Update README.md - new format
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Przemek Strzelczyk
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README.md
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README.md
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@ -21,15 +21,13 @@ The examples are organized first by framework, such as TensorFlow, PyTorch, etc.
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- __SSD__ [[PyTorch](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Detection/SSD)] [[TensorFlow](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/Detection/SSD)]
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- __Mask R-CNN__ [[PyTorch](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Segmentation/MaskRCNN)]
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- __U-Net(industrial)__ [[TensorFlow](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/Segmentation/UNet_Industrial)]
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- __U-Net(medical)__ [[TensorFlow](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/Segmentation/UNet_Medical)]
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- __U-Net(medical)__ [[TensorFlow](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/Segmentation/UNet_Medical)] [[TensorFlow2](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/Segmentation/UNet_Medical)]
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- __VNet__ [[TensorFlow](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/Segmentation/VNet)]
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### Natural Language Processing
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- __GNMT__ [[PyTorch](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Translation/GNMT)] [[TensorFlow](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/Translation/GNMT)]
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- __Transformer__ [[PyTorch](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Translation/Transformer)]
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- __BERT__ [[PyTorch](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/LanguageModeling/BERT)][[TensorFlow](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/LanguageModeling/BERT)]
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- __BERT__ [[PyTorch](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/LanguageModeling/BERT)] [[TensorFlow](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/LanguageModeling/BERT)]
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- __Transformer-XL__ [[PyTorch](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/LanguageModeling/Transformer-XL)]
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@ -44,6 +42,52 @@ The examples are organized first by framework, such as TensorFlow, PyTorch, etc.
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### Speech Recognition
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- __Jasper__ [[PyTorch](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/SpeechRecognition/Jasper)]
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### CUDA Accelerated Applications
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- __Kaldi__ [[TRTIS](https://github.com/NVIDIA/DeepLearningExamples/tree/master/Kaldi/SpeechRecognition)]
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## Jupyter Notebooks
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| Models <img width=500/>| TensorFlow <img width=200/>| PyTorch <img width=500/>| TensorRT <img width=400/>| TRTIS <img width=400/>|
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| ------------- | ------------- | ------------- | ------------- | ------------- |
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| SSD | [ Inference](https://github.com/NVIDIA/DeepLearningExamples/blob/master/TensorFlow/Detection/SSD/models/research/object_detection/object_detection_tutorial.ipynb) | [ Inference](https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/Detection/SSD/examples/inference.ipynb) | - | - |
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| MaskRCNN | - | [ Training & Inference](https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/Segmentation/MaskRCNN/pytorch/notebooks/pytorch_MaskRCNN_pyt_train_and_inference.ipynb) | - | - |
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| Jasper | - | - | [ PyTorch Inference TensorRT Colab](https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechRecognition/Jasper/notebooks/Colab_Jasper_TRT_inference_demo.ipynb), [ PyTorch Inference TensorRT](https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechRecognition/Jasper/notebooks/JasperTRT.ipynb) | [ PyTorch Inference TRTIS](https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechRecognition/Jasper/notebooks/JasperTRTIS.ipynb) |
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| Tacotron2 & WaveGlow | - | [ Training & Inference](https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechSynthesis/Tacotron2/notebooks/Tacotron2.ipynb) | - | [ PyTorch Inference TRTIS](https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechSynthesis/Tacotron2/notebooks/trtis/notebook.ipynb) |
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| BERT | [ Inference Movie Review Sentiment](https://github.com/NVIDIA/DeepLearningExamples/blob/master/TensorFlow/LanguageModeling/BERT/predicting_movie_reviews_with_bert_on_tf_hub.ipynb), [ Fine-Tuning SQuaD](https://github.com/NVIDIA/DeepLearningExamples/blob/80f9481ef8a2c61958f240618077bf89cfce78f6/TensorFlow/LanguageModeling/BERT/notebooks/bert_squad_tf_finetuning.ipynb), [ Inference Colab](https://github.com/NVIDIA/DeepLearningExamples/blob/master/TensorFlow/LanguageModeling/BERT/notebooks/bert_squad_tf_inference_colab.ipynb), [ Inference](https://github.com/NVIDIA/DeepLearningExamples/blob/80f9481ef8a2c61958f240618077bf89cfce78f6/TensorFlow/LanguageModeling/BERT/notebooks/bert_squad_tf_inference.ipynb) | - | - | - |
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| BioBERT | [ Inference](https://github.com/NVIDIA/DeepLearningExamples/blob/master/TensorFlow/LanguageModeling/BERT/notebooks/biobert_ner_tf_inference.ipynb) | - | - | - |
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| UNet Industrial | [ Export and Inference Colab](https://github.com/NVIDIA/DeepLearningExamples/blob/80f9481ef8a2c61958f240618077bf89cfce78f6/TensorFlow/Segmentation/UNet_Industrial/notebooks/Colab_UNet_Industrial_TF_TFHub_export.ipynb), [ Inference](https://github.com/NVIDIA/DeepLearningExamples/blob/80f9481ef8a2c61958f240618077bf89cfce78f6/TensorFlow/Segmentation/UNet_Industrial/notebooks/Colab_UNet_Industrial_TF_TFHub_inference_demo.ipynb) | - | - | - |
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| Automatic Mixed Precision | [ AMP Training](https://github.com/NVIDIA/DeepLearningExamples/blob/80f9481ef8a2c61958f240618077bf89cfce78f6/TensorFlow/docs/amp/notebook_v1.14/auto_mixed_precision_demo_cifar10.ipynb) | - | - | - |
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## Feature Matrix
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| Models | Framework | DALI | AMP | Multi-GPU | Multi-Node | TensorRT | ONNX | TRTIS | TF-TRT |
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| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |------------- |------------- |------------- |------------- |
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| [ResNet50 v1.5](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Classification/ConvNets/resnet50v1.5) |PyTorch | Yes | Yes | Yes | - | - | - | - | - |
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| [ResNeXt101-32x4d](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Classification/ConvNets/resnext101-32x4d) |PyTorch | Yes | Yes | Yes | - | - | - | - | - |
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| [SE-ResNeXt101-32x4d](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Classification/ConvNets/se-resnext101-32x4d) |PyTorch | Yes | Yes | Yes | - | - | - | - | - |
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| [SSD300 v1.1](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Detection/SSD) |PyTorch | Yes | Yes | Yes | - | - | - | - | - |
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| [BERT](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/LanguageModeling/BERT) |PyTorch | N/A | Yes | Yes | Yes | - | - | - | - |
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| [Transformer-XL](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/LanguageModeling/Transformer-XL) |PyTorch | N/A | Yes | Yes | Yes | - | - | - | - |
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| [Neural Collaborative Filtering](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Recommendation/NCF) |PyTorch | N/A | Yes | Yes | - | - |- | - | - |
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| [Mask R-CNN](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Segmentation/MaskRCNN) |PyTorch | N/A | Yes | Yes | - | - | - | - | - |
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| [Jasper](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/SpeechRecognition/Jasper) |PyTorch | N/A | Yes | Yes | - | Yes | Yes | Yes | - |
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| [Tacotron 2 And WaveGlow v1.10](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/SpeechSynthesis/Tacotron2) | PyTorch | N/A | Yes | Yes | - | Yes | Yes | Yes | - |
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| [GNMT v2](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Translation/GNMT) |PyTorch | N/A | Yes | Yes | - | - | - | - | - |
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| [Transformer](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Translation/Transformer) |PyTorch | N/A | Yes | Yes | - | - | - | - | - |
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| [ResNet-50 v1.5](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/Classification/RN50v1.5) |TensorFlow | Yes | Yes | Yes | - | - | - | - | - |
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| [SSD320 v1.2](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/Detection/SSD) | TensorFlow | N/A | Yes | Yes | - | - | - | - | - |
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| [BERT](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/LanguageModeling/BERT) |TensorFlow | N/A | Yes | Yes | Yes | Yes | - | Yes | Yes |
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| [BioBert](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/LanguageModeling/BERT/biobert) | TensorFlow | N/A | Yes | Yes | - | - | - | - | - |
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| [Neural Collaborative Filtering](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/Recommendation/NCF) |TensorFlow | N/A | Yes | Yes | - | - | - | - | - |
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| [Variational Autoencoder Collaborative Filtering](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/Recommendation/VAE-CF) |TensorFlow | N/A | Yes | Yes | - | - | - | - | - |
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| [U-Net Industrial](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/Segmentation/UNet_Industrial) |TensorFlow | N/A | Yes | Yes | - | Yes | - | - | Yes |
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| [U-Net Medical](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/Segmentation/UNet_Medical) | TensorFlow | N/A | Yes | Yes | - | Yes |- | - | Yes |
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| [U-Net Medical](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/Segmentation/UNet_Medical) | TensorFlow2 | N/A | Yes | Yes | - | Yes |- | - | Yes |
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| [V-Net Medical](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/Segmentation/VNet) | TensorFlow | N/A | Yes | Yes | - | Yes | Yes | - | Yes |
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| [GNMT v2](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/Translation/GNMT) | TensorFlow | N/A | Yes | Yes | - | - | - | - | - |
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| [Faster Transformer](https://github.com/NVIDIA/DeepLearningExamples/tree/master/FasterTransformer) | Tensorflow | N/A | - | - | - | Yes | - | - | - |
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| [ResNet50 v1.5](https://github.com/NVIDIA/DeepLearningExamples/tree/master/MxNet/Classification/RN50v1.5) | MXNet | Yes | Yes | Yes | - | - | - | - | - |
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| [HMM](https://github.com/NVIDIA/DeepLearningExamples/tree/master/Kaldi/SpeechRecognition) | Kaldi | N/A | - | Yes | - | - | - | Yes | - |
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## NVIDIA support
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In each of the network READMEs, we indicate the level of support that will be provided. The range is from ongoing updates and improvements to a point-in-time release for thought leadership.
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@ -55,3 +99,4 @@ In each of the network READMEs, we indicate any known issues and encourage the c
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