SimpleNet
Lets Keep it simple, Using simple architectures to outperform deeper and more complex architectures
Lets Keep it simple, Using simple architectures to outperform deeper and more complex architectures
Wide Residual Networks
Award winning ConvNets from 2014 ImageNet ILSVRC challenge
Alexnet-level accuracy with 50x fewer parameters.
Brain-inspired Multilayer Perceptron with Spiking Neurons
An efficient ConvNet optimized for speed and memory, pre-trained on ImageNet
Next generation ResNets, more efficient and accurate
Deep residual networks pre-trained on ImageNet
Efficient networks optimized for speed and memory, with residual blocks
Also called GoogleNetv3, a famous ConvNet trained on ImageNet from 2015
Efficient networks by generating more features from cheap operations
Fully-Convolutional Network model with ResNet-50 and ResNet-101 backbones
Dense Convolutional Network (DenseNet), connects each layer to every other layer in a feed-forward fashion.
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.
DeepLabV3 models with ResNet-50, ResNet-101 and MobileNet-V3 backbones
Ultralytics YOLOv5 🚀 for object detection, instance segmentation and image classification.
Pre-trained Voice Activity Detector
A set of compact enterprise-grade pre-trained TTS Models for multiple languages
A set of compact enterprise-grade pre-trained STT Models for multiple languages.
Reference implementation for music source separation