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)

Classification models trained on ImageNet. Keras.

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

でぃーぷらーにんぐを無限にやってディープラーニングでDeepLearningするための実装CheatSheet

猫狗大战

Support PointRend, Fast_SCNN, HRNet, Deeplabv3_plus(xception, resnet, mobilenet), ContextNet, FPENet, DABNet, EdaNet, ENet, Espnetv2, RefineNet, UNet, DANet, HRNet, DFANet, HardNet, LedNet, OCNet, EncNet, DuNet, CGNet, CCNet, BiSeNet, PSPNet, ICNet, FCN, deeplab)

AI Papers

A treasure chest for image classification powered by PaddlePaddle

AI场景分类竞赛

This is an ongoing re-implementation of DeepLab_v3_plus on pytorch which is trained on VOC2012 and use ResNet101 for backbone.

Xception implemented with caffe

Train/Eval the popular network by TF-Slim,include mobilenet/shufflenet/squeezenet/resnet/inception/vgg/alexnet

Easy-to-use scripts for training and inferencing with Xception on your own dataset

COVID-19 Detection Chest X-rays and CT scans: COVID-19 Detection based on Chest X-rays and CT Scans using four Transfer Learning algorithms: VGG16, ResNet50, InceptionV3, Xception. The models were trained for 500 epochs on around 1000 Chest X-rays and around 750 CT Scan images on Google Colab GPU. A Flask App was later developed wherein user can upload Chest X-rays or CT Scans and get the output of possibility of COVID infection.

Deep learning based tool for image processing. No need for Programing and GPU.

training a classification model with xray14 dataset

Xception V1 model in Tensorflow with pretrained weights on ImageNet

Pytorch implementation of DeepLab V3+

Generating image captions using Xception Network and Beam Search in Keras

99.7% accuracy solution for Dogs vs Cats Redux Kaggle competition

pytorch-1.1 torchvision-0.3

Simple Eye Blink Detection with CNN

Chainer implementation of the paper "Xception: Deep Learning with Depthwise Separable Convolutions" (https://arxiv.org/abs/1610.02357).

This repository presents the codes, data and the trained networks of this paper: https://doi.org/10.1016/j.imu.2020.100360

Given an image of a dog, our algorithm will identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed.

Implementation of state-of-the-art models to do segmentation over our own dataset.

Defect classificaiton using NEU dataset

Lightweight Facial Expression(emotion) Recognition model