Explore and extend models from the latest cutting edge research.
Resnet Style Video classification networks pretrained on the Kinetics 400 dataset
SlowFast networks pretrained on the Kinetics 400 dataset
X3D networks pretrained on the Kinetics 400 dataset
MiDaS models for computing relative depth from a single image.
classify birds using this fine-grained image classifier
Reference implementation for music source separation
A set of compact enterprise-grade pre-trained STT Models for multiple languages.
A set of compact enterprise-grade pre-trained TTS Models for multiple languages
Pre-trained Spoken Language Classifier
Pre-trained Spoken Number Detector
Pre-trained Voice Activity Detector
YOLOv5 in PyTorch > ONNX > CoreML > TFLite
DeepLabV3 model with a ResNet-101 backbone
Transformer models for English-French and English-German translation.
ResNext models trained with billion scale weakly-supervised data.
A simple generative image model for 64x64 images
High-quality image generation of fashion, celebrity faces
ResNet and ResNext models introduced in the "Billion scale semi-supervised learning for image classification" paper
PyTorch implementations of popular NLP Transformers
U-Net with batch normalization for biomedical image segmentation with pretrained weights for abnormality segmentation in brain MRI
Single Shot MultiBox Detector model for object detection
The Tacotron 2 model for generating mel spectrograms from text
WaveGlow model for generating speech from mel spectrograms (generated by Tacotron2)
A Robustly Optimized BERT Pretraining Approach
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.
Dense Convolutional Network (DenseNet), connects each layer to every other layer in a feed-forward fashion.
Fully-Convolutional Network model with a ResNet-101 backbone
Efficient networks by generating more features from cheap operations
GoogLeNet was based on a deep convolutional neural network architecture codenamed "Inception" which won ImageNet 2014.
Harmonic DenseNet pre-trained on ImageNet
Networks with domain/appearance invariance
Also called GoogleNetv3, a famous ConvNet trained on Imagenet from 2015
Boosting Tiny and Efficient Models using Knowledge Distillation.
Efficient networks optimized for speed and memory, with residual blocks
Proxylessly specialize CNN architectures for different hardware platforms.
A new ResNet variant.
Deep residual networks pre-trained on ImageNet
Next generation ResNets, more efficient and accurate
An efficient ConvNet optimized for speed and memory, pre-trained on Imagenet
Alexnet-level accuracy with 50x fewer parameters.
Award winning ConvNets from 2014 Imagenet ILSVRC challenge
Wide Residual Networks
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