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

Implementation of popular deep learning networks with TensorRT network definition API

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)

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

High level network definitions with pre-trained weights in TensorFlow

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

Light-weight Single Person Pose Estimator

SqueezeNet implementation with Keras Framework

AoE (AI on Edge，终端智能，边缘计算) 是一个终端侧AI集成运行时环境 (IRE)，帮助开发者提升效率。

This implements training of popular model architectures, such as AlexNet, ResNet and VGG on the ImageNet dataset(Now we supported alexnet, vgg, resnet, squeezenet, densenet)

Pytorch Imagenet Models Example + Transfer Learning (and fine-tuning)

The squeezenet image classification android example

Implementation of Squeezenet in pytorch, pretrained models on Cifar 10 data to come

Mainly use SSD, YOLO and other models to solve the target detection problem in image and video !

SqueezeNet Keras demo

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

Caffe Squeezenet model for binary classification of pornographic/non-pornographic material

[EXPERIMENTAL] Demo of using PyTorch 1.0 inside an Android app. Test with your own deep neural network such as ResNet18/SqueezeNet/MobileNet v2 and a phone camera.

Dopamine: Differentially Private Federated Learning on Medical Data (AAAI - PPAI)

This project aim to a build system which helps in the detection of cataract and it's type with the use of Machine Learning and OpenCv algorithms with the accuracy of 96 percent.