t50 model trained with mixed precision using Tensor Cores.
![](/assets/images/nvidia_logo.png)
](/hub/nvidia_deeplearningexamples_resnet50/)

[

  * ResNeXt101 
  *   * ![](/assets/images/logo-github.svg)
  *   * 
ResNet with bottleneck 3x3 Convolutions substituted by 3x3 Grouped
Convolutions, trained with mixed precision using Tensor Cores.
![](/assets/images/nvidia_logo.png)
](/hub/nvidia_deeplearningexamples_resnext/)

[

  * SE-ResNeXt101 
  *   * ![](/assets/images/logo-github.svg)
  *   * 
ResNeXt with Squeeze-and-Excitation module added, trained with mixed precision
using Tensor Cores.  ![](/assets/images/nvidia_logo.png)
](/hub/nvidia_deeplearningexamples_se-resnext/)

[

  * SSD 
  *   * ![](/assets/images/logo-github.svg)
  *   * 
Single Shot MultiBox Detector model for object detection
![](/assets/images/nvidia_logo.png) ](/hub/nvidia_deeplearningexamples_ssd/)

[

  * Tacotron 2 
  *   * ![](/assets/images/logo-github.svg)
  *   * 
The Tacotron 2 model for generating mel spectrograms from text
![](/assets/images/nvidia_logo.png)
](/hub/nvidia_deeplearningexamples_tacotron2/)

[

  * WaveGlow 
  *   * ![](/assets/images/logo-github.svg)
  *   * 
WaveGlow model for generating speech from mel spectrograms (generated by
Tacotron2)  ![](/assets/images/nvidia_logo.png)
](/hub/nvidia_deeplearningexamples_waveglow/)

[

  * RoBERTa 
  *   * ![](/assets/images/logo-github.svg)
  *   * 
A Robustly Optimized BERT Pretraining Approach
![](/assets/images/fairseq_logo.png) ](/hub/pytorch_fairseq_roberta/)

[

  * AlexNet 
  *   * ![](/assets/images/logo-github.svg)
  *   * 
The 2012 ImageNet winner achieved a top-5 error of 15.3%, more than 10.8
percentage points lower than that of the runner up.
![](/assets/images/alexnet2.png) ](/hub/pytorch_vision_alexnet/)

[

  * Densenet 
  *   * ![](/assets/images/logo-github.svg)
  *   * 
Dense Convolutional Network (DenseNet), connects each layer to every other
layer in a feed-forward fashion.  ![](/assets/images/densenet1.png)
](/hub/pytorch_vision_densenet/)

[

  * FCN 
  *   * ![](/assets/images/logo-github.svg)
  *   * 
Fully-Convolutional Network model with ResNet-50 and ResNet-101 backbones
![](/assets/images/fcn2.png) ](/hub/pytorch_vision_fcn_resnet101/)

[

  * GhostNet 
  *   * ![](/assets/images/logo-github.svg)
  *   * 
Efficient networks by generating more features from cheap operations
![](/assets/images/ghostnet.png) ](/hub/pytorch_vision_ghostnet/)

[

  * GoogLeNet 
  *   * ![](/assets/images/logo-github.svg)
  *   * 
GoogLeNet was based on a deep convolutional neural network architecture
codenamed "Inception" which won ImageNet 2014.
![](/assets/images/googlenet1.png) ](/hub/pytorch_vision_googlenet/)

[

  * HarDNet 
  *   * ![](/assets/images/logo-github.svg)
  *   * 
Harmonic DenseNet pre-trained on ImageNet  ![](/assets/images/hardnet.png)
](/hub/pytorch_vision_hardnet/)

[

  * IBN-Net 
  *   * ![](/assets/images/logo-github.svg)
  *   * 
Networks with domain/appearance invariance  ![](/assets/images/ibnnet.png)
](/hub/pytorch_vision_ibnnet/)

[

  * Inception_v3 
  *   * ![](/assets/images/logo-github.svg)
  *   * 
Also called GoogleNetv3, a famous ConvNet trained on ImageNet from 2015
![](/assets/images/inception_v3.png) ](/hub/pytorch_vision_inception_v3/)

[

  * MEAL_V2 
  *   * ![](/assets/images/logo-github.svg)
  *   * 
Boosting Tiny and Efficient Models using Knowledge Distillation.
![](/assets/images/MEALV2.png) ](/hub/pytorch_vision_meal_v2/)

[

  * MobileNet v2 
  *   * ![](/assets/images/logo-github.svg)
  *   * 
Efficient networks optimized for speed and memory, with residual blocks
![](/assets/images/mobilenet_v2_1.png) ](/hub/pytorch_vision_mobilenet_v2/)

[

  * ProxylessNAS 
  *   * ![](/assets/images/logo-github.svg)
  *   * 
Proxylessly specialize CNN architectures for different hardware platforms.
![](/assets/images/proxylessnas.png) ](/hub/pytorch_vision_proxylessnas/)

[

  * ResNeSt 
  *   * ![](/assets/images/logo-github.svg)
  *   * 
A