A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities

Segmentation models with pretrained backbones. Keras and TensorFlow Keras.

Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet, NasNet, Residual Attention Network, SENet, WideResNet)

Semantic Segmentation Suite in TensorFlow. Implement, train, and test new Semantic Segmentation models easily!

A pytorch-based deep learning framework for multi-modal 2D/3D medical image segmentation

Classification with PyTorch.

A memory-efficient implementation of DenseNets

Fully convolutional deep neural network to remove transparent overlays from images

Classification models trained on ImageNet. Keras.

High level network definitions with pre-trained weights in TensorFlow

A Deep Learning library for EEG Tasks (Signals) Classification, based on TensorFlow.

PyTorch to Keras model convertor

A PyTorch implementation of DenseNet.

Play deep learning with CIFAR datasets

Simple Tensorflow implementation of "Squeeze and Excitation Networks" using Cifar10 (ResNeXt, Inception-v4, Inception-resnet-v2)

DenseNet implementation in Keras

PyTorch implementation of CNNs for CIFAR benchmark

DenseNet Implementation in Tensorflow

A tensorflow2 implementation of some basic CNNs(MobileNetV1/V2/V3, EfficientNet, ResNeXt, InceptionV4, InceptionResNetV1/V2, SENet, SqueezeNet, DenseNet, ShuffleNetV2, ResNet).

Simple Tensorflow implementation of Densenet using Cifar10, MNIST