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|[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<br/>• FP32 Inference<br/>• FP32 Training |[Intel](https://github.com/IntelAI)|
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|[EfficientNet](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/Classification/ConvNets/efficientnet)|[EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/pdf/1905.11946.pdf)| • Automatic mixed precision<br/>• Horovod Multi-GPU training (NCCL)<br/>• Multi-node training on a Pyxis/Enroot Slurm cluster<br/>• XLA |[NVIDIA](https://github.com/NVIDIA)|
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| EfficientNet[v1](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/Classification/ConvNets/efficientnet_v1)[v2](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/Classification/ConvNets/efficientnet_v2)|[EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/pdf/1905.11946.pdf)| • Automatic mixed precision<br/>• Horovod Multi-GPU training (NCCL)<br/>• Multi-node training on a Pyxis/Enroot Slurm cluster<br/>• XLA |[NVIDIA](https://github.com/NVIDIA)|
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