187 Open source projects that are alternatives of or similar to image-defect-detection-based-on-CNN

Label Embedding Network

Theano implementation of XNOR-Net

Non-negative Positive-Unlabeled (nnPU) and unbiased Positive-Unlabeled (uPU) learning reproductive code on MNIST and CIFAR10

The origin of the Non-IID phenomenon is the personalization of users, who generate the Non-IID data. With Non-IID (Not Independent and Identically Distributed) issues existing in the federated learning setting, a myriad of approaches has been proposed to crack this hard nut. In contrast, the personalized federated learning may take the advantage…

Replace FC2, LeNet-5, VGG, Resnet, Densenet's full-connected layers with MPO

tensorflow implementation of Exploring Randomly Wired Neural Networks for Image Recognition

A look at some simple autoencoders for the Cifar10 dataset, including a denoising autoencoder. Python code included.

Generative Adversarial Networks in TensorFlow 2.0

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The implementation of Relativistic average GAN with Keras

Keras implementations of Generative Adversarial Networks. GANs, DCGAN, CGAN, CCGAN, WGAN and LSGAN models with MNIST and CIFAR-10 datasets.

One-Shot Learning with Triplet CNNs in Pytorch

Implementation of Conv-based and Vit-based networks designed for CIFAR.

Playing with MNIST. Machine Learning. Generative Models.

手写数字识别。利用Tensorflow和MNIST实现的一个简单的手写数字识别程序。

Multi-Classification on dataset of MNIST

The simplest machine learning library for launching UIs, running evaluations, and comparing model performance.

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A small convolution neural network deep learning framework implemented in c++.

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Minimalist implementation of VQ-VAE in Pytorch

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A Pytorch implementation of "Deep Convolutional Generative Adversarial Networks"

Tensorflow implementation of adversarial auto-encoder for MNIST

Çeşitli kütüphaneler kullanılarak Türkçe kod açıklamalarıyla TEMEL SEVİYEDE pratik derin öğrenme uygulamaları.

PyTorch implementation of shake-drop regularization

Pytorch reimplementation for "Gradient Surgery for Multi-Task Learning"

Implementation of a digit recognition using my Neural Network with the MNIST data set.

Implementation of LeNet5 without any auto-differentiate tools or deep learning frameworks. Accuracy of 98.6% is achieved on MNIST dataset.

In-Browser Digit recognition with Tensorflow.js and React using Mnist dataset

CNN digit recognizer implemented in Keras Notebook, Kaggle/MNIST (0.995).

CNN classification model built in Keras used for Digit Recognizer task on Kaggle (https://www.kaggle.com/c/digit-recognizer)

PC-DARTS (PC-DARTS: Partial Channel Connections for Memory-Efficient Differentiable Architecture Search, published in ICLR 2020) implemented in Tensorflow 2.0+. This is an unofficial implementation.

A curated list of dedicated resources and applications

A implementation of DCGAN (Deep Convolutional Generative Adversarial Networks) for CIFAR10 image

Variational Autoencoder and Conditional Variational Autoencoder on MNIST in PyTorch

PyTorch implementation of Conditional Deep Convolutional Generative Adversarial Networks (cDCGAN)

MNIST with TensorFlow Lite on Android

Tensorflow-example：使用MNIST训练模型，并识别手写数字图片

A scratch implementation of Convolutional Neural Network in Python using only numpy and validated over CIFAR-10 & MNIST Dataset

GBDT (Gradient Boosted Decision Tree: 勾配ブースティング) のpythonによる実装

Lingvo

anomaly detection by one-class SVM

Bottleneck Transformers for Visual Recognition

Generative Adversarial Network for MNIST with tensorflow

Collection of generative models in Pytorch version.

Keras implementation of temporal ensembling(semi-supervised learning)

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

텐서플로우를 기초부터 응용까지 단계별로 연습할 수 있는 소스 코드를 제공합니다

A realtime digit OCR on the browser using Machine Learning

Experiments on cluttered mnist dataset with Tensorflow.

Tensorflow 2.0 implementation of Adversarial Autoencoders

Tensorflow implementation of Generative Adversarial Networks (GAN) and Deep Convolutional Generative Adversarial Netwokrs for MNIST dataset.

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🎎 InfoGAN: Interpretable Representation Learning

Tensorflow implementation of conditional variational auto-encoder for MNIST

A numpy based CNN implementation for classifying images

This program shows how Bounding-Box-Regression works in a visual form. Intersection over Union ( IOU ), Non Maximum Suppression ( NMS ), Object detection, 边框回归，边框回归可视化，交并比，非极大值抑制，目标检测。