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

利用pytorch实现图像分类的一个完整的代码，训练，预测，TTA，模型融合，模型部署，cnn提取特征，svm或者随机森林等进行分类，模型蒸馏，一个完整的代码

主流ocr算法研究实验性的项目，目前实现了CNN+BLSTM+CTC架构

Fully convolutional deep neural network to remove transparent overlays from images

Classification models trained on ImageNet. Keras.

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

High level network definitions with pre-trained weights in TensorFlow

PyTorch to Keras model convertor

A PyTorch implementation of DenseNet.

Play deep learning with CIFAR datasets

Books, Presentations, Workshops, Notebook Labs, and Model Zoo for Software Engineers and Data Scientists wanting to learn the TF.Keras Machine Learning framework

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