Beast code in Giters

jangjiun's starred repositories

Deep-Learning-with-TensorFlow-book

深度学习入门开源书，基于TensorFlow 2.0案例实战。Open source Deep Learning book, based on TensorFlow 2.0 framework.

Language:Jupyter Notebook13175 486 220

imgclsmob

Sandbox for training deep learning networks

Language:PythonMIT2942 76 96

dimensionality_reduction_alo_codes

特征提取/数据降维:PCA、LDA、MDS、LLE、TSNE等降维算法的python实现

Language:PythonApache-2.02258 43 5

YOLOv3_TensorFlow

Complete YOLO v3 TensorFlow implementation. Support training on your own dataset.

Language:PythonMIT1549 60 252

Basic_CNNs_TensorFlow2

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

Language:PythonMIT525 19 38

Densenet-Tensorflow

Simple Tensorflow implementation of Densenet using Cifar10, MNIST

Language:PythonMIT504 26 24

TensorFlow2.0_ResNet

A ResNet(ResNet18, ResNet34, ResNet50, ResNet101, ResNet152) implementation using TensorFlow-2.0.

Language:PythonMIT313 5 9

tensorflow-mnist-cnn

MNIST classification using Convolutional NeuralNetwork. Various techniques such as data augmentation, dropout, batchnormalization, etc are implemented.

Language:Python196 8 8

tensorflow-mnist-predict

Use TensorFlow to predict handwriting

Language:Python183 21 8

tensorflow_multigpu_imagenet

Tensorflow code for training different architectures(DenseNet, ResNet, AlexNet, GoogLeNet, VGG, NiN) on ImageNet dataset + Multi-GPU support + Transfer Learning support

Language:PythonMIT169 10 25

ResNeXt-Tensorflow

Simple Tensorflow implementation of ResNeXt using Cifar10

Language:PythonMIT158 9 9

PRT

Pattern Recognition Toolbox for MATLAB

Language:MATLABMIT144 39 68

deeplearning-tf2

Deep learning model zoo with TensorFlow 2.X (& Keras)

Language:Python134 13 2

CNN

Training a convolutional neural network using own picture dataset

Language:Python98 3 4

ImageNet_ResNet_Tensorflow2.0

Train ResNet on ImageNet in Tensorflow 2.0; ResNet 在ImageNet上完整训练代码

Language:Python82 2 5

inat_comp_2018

CNN training code for iNaturalist 2018 image classification competition

Language:Python82 8 5

Feature reduction projections and classifier models are learned by training dataset and applied to classify testing dataset. A few approaches of feature reduction have been compared in this paper: principle component analysis (PCA), linear discriminant analysis (LDA) and their kernel methods (KPCA,KLDA). Correspondingly, a few approaches of classification algorithm are implemented: Support Vector Machine (SVM), Gaussian Quadratic Maximum Likelihood and K-nearest neighbors (KNN) and Gaussian Mixture Model(GMM).

Language:MATLAB69 2 5