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For Researchers

Explore and extend models from the latest cutting edge research.

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  • GitHub Stars
    :: 오름차순
  • GitHub Stars
    :: 내림차순

  • HybridNets

HybridNets - End2End Perception Network

  • 3D ResNet

Resnet Style Video classification networks pretrained on the Kinetics 400 dataset

  • SlowFast

SlowFast networks pretrained on the Kinetics 400 dataset

  • X3D

X3D networks pretrained on the Kinetics 400 dataset

  • YOLOP

YOLOP pretrained on the BDD100K dataset

  • MiDaS

MiDaS models for computing relative depth from a single image.

  • ntsnet

classify birds using this fine-grained image classifier

  • Open-Unmix

Reference implementation for music source separation

  • Silero Speech-To-Text ...

A set of compact enterprise-grade pre-trained STT Models for multiple languages.

  • Silero Text-To-Speech ...

A set of compact enterprise-grade pre-trained TTS Models for multiple languages

  • Silero Language Classi...

Pre-trained Spoken Language Classifier

  • Silero Number Detector

Pre-trained Spoken Number Detector

  • Silero Voice Activity ...

Pre-trained Voice Activity Detector

  • YOLOv5

YOLOv5 in PyTorch > ONNX > CoreML > TFLite

  • Deeplabv3

DeepLabV3 models with ResNet-50, ResNet-101 and MobileNet-V3 backbones

  • Transformer (NMT)

영어-프랑스어 번역과 영어-독일어 번역을 위한 트랜스포머 모델

  • ResNext WSL

ResNext models trained with billion scale weakly-supervised data.

  • DCGAN on FashionGen

64x64 이미지 생성을 위한 기본 이미지 생성 모델

  • Progressive Growing of...

High-quality image generation of fashion, celebrity faces

  • Semi-supervised and se...

Billion scale semi-supervised learning for image classification 에서 제안된 ResNet, ResNext 모델

  • PyTorch-Transformers

PyTorch implementations of popular NLP Transformers

  • U-Net for brain MRI

U-Net with batch normalization for biomedical image segmentation with pretrained weights for abnormality segmentation in brain MRI

  • EfficientNet

EfficientNets are a family of image classification models, which achieve state-of-the-art accuracy, being an order-of-magnitude smaller and faster....

  • ResNet50

ResNet50 model trained with mixed precision using Tensor Cores.

  • ResNeXt101

ResNet with bottleneck 3x3 Convolutions substituted by 3x3 Grouped Convolutions, trained with mixed precision using Tensor Cores.

  • SE-ResNeXt101

ResNeXt with Squeeze-and-Excitation module added, trained with mixed precision using Tensor Cores.

  • SSD

Single Shot MultiBox Detector model for object detection

  • Tacotron 2

The Tacotron 2 model for generating mel spectrograms from text

  • WaveGlow

WaveGlow model for generating speech from mel spectrograms (generated by Tacotron2)

  • RoBERTa

BERT를 강력하게 최적화하는 사전 학습 접근법, RoBERTa

  • AlexNet

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.

  • Densenet

Dense Convolutional Network (DenseNet), connects each layer to every other layer in a feed-forward fashion.

  • FCN

Fully-Convolutional Network model with ResNet-50 and ResNet-101 backbones

  • GhostNet

Efficient networks by generating more features from cheap operations

  • GoogLeNet

GoogLeNet was based on a deep convolutional neural network architecture codenamed "Inception" which won ImageNet 2014.

  • HarDNet

Harmonic DenseNet pre-trained on ImageNet

  • Inception_v3

Also called GoogleNetv3, a famous ConvNet trained on Imagenet from 2015

  • MEAL_V2

Boosting Tiny and Efficient Models using Knowledge Distillation.

  • MobileNet v2

잔차 블록에 기반한 속도와 메모리에 최적화된 효율적인 네트워크

  • ProxylessNAS

Proxylessly specialize CNN architectures for different hardware platforms.

  • ResNeSt

A new ResNet variant.

  • ResNet

Deep residual networks pre-trained on ImageNet

  • ResNext

Next generation ResNets, more efficient and accurate

  • ShuffleNet v2

An efficient ConvNet optimized for speed and memory, pre-trained on Imagenet

  • SqueezeNet

Alexnet-level accuracy with 50x fewer parameters.

  • vgg-nets

Award winning ConvNets from 2014 Imagenet ILSVRC challenge

  • Wide ResNet

Wide Residual Networks

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