
• XLA | [NVIDIA](https://github.com/NVIDIA) |
## Contributions
If you want to contribute, please review the [contribution guidelines](https://github.com/tensorflow/models/wiki/How-to-contribute).