Mengxianke's starred repositories
Extreme-Computing
coursework for Extreme Computing
AdvancedDatabases
The solution to Advanced Databases assignments.
RLPractical
Algorithm implements for lecture Reinforcement Learning in University of Edinburgh.
Pytorch-Stochastic-Depth-Resnet
Pytorch Implementation of Deep Networks with Stochastic Depth
ResNeXt.pytorch
Reproduces ResNet-V3 with pytorch
Traffic-Signs-CNN-Classifier
This project is to recognize the traffic signs in the wild (real-world) which is one of the main tasks for any self-driving car project. It is a computer vision classification problem that I’ve tackled by building a CNN model trained from scratch to do the job.
Traffic-Sign-CNN
Deep learning network for traffic sign image classification
car-traffic-sign-classification
Built and trained a deep neural network to classify traffic signs using Tensorflow
CarND-Traffic-Sign-Classifier-P2
Traffic Sign Classification using Convolutional Neural Networks
pytorch-cifar100
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)
pytorch-playground
Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)
License-Plate-Detector
基于Yolov5车牌检测,更快更准.
cats-and-dogs-classification
The Oxford-IIIT-Pet dataset - Image classification using CNN
License-Plate-Detect-Recognition-via-Deep-Neural-Networks-accuracy-up-to-99.9
works in real-time with detection and recognition accuracy up to 99.8% for Chinese license plates: 100 ms/plate